On 2017 Jan 28, Robert West commented:

This is a very useful article that will be of value to those working in other disciplines and interdicisplinary fields such as addiction. We are just at the beginning of a era in which we use ontologies and AI to build behavioural science. The Human Behaviour Change Project (www.humanbehaviourchange.org) is an ambitious attempt to take this forward, headed up by Prof Susan Michie with collaboration of IBM, computer and information scientists at UCL, and behavioural scientists in the Universities of Aberdeen and Cambridge.

On 2016 Nov 10, Lydia Maniatis commented:

The authors assert that their findings: “support the idea that there are density-selective channels in the visual system, and that perceived density is in part based on a comparison of these channel responses across space. “

Looking at the stimuli, I would suggest another interpretation. The areas with more closely grouped dots tend to be seen as figure, and the more dilute ones as ground. This is my impression. If this is the case, we should expect the dense areas to appear even denser, and the less dense areas to appear even less dense because: It has been understood since Rubin that figure appears more dense and ground less so: “According to Rubin (1915/1921), figures…adhere or cling together (are compact)…In comparison, the ground…has a “loose” structure....” (Wertheimer/Spillman Ed. 2012 On perceived motion and figural organization, MIT Press).

There is no obvious functional rationale for positing “density channels” that compare the densities (how is this evaluated?) of adjacent or overlapping surfaces.

On 2016 Dec 04, Alessandro Rasman commented:

Increased levels of coagulation factors in Multiple Sclerosis: a defending mechanism from microbleedings?

On 2016 Nov 12, Martine Crasnier-Mednansky commented:

The statement by Vincent Detours "Our interest, as scientists and citizens, is to reject the totalitarian reduction of human activities to numbers, and adopt policies acknowledging the diversity of human talents and promoting individuals’ autonomy." is echoing the statement by Lewis Mumford "The test of maturity, for nations as well as individuals, is not the increase in power, but the increase of self-understanding, self-control, self-direction, and self-transcendence. For in a mature society, man himself and not his machines or his organizations is the chief work of art."

On 2016 Nov 09, Donald Forsdyke commented:

Marketing in science

In it ironic that Vincent Detours insightful analysis of the "managers" who outdo the "competent" comes at a time when the triumph of marketing over ability is so evident on the political scene. For any who might think this could not happen in science, two accounts of the career of Niels Jerne will perhaps provide helpful reading (1, 2).

1.Soderqvist T (2003) Science as Autobiograph: the Troubled Life of Niels Jerne (Yale Univ. Press, New Haven).

2.Eichmann K (2008) The Network Collective: Rise and Fall of a Scientific Paradigm (Birkhauser, Berlin).

On 2016 Nov 07, Vincent Detours commented:

Who wants to be a number?

Sinatra et al. present another metrics to predict scientific impact. Like their predecessors, they fail to discuss the wider consequences of (i) equating impact and scientific quality, and (ii) reducing scientists' activity to a number.

The number of times a paper is cited—the basic quantity behind impact metrics—is by essence a measure of popularity, not a direct measure of truth and novelty. Intelligent thinking together with the availability of resources required to work, effective communication and access to high circulation venues all contribute to popularity. Building impact often means less work in the lab and more networking with those in charge of science funding and dissemination, presence on social media, etc. In this context, the form of communication increasingly takes precedence over its content, aggravating the current reproducibility crisis.

Impact metrics routinely guide hiring and funding. It spares decision makers the hurdle and risk of exerting sound scientific judgment: they simply promote the most popular folks. It conveniently shortens debates arising from diverse expert viewpoints. And who can argue against it? Aren’t the winners ‘elected’ by the community? As a result, scientifically unremarkable managers gain control at the expense of competent active scientists and, incidentally, the riches get richer. Yet, the vast majority of scientists are loosing control over resources and over their own destiny, being herded to the same 'high-impact' topics, for example.

The proliferation of impact metrics, social networks ‘likes’ and other audience measures fuel the increasing tyranny of rankings in society. While alienating and isolating individuals in narcissism and permanent competition, rankings ultimately benefit those who aggregate information and control communication. Our interest, as scientists and citizens, is to reject the totalitarian reduction of human activities to numbers, and adopt policies acknowledging the diversity of human talents and promoting individuals’ autonomy.

On 2017 May 25, Lydia Maniatis commented:

"A simple alternative model is developed that is consistent with the results."

I.e. an ad hoc, data-fitted proposal is submitted that, as such, is experimentally uncorroborated and carries no theoretical weight. This is because experimental conditions must always be informed by theoretical assumptions (which they are supposed to test), so that the experiment can select which variables to hold constant, and which to allow to vary so as to test their hypothesized role, Post hoc explanations cannot distinguish between causal variables and confounds, and thus the experiments which they are evaluating in hindsight cannot serve as tests of a hypothesis so-derived.

I suppose the authors will test their proposal in the future, but at the the present stage it was not worth reporting. Testing it would, of course, require much more extensive and detailed conceptual development, precisely so that anyone wanting to test it could exercise the necessary experimental control over the presumed causal variables, and minimize researcher degrees of freedom, i.e. the degree of uncertainty, in interpreting results.

On 2017 Feb 08, Seung Gyu Yun commented:

First, I agree with your opinion, such as definition of saliva. We seem to have misrepresented a saliva that was confused it with the oral fluid.

ASAP, I will request a correction to JCM.

Second, we don't exaclty know that the levels of virus in oral fluid would be sufficient to cause infection. Because we did not test virus culture test in oral fluid. However, we generally found that virus in oral fluid had low level. Perhaps your opinion is correct, but unfortunately I have not found accurate data about resiratory viral culture in oral fluid

Thank for your comment.

On 2017 Jan 26, Ronald Eccles commented:

Dear Authors

I was very pleased to read your recent article in Virology- a very interesting project.

I am often asked if saliva can be a source of common cold and influenza infections and I usually reply NO

The respiratory viruses such as rhinovirus and influenza replicate in the respiratory epithelium of nose, larynx and trachea and do not replicate in the oral mucosa.

However, your study clearly demonstrates that these respiratory viruses can be found in fluid samples taken from the oral cavity- I say fluid samples because saliva in the mouth may be contaminated with respiratory mucus on sneezing an coughing. The fluid you samples in the mouth was not pure saliva.

I agree that adenoviruses may replicate in the oral cavity but the respiratory viruses by definition do not.

I believe that the levels of virus you have detected in oral fluid with your very sensitive pcr technique demonstrate the presence of viruses in oral fluid rather than saliva- it is a fine point but in order to get pure saliva you would need to cannulate the parotid duct and I doubt if you would find any respiratory viruses in this pure saliva.

I found your research very interesting and congratulate you on a very important project.

One final point- do you believe the levels of virus you found in oral fluid would be sufficient to cause infection? I am doubtful as your pcr technique would detect very small amounts of RNA or DNA and the viral titre of infectious particles would be very low

I would be interested in your comments

Kind Regards

Professor Ron Eccles Director Common Cold Centre & Healthcare Clinical Trials Cardiff School of Biosciences Sir Martin Evans building Cardiff University Museum Avenue Cardiff CF10 3AX United Kingdom

Ronald Eccles Cyfarwyddwr Treialon Gofal lechyd a Chanolfan Anywyd Caerdydd Ffordd yr Amgueddfa Caerrdydd CF10 3AX

On 2017 Mar 19, Harri Hemila commented:

Shortcomings in the meta-analysis on vitamin C and atrial fibrillation

In their meta-analysis on vitamin C and postoperative atrial fibrillation (POAF), Baker WL, 2016 stated that they restricted to randomized trials; however, they included the Carnes CA, 2001 study which was not randomized. Furthermore, their meta-analysis did not include data for three US trials, two of which were substantially larger than the included POAF trials, see Hemilä H, 2017.

The conclusion by Baker WL, 2016 that vitamin C does have effects on POAF is reasonable, yet their analysis did not reveal the significant heterogeneity in the effects. Although 5 trials studies in the USA found no benefit, several studies in less wealthy countries have found significant benefit of vitamin C against POAF indicating that further research should be carried in less wealthy countries, see Hemilä H, 2017.

On 2016 Dec 07, Peter Hajek commented:

There was no significant association between respiratory symptoms and current vaping when controlling for smoking status, and this is the key analysis which should have been reported, rather than unadjusted results that reflect the fact that most vapers are smokers.

Past experimentation with vaping remained linked to some symptoms but it also significantly reduced wheezing. With the absence of any effects of current vaping, these links are likely to be flukes as there is no obvious mechanism for them. Here is a link to a more detailed critique of the way these findings were reported: http://www.ecigarette-research.org/research/index.php/whats-new/whatsnew-2016/248-bronch

On 2016 Nov 21, MICHAEL SIEGEL commented:

None

On 2017 Sep 19, Helmi BEN SAAD commented:

The correct name of the last author is: "Ben Saad H".

On 2016 Nov 24, Helmi BEN SAAD commented:

None

On 2017 Sep 30, Yuichi Hongoh commented:

Dear Dr.Price, your comment is very helpful to us. I agree with your suggestions. Thank you very much.

On 2017 Sep 25, Morgan Price commented:

The authors suggest that D. trichonymphae cannot synthesize threonine or methionine, but I think pathways for synthesizing both amino acids are present. First, the only gap in threonine synthesis appears to be a missing homoserine kinase. In Desulfivibrio vulgaris Miyazaki F, a misannotated shikimate kinase (DvMF_0971) provides the missing homoserine kinase activity (https://www.biorxiv.org/content/early/2017/09/23/192971). RSDT_0983 from D. trichonymphae is over 50% identical to DvMF_0971 and is also probably a homoserine kinase. Second, I found plausible candidates for all steps in methionine synthesis. D. trichonymphae has putative genes converting aspartate to homoserine (RSDT_0709, RSDT_1035, RSDT_0549), for activating homoserine (RSDT_0485), for sulfhydrylation of the activated homoserine (RSDT_0816), and for B12-dependent methionine synthase (RSDT_0316). Thus, D. trichonymphae contains the genes to synthesize threonine and methionine.

On 2017 Oct 06, Christopher Southan commented:

The structure mapping analysis, including SciFinder intersects, has now been updated, one year on https://cdsouthan.blogspot.se/2017/09/osm-antimalarial-series-1-findability.html

On 2016 Nov 02, Christopher Southan commented:

Blog post on PubChem mappings for the structures https://cdsouthan.blogspot.se/2016/09/series-1-antimalarials-publication.html

On 2017 Jun 23, Joshua L Cherry commented:

NO EVIDENCE THAT SELECTION DRIVES SWITCHING

This article claims that “the great majority of codon set switches proceed by two consecutive nucleotide substitutions…and are driven by selection”. The data in fact support a predominance of simultaneous switches that are not driven by selection. Even if we assume that sequential switches predominate, the implication that selection increases their rate by a factor of ~50 is unjustified. Moreover, selection against the non-serine intermediate is expected to decrease the rate of sequential switches, not to drive them. The authors’ argument to the contrary is analogous to arguing that a mountain range between two locations speeds the journey between them because it accelerates the downhill portion of the trip.

Inappropriate standard of comparsion

The usual way to establish that an evolutionary process is driven by selection is show that it is faster than some process that is largely unaffected by selection. The authors instead made comparisons to “expectations” derived from nonsynonymous substitution rates, which are greatly decreased by selection. This comparison cannot establish that switching is driven by selection, and would greatly overestimate any such effect.

A more appropriate comparison would be to expectations derived from synonymous rates. These are several-fold higher than nonsynonymous rates, and “expectations” involve products of two rates. Thus, the claimed acceleration by selection mostly or entirely disappears with a proper standard of comparison.

Unjustified rejection of simultaneous switching

The authors reject a significant role for simultaneous double mutation in switching because the rate of switching is higher than the rate of analogous double changes in noncoding sequences by a factor of 5-10. This argument would be valid if non-coding regions were evolving nearly neutrally, but this is far from the case: rates of non-coding transversions (Fig. 3) are comparable to the rates of some nonsynonymous transversions (Fig 2).

I have determined that the rates of the relevant synonymous transversions are higher than the corresponding single-base non-coding changes by a factor of >5. This presumably reflects purifying selection in non-coding regions. The effect of selection on simultaneous tandem changes is expected to be larger. Thus, the excess of serine switches over non-coding tandem changes can easily be explained by selection in non-coding regions. Put differently, we can estimate a lower limit on the rate of simultaneous serine switches, and it corresponds to the majority of the observed switches.

Slow- vs. fast-evolving genes

The article claims to have shown that the rate of switching is “higher in conserved genes than in nonconserved genes in full agreement with the selection hypothesis”. The results (Fig. 6) in fact demonstrate just the opposite: the rate of switching in “nonconserved” genes (0.0032 or 0.0022) is about three times higher than that in “conserved” genes (0.0010 or 0.0008).

The authors considered the ratio of the switching rate to a sum of products of nonsynonymous rates. This ratio is higher in “conserved” genes only because the nonsynonymous rates are lower in “conserved” genes. This is true almost by the definition of “conserved” (low dN/dS), and has nothing to do with serine codon switches.

Theoretical expectation

Under the simple selection scheme considered by the authors, selection will actually decrease the rate of sequential switching. After fixation of a deleterious Ser->Thr or Ser->Cys mutant, selection will indeed increase the fixation probability of a mutant that restores Ser. However, selection always decreases the fixation probability of the initial deleterious mutant by a larger factor. As illustrated here, the product of the two relative fixation probabilities, and hence the relative probability of a switch during a short interval, is always less than one (selection slows switching) for nonzero s, and it decreases monotonically and approaches zero as the strength of selection (|Nes|) increases.

The above implicitly assumes weak mutation, but the same conclusion holds outside of this regime (Kimura, 1985).

Conclusion

Neither the data nor the authors’ model supports the claim that serine codon switching is driven by selection or has an especially “high frequency”. In fact, both data and theory point to the opposite conclusion.

References

Kimura, M (1985) The role of compensatory neutral mutations in molecular evolution. Journal of Genetics 64(1):7-19.

On 2016 Nov 03, Donald Forsdyke commented:

CONSIDERATION OF NUCLEIC ACID LEVEL SELECTION?

The authors set out "to investigate the evolutionary factors that affect serine codon set switches" (i.e. between TCN and AGY). Their "findings imply unexpectedly high levels of selection" (1). Indeed, the data strongly support the conclusion that codon mutations "are driven by selection." It is conjectured that the codon mutation "switch would involve as an intermediate either threonine ACN or cysteine TGY, amino acid residues with properties substantially different from those of serine, so that such changes are unlikely to be tolerated at critical functional or structural sites of a protein."

However, it does not follow that the unsuitability of the interim amino acids drove the rapid tandem substitutions. Choice of "coincident codons" has long been seen as influenced by pressures acting at the nucleic acid level (2-4). These pressures evolve in parallel with, and sometimes dominate, protein pressures. One example is purine-loading pressure (3). If this cannot be satisfied by changes at third codon positions, then sometimes the organism must accept a less favorable amino acid. With serine codons, a change from TCN to AGY (i.e. first and second codon positions) can increase purine-loading pressure without compromising the amino acid that is encoded see Ref. 3.

1.Rogozin IB, Belinky F, Pavlenko V, Shabilina SA, Kristensen DM, Koonin EV (2016) Evolutionary switches between two serine codon sets are driven by selection. Proc Natl Acad Sci USA www.pnas.org/cgi/doi/10.1073/pnas.1615832113 Rogozin IB, 2016

2.Bains W. (1987) Codon distribution in vertebrate genes may be used to predict gene length. J Mol Biol 197:379-388. Bains W, 1987

3.Mortimer JR, Forsdyke DR (2003) Comparison of responses by bacteriophage and bacteria to pressures on the base composition of open reading frames. Appl Bioinf 2: 47-62. Mortimer JR, 2003

4.Forsdyke DR (2016) Evolutionary Bioinformatics, 3rd edition (Springer, New York).

On 2017 Apr 15, Darko Lavrencic commented:

There are three levels of research on intracraniospinal anatomy and fluids:

1) Research at the basic level: intracraniospinal anatomy, cells, barriers, biochemical fluids exchange, flow patterns, composition, etc.

2) Physiological intracraniospinal hydrodynamics

3) Pathological hydrodynamic changes and anatomic adaptations

Contemporary stage of research is predominantly still at the first level. The problems of the first level limit the solutions to the second and the third level.

Hypothesis "The Intracraniovertebral Volumes, the Cerebrospinal Fluid Flow and the Cerebrospinal Fluid Pressure, Their Homeostasis and Its Physical Regulation" is at the second level of research: http://www.med-lavrencic.si/research/the-intracraniovertebral-volumes/.

On 2016 Dec 20, Natalie Clairoux commented:

Free version of this article available at https://papyrus.bib.umontreal.ca/xmlui/handle/1866/16349

On 2016 Nov 18, David Mage commented:

Any candidate model for SIDS causation must explain the 50% male excess in SIDS and its 4-parameter lognormal age distribution. OMIM shows orexin, pPERK and ATF4 are all autosomal with no X-linkage involved. Therefore this line of research appears to be "barking up the wrong tree." Do the authors have any other explanation for the universal 0.61 male fraction of SIDS other than a recessive X-linkage or pure happenstance?

On 2017 Jun 27, Louise B Andrew MD JD commented:

Medscape has published a synopsis of this article at www.medscape.com/viewarticle/869777. The principal author of this study has more recently published a formal study of licensure application questions which is Open Access, and can be found at https://www.ncbi.nlm.nih.gov/pubmed/28633174 They should really be read together. I have written a comprehensive article on Physician Suicide for Medscape http://emedicine.medscape.com/article/806779. Medscape requires a free subscription. However, all materials and many more resources can be accessed freely through www.Physiciansuicide.com. If you care about mental health and physicians, please learn more and help to publicize and address it.<br> A doctor a day (in the US alone) is too many to lose to an eminently treatable disease.

On 2016 Nov 10, Egon Willighagen commented:

The SPLASH methods used in the paper is now described in this Nature Biotech paper: http://dx.doi.org/10.1038/nbt.3689

On 2017 Jan 11, Tania M O Abe commented:

After reviewing the entire paper, we noticed an error in data of the last column of Table 2. During the registration of information in Table 2, the last column mistakenly recorded incorrect monthly number of deaths for myocardial infarction. The correction will be done this week. Once this is an government data, it can be found in http://tabnet.datasus.gov.br/cgi/tabcgi.exe?sim/cnv/obt10SP.def

On 2016 Dec 27, Clive Bates commented:

The reporting of this study is highly misleading. The abstract confidently asserts:

RESULTS: We observed a reduction in mortality rate (-11.9% in the first 17 months after the law) and in hospital admission rate (-5.4% in the first 3 months after the law) for myocardial infarction after the implementation of the smoking ban law.

In fact, those with access to the full study will not find these observed reductions anywhere. Both myocardial infarction (heart attack) deaths and hospital admissions increased substantially after the smoking ban came into effect in August 2009. See graphs of the study data for myocardial infarction deaths and hospital admissions courtesy of Chris Snowden's blog post on these findings: Brazilian Smoking Ban Miracle.

The supposed 'decrease' emerges from modelling that adjusts for other factors that may influence heart attacks to create a counterfactual (what would have been expected to happen without the smoking ban). These factors give predicted rates of heart attack deaths and hospital admissions over the period studied. But why would the predicted rates suddenly shoot up to levels unprecedented in the dataset and so high that the observed large observed increases represent a decline compared to the even-higher prediction?

The explanation given in the paper is as follows:

The Autoregressive Integrated Moving Average with exogenous variables (ARIMAX) method was used to analyse the effect of the smoking ban law, modelled as a dummy variable, in the mortality rate and hospital admission rate data for myocardial infarction. The ARIMAX models were also adjusted to other parameters, including ‘total hospital admission’, CO, minimum temperature and air relative humidity. The ARIMAX method allows to estimate lag effects of input series and to forecast output series, as a function of a linear filter of the input series (transfer function) and of the noise (ARIMA filter) and by controlling for the autocorrelations. It enables us to compare the predicted rate of hospital admission and mortality with the real observed rate.

But how these corrections are made and whether they are valid is barely justified in the paper - they are buried in the black-box model used and reported uncritically. Given that these adjustments reverse the observed effects - turning a sharp rise into a decline - then surely the authors should have asked themselves harder questions and not just trusted the model and their choice of inputs to it. However, they don't even remark on this change of the sign of the effect in the paper as if the actual observations are an embarrassment to be ignored rather than discussed. Yet this is the most striking feature of the paper. Had the authors wished to explain their work transparently, they could have plotted the counterfactual (the predicted values for deaths and admissions with no ban) and the actual emissions and shown the decrease that way. But that would have begged the question: what is causing the very steep predicted rise? Or raised the possibility of modelling error or rogue assumptions.

Surely, confronted with this highly counterintuitive result, the editors and peer-reviewers should have demanded more explanation for the choice of confounding variables, a sensitivity analysis to flex whatever opaque assumptions have been made, publication of the data used to make adjustments, and a plausible narrative to explain the reversal of an increase to a decrease and the implicit massive underlying increase in background hospital admission and MI mortality rate that apparently coincided with the smoking ban. Finally, whatever the methodology, it is highly misleading to report these adjusted figures as an observed reduction in the abstract, especially with the faux precision of one decimal point.

I would like to suggest the following rewording of the results for inclusion a revised abstract:

RESULTS: We observed a substantial increase in mortality and hospital admissions for myocardial infarction after the implementation of the smoking ban law in Sao Paulo in August 2009. However, it is possible that other factors are responsible for this increase. After hand-picking a small number of possible confounding variables, and applying opaque statistical adjustments to account for their effect though without providing the data necessary for verification, we have been able to demonstrate that these increases could represent a modelled reduction in mortality attributable the smoking ban (−11.9% in the first 17 months after the law) and in hospital admission rate (−5.4% in the first 3 months after the law).

CONCLUSIONS: Hospital admissions and mortality rate for myocardial infarction were increased in the first months after the comprehensive smoking ban law was implemented. However, it is possible that factors other than the smoking ban accounted for some or all of this.

One must be concerned about the role of the journal Tobacco Control. Is this journal really an easy conduit for admitting studies of dubious quality to the peer-reviewed literature simply because the findings appear to provide support for certain tobacco control policies? I would welcome the editors' comments as well as that of the authors.

Please see original commentary from Dr Michael Siegel, Professor in the Department of Community Health Sciences, Boston University School of Public Health on his blog here and here.

On 2017 Aug 17, José L Oliver commented:

We are glad to announce that, after correcting the serious problem suffered by the backend on the past weeks, NGSmethDB is now running again: http://bioinfo2.ugr.es/NGSmethDB. Sorry by the inconveniences the downtime may have caused.

On 2017 Aug 11, José L Oliver commented:

The main backend of NGSmethDB, a no-SQL database coupled to an API-server, has suffered a serious, and by the moment unrecoverable, problem, being therefore currently unavailable. Sorry by the inconveniences this may cause. We follow applying our best efforts to recover it as soon as possible.

In the meantime, we want to remember that NGSmethDB implemented a second mode to access the data: the NGSmethDB track hubs at UCSC, which are fully operative these days: http://bioinfo2.ugr.es:8080/NGSmethDB/data-access/ Track hubs, together with the coupled Table Browser and Data Integrator tools, provide standard and efficient ways for visualizing, retrieving, combine and compare NGSmethDB data to any other third-part annotation.

On 2017 Aug 03, Julia Romanowska commented:

The link seems to lead to an unexisting page. Is it just a short maintenance issue?

On 2016 Nov 03, Darren L Dahly commented:

Because BMC has not actually linked the original reviews to this paper on their site, I have posted my original review here. Some but not all of my concerns were addressed in the final manuscript.

Major Compulsory Revisions

The paper is entirely exploratory and this should be emphasized by the authors. There are no specific hypotheses being tested here, nor are there any theory based predictions that the observed results can be compared against. While exploratory analyses can of course be helpful, the utility here is seriously limited by the observational nature of the data, the crudeness with which key covariates are measured (by survey) and represented (by dichotomization), and the use of modelling technique that will be unfamiliar to most readers. Consequently, almost any result obtained from this analysis could be explained, or explained away, fairly easily. Given the results obtained, the authors claim the research can inform out understanding of the aetiology and prevention of obesity, but notably fail to provide even one concrete example of how.

My concerns are amplified in light of how the modelling of BMI SD scores has been reported in the paper. First and foremost, the paper doesn’t report the exact form of the growth mixture model (i.e. exactly what is the set of parameters being estimated), or the values of any of the estimates obtained (nor any indication of the uncertainly in these estimates). It is thus not possible to fully evaluate the work that has been done, and this must be corrected before any final decision on the paper could be made.

Based on what I can infer about the model from the text, I have some additional concerns for the authors that I hope are useful. The authors only state that the variances of the latent growth factors are “fixed.” This could mean they are fixed to any specific value, or that they are estimated but fixed to be equal across classes. Based on the text, I will assume they were fixed as zero. This means that 100% of the variance in BMI SD scores is explained by group membership, and the final model reported includes two groups, each with similar intercepts but different slopes (one increasing and one decreasing). The model being reported (assuming I am correctly guessing the exact form of the model) precludes any variation in the degree of these changes. Thus, if the model was a faithful, complete representation of how these children are growing, then the exact same grouping could be discovered by simply dividing the sample into children with increasing BMI SDs and those with decreasing scores. I find this hard to believe. Ironically, to justify the use of growth mixture modelling, the authors state that it is useful for better understanding heterogeneity in growth – but they then go on to describe a model that describes all of that heterogeneity with a binary classification. I predict that the variability in the BMI SD scores at any single time point is more informative than the binary classification resulting from the “complex” model being reported.

The authors should report how differences in the exact ages of measurement were handled. There are several options. The authors might have assumed everyone was measured at the same age at times 1, 2, and 3, which could be a considerable source of error depending on the variances of the ages of measurement. It seems more likely they have used the Mplus time-scores option, and if so, this needs to be described along with other details of the model. The authors might have also smoothed the individual curves prior to modelling, in which case the details of the procedure used should be reported.

The use of BMI SDs scores could be problematic. One the one hand, it has the advantage of normalising the BMI measures and simplifying the functional form of the growth curve (though BMI change is fairly linear from the age of 4 years anyway). However, it introduces a new challenge to interpreting the result, as it’s hard to distinguish the degree to which the model is describing changes in BMI within children over time, and differences between the observed sample and the reference population.

The 2 stage modelling process employed is sub-optimal. It ignores the uncertainty inherent in the classification and thus subsequent standard errors of the estimated relationships between class memberships and other covariates are artificially reduced. Mplus is very capable of estimating the models being reported here in a single model that avoids this limitation (and has several other options for relating class membership to covariates that are also likely more appropriate).

The authors state that the “clinical interpretation” of the models was an important factor in determining the number of classes included in the final, reported models, but give no indication or example of what this term means in this specific context. This should be clarified.

There are some serious limitations, independent of the growth mixture modelling, that bear consideration by the authors. The first is that there is no consideration of the role of puberty, or recognition of the distinction between developmental time vs calendar time. Second, there is no consideration of the children’s heights, and even if BMI is defined as a measure of mass that is roughly independent of height, it’s hard to say anything useful about a child’s growth while ignorant of how tall he/she is.

The description of how missing data was handled is insufficient and I would point the authors to several guidelines that I hope they find helpful (doi 10.1186/1471-2288-12-96).

The sample is not described in sufficient detail. At the very least, the overall response rate should be provided. I would suggest that the authors refer to an established reporting guideline (e.g. STROBE) to help avoid this kind of reporting error.

The paper overemphasizes the novelty of this analysis based solely on the use of growth mixture models. There the hundreds of existing population based studies of BMI in children and no reason to think this particular analysis is more informative than many/most of these.

The term “confounding” is found nowhere in the paper. To have any utility regarding identified risk factors, the authors should have something to say about the exchangeability of groups being compared.

On 2017 Feb 10, Richard Holliday commented:

As highlighted by the previous comments there are several weaknesses with this study. I would like to add a few further observations:

-There is inadequate choice of e-liquids and controls. The cells were exposed to three conditions: air control, e-liquid [tobacco, 16mg] and e-liquid [menthol, 0mg nicotine]. There are two variables here (nicotine concentration and flavour) making any analysis of the results impossible.

-In the methods, a menthol, 13-16 mg nicotine e-liquid is mentioned but this is not mentioned anywhere else in the paper, nor presented in the results.

-The main conclusion of this paper is that ‘flavoured e-cigs’ gave a ‘greater response’. As there was no unflavoured control this conclusion is invalid. Likewise, if the authors are trying to say ‘menthol flavour’ gave a greater response than ‘tobacco flavour’ this is again invalid as nicotine concentration is a confounding factor in their study design.

The UK E-cigarette Research Forum (an initiative developed by Cancer Research UK in partnership with Public Health England and the UK Centre for Tobacco and Alcohol Studies) recently reviewed this paper. The full review can be found here (see review 5 with further comments in the final paragraph of the overview).

On 2016 Dec 27, Clive Bates commented:

There are several weaknesses in this paper and the surrounding commentary, and it caused unwarranted alarmist news coverage, which has not been corrected: New e-cigarettes alert as experts warn that the exposure to chemicals could trigger severe gum disease and even increase the risk of mouth cancer, Daily Mail, 17 December 2016.

Comparators. The study lacks adequate comparators - for example, coffee and/or cigarette smoke could have been used. That would have allowed these observations to be placed in a meaningful comparative context. Given that the vast majority of users or potential users are smokers, the comparison with smoking is most relevant. Further, if the effect of vaping is no different to that arising from an everyday habit like drinking coffee then we would be correspondingly reassured.

Interpretation of cell study. These effects on cells in vitro will not necessarily translate into material oral health risks in live subjects and the authors do not show evidence that the effects observed at the magnitudes measured are a realistic proxy for human gum disease risk. If human cells are exposed to any aversive environment, some effect is likely. But it is heroic to interpolate that to a human disease risk associated with normal use of the product. Ames (Ames BN, 2000) explains why:

Humans have many natural defenses that buffer against normal exposures to toxins and these are usually general, rather than tailored for each specific chemical. Thus they work against both natural and synthetic chemicals. Examples of general defenses include the continuous shedding of cells exposed to toxins. The surface layers of the mouth, esophagus, stomach, intestine, colon, skin and lungs are discarded every few days; DNA repair enzymes, which repair DNA that was damaged from many different sources; and detoxification enzymes of the liver and other organs which generally target classes of chemicals rather than individual chemicals.

Professor Brad Rodu elaborates further: Imaginary Hobgoblins From E-Cigarette Liquid Lab Tests, February 2016.

Methodology The lead author's commentary speaks of 'burning' e-liquid and 'smoking' e-cigarettes. This does not inspire much confidence that the authors understand this non-combustible technology or that they have operated the device in realistic conditions for humans. There have been other studies where the devices have been operated at higher temperature than would be possible for human users, and then measurements of unrealistic levels of thermal decomposition products reported - see for example, Jensen RP, 2015. The authors provide little reassurance that have not fallen into the same methodological pit. The discussion of the methodology followed is thin and insufficient to allow replication.

Overpromoting results. So in the absence of useful comparators and no link from these observations to disease risk it hard to see what this study adds. Further, it is unclear how it justifies the alarmist over-confident commentary First-ever Study Shows E-cigarettes Cause Damage to Gum Tissue that accompanied it and led to the news coverage cited above. This promotes the implicit claim (in the absence of an explicit caveat) that e-cigarette use would damage the gums in the mouth of a living person.

Failure to provide a rounded view when communicating with the public. Other studies suggest that switching from smoking to vaping has a beneficial effect on oral health. For example, see Tatullo M, 2016:

At the end of the study, we registered a progressive improvement in the periodontal indexes, as well as in the general health perception. Finally, many patients reported an interesting reduction in the need to smoke.In the light of this pilot study, the e-cigarette can be considered as a valuable alternative to tobacco cigarettes, but with a positive impact on periodontal and general health status.

And this study, Wadia R, 2016 which found an increase in gingival inflammation when tobacco smokers switched from smoking to vaping for two weeks but noted that this was similar to the effects observed when people quit smoking:

The clinical findings from the current study are similar to those that occur following verified smoking cessation. For example, during a successful period of quitting smoking, gingival bleeding doubled from 16% to 32% in a group of 27 smokers followed for 4–6 weeks, even though there were some improvements in the subjects’ plaque control. Results from this study are also consistent with studies that suggest a fairly rapid recovery of the inflammatory response following smoking cessation. (emphases added)

The benefits of switching from smoking to vaping are pervasive and include improvements in oral health.

On 2017 Jul 09, Lydia Maniatis commented:

"The former observation was expected, as human vision prioritizes processing as a function of distance from fixation (23⇓–25). "

Reference 25 is to Rovamo & Virsu (1979) "An estimation and application of the human cortical magnification factor. Exp Brain Res 37(3):495–510.. ." The authors seem to be making a statement of fact, based on this and two other references, that "human vision prioritizes processing as a function of distance from fixation."

However, according to Strasburger, Rentschler and Juttner, (2011):

"The strong, all-embracing hypothesis put forward by Rovamo & Virsu (1979) is hardly, if ever, satisfied." In other words, to the extent that it rests on the Rovamo citation, the first statement quoted above appears to me to be false. At best, it requires qualification. I don't know how reliable the other two citations are, I haven't checked.

On 2017 Apr 12, Konstantinos Fountoulakis commented:

None

On 2017 Apr 12, Konstantinos Fountoulakis commented:

This is a study on 70 patients suggesting that depressed people exposed to high early life stress (ELS) had a greater likelihood of remission when their amygdala showed hyperreactivity to socially rewarding stimuli, whereas for those with low-ELS exposure, amygdala hyporeactivity to both rewarding and threat-related stimuli predicted remission (1) The main problem with this study is that the data are uncontrolled. It is important to note that depressed patients have a high placebo response rate (up to 40%) in the short term (2) We also know that acute response to placebo is equal to up to 80% or more to the response to active drugs (3, 4), meaning that if we accept the additivity hypothesis (this is of course a matter of debate) (5, 6), most patient who acutely responded to active drug might be in reality placebo responders. Therefore the results of the study under discussion should not be accepted without caution. Adding to the above concerns is the report the STAR-D study, that one third of patients who remitted after step 1 will relapse within 4-5 months, while up to 50% will relapse from later steps (7). It is unknown what these patients stand for, however one could argue that at least some of them are patients unresponsive to antidepressants who however manifested a placebo response. Additional problems are that the patients included had low depression severity (mean HDRS=21) and low dosages of antidepressants used (on average 10 mg of escitalopram, 50-62.5 mg of sertraline and 87.5-90.8 mg of venlafaxine). Also, of prime importance is that the determining of early life stress (ELF) with the use of a self-report questionnaire alone could be misleading, since many depressed patients especially with character pathology might tend to over-report such events.<br> These problems exist mainly because the study under discussion tries to elucidate an issue concerning a mechanism of action without using an adequately controlling methodology. What that study suggests is that patients who acutely remit (no matter the reason) might present with the characteristics reported, but any inference concerning the role of pharmacotherapy per se is problematic.

References

1. Goldstein-Piekarski A, et al. (2016) Human amygdala engagement moderated by early life stress exposure is a biobehavioral target for predicting recovery on antidepressants. PNAS.
2. Furukawa TA, et al. (2016) Placebo response rates in antidepressant trials: a systematic review of published and unpublished double-blind randomised controlled studies. The lancet. Psychiatry.
3. Khan A, Leventhal RM, Khan SR, & Brown WA (2002) Severity of depression and response to antidepressants and placebo: an analysis of the Food and Drug Administration database. Journal of clinical psychopharmacology 22(1):40-45.
4. Gibbons RD, Hur K, Brown CH, Davis JM, & Mann JJ (2012) Benefits from antidepressants: synthesis of 6-week patient-level outcomes from double-blind placebo-controlled randomized trials of fluoxetine and venlafaxine. Archives of general psychiatry 69(6):572-579.
5. Yang H, Novick SJ, & Zhao W (2015) Testing drug additivity based on monotherapies. Pharmaceutical statistics 14(4):332-340.
6. Lund K, Vase L, Petersen GL, Jensen TS, & Finnerup NB (2014) Randomised controlled trials may underestimate drug effects: balanced placebo trial design. PloS one 9(1):e84104.
7. Rush AJ, et al. (2006) Acute and longer-term outcomes in depressed outpatients requiring one or several treatment steps: a STAR*D report. The American journal of psychiatry 163(11):1905-1917.

On 2017 May 30, Lydia Maniatis commented:

Comment 3: The speciousness of the interpretation may perhaps be better grasped if we imagine that the centroids contained patches differing in ways other than color. If, for example, some had been shaped like rectangles and others stars, would we have been justified in concluding that we were measuring the activities of rectangle and star "filters"? Or if some had been x's and some had been o's...etc. Color might seem like a simpler property than shape, but given that it is wholly mediated by the organization of the visual field and the resulting shape properties, this intuition is in error (the tendency of vision science publications to refer to color as a "low-level" property notwithstanding.)

In fact, while we're talking about shape, there can be little doubt that the arrangement (e.g. symmetrical vs asymmetrical) of the differently colored patches in the present type of experiment will affect the accuracy of the responses. The effects might, perhaps, be averaged out, but this doesn't mean that these "high-level" effects of organization aren't mediating the purportedly "low-level" effects of color at all times.

On 2017 May 29, Lydia Maniatis commented:

Comment 2:

There seems to have been a kind of natural selection in vision science (and of course not only vision science) in which the following practice has come to dominate: The results of ad hoc measurements made under arbitrary (poorly rationalized) conditions and fitted to “models” with the help of ad hoc post hoc mathematical adjustments are treated as though they amounted to, or could amount to, functional principles.

Thus, here, the data generated by a particular task are reified; the data patterns are labelled “attention filters,” and the latter are treated as though they corresponded to a fundamental principle of visual processing. But principles have generality, while the "attention filter" moniker is here applied in a strictly ad hoc fashion:

First, the model is based on an arbitrary definition of color in terms of isolated ""colors" on a "neutral" background (i.e. conditions producing the perception of particular color patches on a neutral background), whose attributes we are told are “fully described by the relative stimulation of long, medium and short wavelength sensitive retinal cones.” These conditions and, thus, the specific patterns of stimulation correlated with them, constitute only one of an infinite number of possible conditions and thus of patterns of stimulation. (The naive equating of cone activity with color perception is a manifestation of the conceptual problems discussed in my earlier comment.)

Second, the model is ad hoc (“particularized”); “The inference process is illustrated by the model of selective attention illustrated in Fig. 1B particularized for the present experiments.” What would the generalized form of the model look like?

Third, the results only apply to individual subject/context combinations: “The model’s optimally predictive filter fk(i)fk(i) is called the observed attention filter. It typically is a very good [post hoc] predictor of a subject’s observed centroid judgments.† Therefore, we say for short that fk(i)fk(i) is the subject’s attention filter for attending to color CkCk in that context.”

It is the case that different colors vary in their salience. We could perform any number of experiments under any number of conditions with any number of observers, and generate various numbers that reflected this fact. Our experiments would, hopefully, succeed in reproducing the general facts, but the actual numbers would differ. Unless underpinned by potentially informative rationalizations guiding experimental conditions, none of these quantifications would carry any more theoretical weight than any of the others (the value-added via quantification would be zero). There is, in other words, nothing special about the numbers generated by Sun et al (2017). They make no testable claims; their specific "predictions" are all post hoc. Their results are entirely self-referential.

On 2017 May 27, Lydia Maniatis commented:

"The visual images in the eyes contain much more information than the brain can process. An important selection mechanism is feature-based attention (FBA)."

There is something very wrong here. There are no visual images in the eyes, if by images we mean organized percepts - shaped figures with features such as colors, relative locations, etc. Such things are the products of the whole perceptual process, i.e. the products of a process that begins with the effects of the point stimulation of light striking photoreceptors on the retina, setting into motion dynamic interactions of the integrated neural elements of the visual system, ultimately leading to conscious percepts.

Thus, the mechanism being referenced (if it exists) is selecting from features of the conscious products of these perceptual processes, not from the initial point information or early stages of processing in the retina.

"...a color-attention filter describes the relative effectiveness with which each color in the retinal input ultimately influences performance."

Again, there are no colors in the retinal input, color being a perceptual property of the organized output. So we are missing a retinal-state-based description of what the proposed "filters" are supposed to be attending to. This is a problem since, as is well-known, the physical (wavelength) correlate of any perceived color can have pretty much any composition, because what is perceived locally is contingent on the global context.

The use of the term filter here seems inappropriate, its misuse linked to the failure to distinguish between the proximal stimulation and perceptual facts. The implication seems to be that we are dealing with a constraint on what will be perceived, whereas on the contrary we are dealing with selection from available perceptual facts, .

The theoretical significance of measuring jnd's is not clear, as they are known to be condition-sensitive in a way not predictable on the basis of available theory. The failure to discriminate between physical/perceptual facts also means it isn't clear which of these potential differences is being referred to.

On 2016 Dec 17, Matthew Romo commented:

In their article, Kelling and colleagues identified clinical preventive services recommended by the US Preventive Services Task Force (USPSTF) that can be offered by community pharmacists, including folic acid supplementation, smoking cessation, and screening for osteoporosis and HIV (1). Clinical services are typically thought of in the context of individual patient-provider interaction, but adapting a population-level approach is particularly important for pharmacy-based preventive care services. Specifically, pharmacies located in high poverty areas have a tremendous opportunity to not only meet their communities’ public health needs, but to also reduce health disparities.

New York City exemplifies the need for this type of population-level thinking because health differs greatly by neighborhoods, which often have both poverty and racial/ethnic distinctions. For example, there are major disparities in HIV incidence, with a median annual HIV diagnosis rate of 75.6 per 100,000 population in the highest poverty neighborhoods vs. 13.7 per 100,000 population in the lowest poverty neighborhoods (2). Pharmacy-based HIV testing is indeed feasible in the city (3) and could be a service targeted to residents of high-risk neighborhoods. Nonprescription syringe sales by pharmacies to injection drug users present a propitious opportunity to promote screening for HIV.

Smoking prevalence in the highest poverty neighborhoods of New York City is more than double that of the lowest poverty neighborhoods (29.7% vs. 14.3%) (4). Availability of tobacco in pharmacies is germane if community pharmacies are to be regarded by the public as health promoting institutions. Tobacco bans in pharmacies are, of course, strongly advisable but do not appear to have a real impact on tobacco availability in poorer neighborhoods where smoking prevalence is highest. In an analysis of 240 census tracts in Rhode Island (5), tobacco retail outlet density was positively associated with neighborhood poverty and when excluding pharmacies as tobacco retailers, this association did not change. Of course, the availability of nicotine replacement therapy on pharmacy shelves allows pharmacists to counsel patients on their use. However, as mentioned by Kelling and colleagues, simple frameworks like “Ask, Advise, Refer” can help pharmacists connect patients to a telephone quitline, which can provide counseling and linkage to programs offering free or low cost nicotine replacement therapy and medication. Linkage to quitlines could be coupled with existing services, such as administering seasonal influenza vaccines or screening for drug-tobacco interactions (which are numerous). These opportunities could give a non-intrusive opportunity to “ask” and “advise.”

Pharmacy services, like other healthcare services, can differ by neighborhood. In New York City, higher poverty neighborhoods are characterized as having significantly more independent (vs. chain) pharmacies and pharmacies that are more likely to have medications out of stock (6). Nevertheless, it appears that community pharmacists support providing clinical preventive services, regardless of the neighborhood poverty level where their pharmacy is located. This was suggested by a study assessing New York City pharmacists’ attitudes about providing vaccinations to their patients when state legislation was passed allowing them to do so (7).

As highlighted by Kelling and colleagues, community pharmacists are highly accessible (and often underutilized) healthcare professionals who are clearly capable of implementing USPSTF recommendations, among others. Pharmacies are also attractive conduits for improving public health, as demonstrated by successes in immunization uptake and most recently with expansion of non-prescription naloxone access. Because of their focus on population health, local health departments should partner with community pharmacists and pharmacy owners, if they are not doing so already, to better meet the public health needs of their communities. Community pharmacists not only have the potential to positively impact public health, but because of where they work in the community, they are ideally positioned to reduce health disparities.

Matthew L. Romo, PharmD, MPH

Department of Epidemiology and Biostatistics, CUNY Graduate School of Public Health and Health Policy; CUNY Institute for Implementation Science in Population Health; matthew.romo@sph.cuny.edu

REFERENCES 1. Kelling SE, Rondon-Begazo A, DiPietro Mager NA, Murphy BL, Bright DR. Provision of clinical preventive services by community pharmacists. Prev Chronic Dis 2016;13:160232. DOI: http://dx.doi.org/10.5888/pcd13.160232. 2. Wiewel EW, Bocour A, Kersanske LS, Bodach SD, Xia Q, Braunstein SL. The association between neighborhood poverty and HIV diagnoses among males and females in New York City, 2010-2011. Public Health Rep. 2016;131(2):290-302. 3. Amesty S, Crawford ND, Nandi V, Perez-Figueroa R, Rivera A, Sutton M, et al. Evaluation of pharmacy-based HIV testing in a high-risk New York City community. AIDS Patient Care STDS. 2015;29(8):437-44. 4. Perlman SE, Chernov C, Farley SM, Greene CM, Aldous KM, Freeman A, et al. Exposure to secondhand smoke among nonsmokers in New York City in the context of recent tobacco control policies: Current status, changes over the past decade, and national comparisons. Nicotine Tob Res. 2016;18(11):2065-74. 5. Tucker-Seeley RD, Bezold CP, James P, Miller M, Wallington SF. Retail pharmacy policy to end the sale of tobacco products: What is the impact on disparity in neighborhood density of tobacco outlets? Cancer Epidemiol Biomarkers Prev. 2016;25(9):1305-10. 6. Amstislavski P, Matthews A, Sheffield S, Maroko AR, Weedon J. Medication deserts: survey of neighborhood disparities in availability of prescription medications. Int J Health Geogr. 2012;11:48. 7. Crawford ND, Blaney S, Amesty S, Rivera AV, Turner AK, Ompad DC, et al. Individual- and neighborhood-level characteristics associated with support of in-pharmacy vaccination among ESAP-registered pharmacies: pharmacists' role in reducing racial/ethnic disparities in influenza vaccinations in New York City. J Urban Health. 2011;88(1):176-85.

On 2017 Aug 15, Victoria MacBean commented:

Plain English summary:

Parasternal intercostal electromyography (EMGpara) is a method used to measure breathing function by monitoring signals sent from the brain, to the parasternal intercostal muscles (muscles between the ribs). These muscles, together with the diaphragm (a thin muscle under the lungs) and some others, move together to control your breathing. EMGpara can be used to measure a person's neural respiratory drive (NRD), which is an indication of the strength of the respiratory (breathing) muscles under a certain amount of strain (how hard the muscles may have to work when they are coping with different diseases or environments). This method is an alternative to other more traditional practices that, for example, may involve the use of needles. Therefore, EMGpara is less invasive and ideal for monitoring the breathing muscles in many groups of people.

In the case of this study EMGpara was measured in healthy adults in order to discover what factors determine normal EMGpara readings. The participants were over the age of 18, and were of different body types and sexes.

In preparation for the EMGpara tests, each participant's body size, shape and composition was measured – this included taking note of their height, weight, hip and waist size, body fat percentage and body mass index – as well as tests to confirm that each person had normally functioning lungs.

Electrode stickers were placed on the chest to measure the EMGpara signals as the subjects breathed normally and effortfully. The tests were repeated at a later date to make sure the results could be reproduced, thereby checking that the EMGpara technique is consistent.

The study suggests that sex is the most important factor in determining EMGpara; a higher value for EMGpara was observed in the women who took part. This may be because in general, woman have smaller lungs and narrower airways compared to men and their respiratory muscles are usually not as strong. Age did not seem to have a significant effect on the readings; however this could have been because the average age of those involved was only 31, and those who were older were quite athletic, meaning their respiratory health was very good.

The results of this study can be used as a reference for what a normal EMGpara reading is, and therefore they can be used when assessing patients in the future. The study included many people from different backgrounds, so it is quite representative of the population. The study was also important in working out which methods and techniques are best for measuring EMGpara, as well as for highlighting possible areas of further research for future studies.

This summary was produced by Djenné Oseitwum-Parris, Year 12 student from Burntwood School, London, UK, as part of the authors' departmental educational outreach programme.

On 2017 Jan 18, Martin Hofmeister commented:

Physical activity is an underestimated modifiable factor

I thank Wat et al. for their very interesting review article "Associations between diabetic retinopathy and systemic risk factors" in the December 2016 issue of the Hong Kong Medical Journal. I agree with the authors but there is one lifestyle aspect worth mentioning. Recent studies suggest that regular physical activity can be a protective factor for the development of diabetic retinopathy (DR) and microvascular diabetes-related complications [1-6]. Physical activity and exercise is characterized by a simultaneous antihyperglycemic (reduction in haemoglobin A1c of -0.6%), antihyperlipidemic, antihypertensive, antioxidative, anti-inflammatory, and cardioprotective effects.

An example of neurobiological adaptations to exercise is the increased expression of neuroprotective factors such as brain-derived neurotrophic factor (BDNF) in the brain, blood, and muscles. Decreased plasma levels of BDNF were detected as an independent risk factor for DR and vision-threatening DR in Chinese type 2 diabetic patients [7-8]. The downregulation of BDNF probably has an important role in the complex and multifactorial pathogenesis of DR [9].

In a first study, Loprinzi also observed a positive association between sedentary behavior and DR [10]. The American Diabetes Association has recently updated its evidence-based recommendations on physical activity and exercise for diabetic patients. The main innovation is that diabetics should minimize the total amount of daily sedentary time. Prolonged sitting time should be interrupted every 30 min with brief (≤5 min) bouts of standing or light activity to improve the glycemic control [11].

In the case of severe nonproliferative and unstable proliferative retinopathy, it is recommended that vigorous-intensity activities be avoided [11].

REFERENCES

1) Dirani M, Crowston JG, van Wijngaarden P. Physical inactivity as a risk factor for diabetic retinopathy? A review. Clin Exp Ophthalmol 2014;42(6):574-81. Dirani M, 2014

2) Loprinzi PD, Brodowicz GR, Sengupta S, Solomon SD, Ramulu PY. Accelerometer-assessed physical activity and diabetic retinopathy in the United States. JAMA Ophthalmol 2014;132(8):1017-9. Loprinzi PD, 2014

3) Gutiérrez Manzanedo JV, Carral San Laureano F, García Domínguez G, Ayala Ortega C, Jiménez Carmona S, Aguilar Diosdado M. High prevalence of inactivity among young patients with type 1 diabetes in south Spain. Nutr Hosp. 2014;29(4):922-8. Gutiérrez Manzanedo JV, 2014

4) Loprinzi PD. Concurrent healthy behavior adoption and diabetic retinopathy in the United States. Prev Med Rep. 2015;2:591-4. Loprinzi PD, 2015

5) Li Y, Wu QH, Jiao ML, Fan XH, Hu Q, Hao YH, Liu RH, Zhang W, Cui Y, Han LY. Gene-environment interaction between adiponectin gene polymorphisms and environmental factors on the risk of diabetic retinopathy. J Diabetes Investig. 2015;6(1):56-66. Li Y, 2015

6) Praidou A, Harris M, Niakas D, Labiris G. Physical activity and its correlation to diabetic retinopathy. J Diabetes Complications. 2016 Jun 29. pii: S1056-8727(16)30256-2. doi: 10.1016/j.jdiacomp.2016.06.027. [Epub ahead of print]. Praidou A, 2017

7) Liu SY, Du XF, Ma X, Guo JL, Lu JM, Ma LS. Low plasma levels of brain derived neurotrophic factor are potential risk factors for diabetic retinopathy in Chinese type 2 diabetic patients. Mol Cell Endocrinol 2016;420:152-8. Liu SY, 2016

8) Guo M, Liu H, Li SS, Jiang FL, Xu JM, Tang YY. LOW SERUM BRAIN-DERIVED NEUROTROPHIC FACTOR BUT NOT BRAIN-DERIVED NEUROTROPHIC FACTOR GENE VAL66MET POLYMORPHISM IS ASSOCIATED WITH DIABETIC RETINOPATHY IN CHINESE TYPE 2 DIABETIC PATIENTS. Retina. 2016 Jun 27. [Epub ahead of print]. Guo M, 2017

9) Behl T, Kotwani A. Downregulated Brain-Derived Neurotrophic Factor-Induced Oxidative Stress in the Pathophysiology of Diabetic Retinopathy. Can J Diabetes. 2016 Nov 29. pii: S1499-2671(16)30079-X. doi: 10.1016/j.jcjd.2016.08.228. [Epub ahead of print]. Behl T, 2017

10) Loprinzi PD. Association of Accelerometer-Assessed Sedentary Behavior With Diabetic Retinopathy in the United States. JAMA Ophthalmol 2016;134(10):1197-8. Loprinzi PD, 2016

11) Colberg SR, Sigal RJ, Yardley JE, Riddell MC, Dunstan DW, Dempsey PC, Horton ES, Castorino K, Tate DF. Physical activity/exercise and diabetes: a position statement of the American Diabetes Association. Diabetes Care 2016; 39(11): 2065-79. Colberg SR, 2016

On 2016 Oct 26, Ian Dworkin commented:

Links to the dataset and scripts can be found here: https://github.com/DworkinLab/HaberDworkin_Evolution2016

and on DRYAD http://www.datadryad.org/resource/doi:10.5061/dryad.3b426

On 2016 Dec 04, Alessandro Rasman commented:

Increased levels of coagulation factors in Multiple Sclerosis: a defending mechanism from microbleedings?

On 2016 Nov 08, P P Wolkow commented:

It is true that culture negative blood samples are difficult to work with and the interpretation of the obtained results is not easy. We believe that we have taken every care to avoid contamination and to analyze our data accordingly. However, we understand that you might not fully concur with our results.

Indeed NTC and blood samples contain the same taxa as viewed on Fig. 3a. However based on this sole statement, one cannot claim that these samples are similar. In fact they are different, what can be seen below based on following examples at the Order level. Bifidobacteriales in the healthy blood constituted 73.0% of reads vs. 12.8% in NTC samples (p = 2.76 x 10-7). In our opinion this level of significance confirms that the groups are different. Few other examples based on Figure 4 data: healthy vs NTC vs sepsis: Actinomycetales: 2.0% vs 7.7% vs 30.9%- p=0.04; Pseudomonadales: 6.7% vs 0.0% vs 4.4% - p=0.006; Sphingomonadales: 0.2% vs 11.4% vs 7.3%-p=3 x 10-7.

It is true that NTC samples cluster with clinical samples in PCoA analysis, however they cluster with septic samples but not with healthy ones. Clustering with the latter could potentially mean that the results for the healthy people are untrue due to sequencing a contamination only. Please note that only 3 out of 5 NTC samples passed the analytical threshold and only these are depicted on Fig. 2. In the two NTC samples filtered out from further analysis, the numbers of reads were very low, 480 and 73, respectively. Also, contamination of simultaneously processed NTC samples should result in similar abundance of phyla in these samples, which is not the case (Fig. 3).

PCR conditions are provided in Table 1. Negative control procedure was exactly the same for all samples analyzed and should have no impact on the results. All samples were prepared in one batch so we did not expect a batching effect.

An idea of retrospective contaminant read removal is in our opinion controversial. There are deep inter-individual differences between the NTC samples. Removal of the reads based on mean number of reads would lead to skewed results and negative read numbers in some samples.

We expected that results depicted on Fig. 5 would be self-explanatory. However, we admit that providing qPCR data would be superior. We are grateful for drawing our attention to this issue which will have implications for our future work.

On 2016 Oct 27, Susannah Salter commented:

The paper by Gosiewski et al (PMID27771780) draws strange conclusions from the data. Culture-negative blood samples are certainly difficult to work with as they are more susceptible to the influence of DNA contamination.

In the text attention is drawn to the sequenced controls (water) but some assertions are patently untrue: for example stating that Bifidobacterales were a noteworthy constituent of healthy blood when it also makes up >10% proportion of the negative controls (Fig 4), or stating that the control profiles are "completely different" to the blood samples despite appearing to contain most of the same taxa (Fig 4) and clustering with the clinical samples on PCoA (Fig 2).

The authors provide no detailed information about the number of PCR cycles, the negative control procedure, kit batching of samples, retrospective contaminant read removal etc, which would lend confidence that the described patterns are not artefacts of sample processing. qPCR would also help to clarify the background contaminant DNA levels and allow more robust conclusions to be drawn.

The supplemental figure has the most potentially interesting information but unfortunately it is not labelled or described in text. If the contaminant taxa are removed, there may be some nice signals hiding in there.

On 2016 Dec 20, Natalie Clairoux commented:

Free version of this article available after 2017-10-19 at https://papyrus.bib.umontreal.ca/xmlui/handle/1866/16350

On 2016 Oct 24, Peter Hajek commented:

The title and the conclusion are misleading because they imply that the study followed up everyone who tried e-cigarettes (EC) to stop smoking. In fact, it only followed up people who tried but failed. Only people who tried e-cigarettes but reverted to smoking were included at baseline, successful quitters were no longer smokers and so were excluded.

Here is an analogy: Football scouts go round 100 schools and remove talented kids. Some time later they go round the same schools and also 100 new ones. The old schools now produce significantly less talent than the new ones. This is not because the earlier scout visit somehow damaged talent, but because talent was simply removed, in the same way successful quitting with EC removed good quitting prospects here.

The conclusion does not mention that the same effect applied to past use of stop-smoking medications, that result is also predictable for the same reasons.

On 2017 Dec 26, Elena Gavrilova commented:

The detailed answers to all E.V. Dueva’s comments regarding this article have been already published and could be found here: https://www.ncbi.nlm.nih.gov/pubmed/28036118 Briefly, Anaferon for children is not homeopathic drug; the manufacturing process of the drug preparation was described in the article concisely; the detailed answers to the concerns regarding experimental design have been provided; information about financial support was presented in the article.

On 2017 Dec 13, Evgenia V Dueva commented:

The Anaferon for children (AC) is homeopathic preparation. The authors state that «AC contained RAF of Abs to IFN-γ is a mixture of 12, 30,and 50 centesimal dilutions of antibodies to IFN-γ». RAF(release active form) of Abs (antibodies) is not an accepted scientific concept and the term appears only in the articles involving the commercial products of «MATERIA MEDICA HOLDING». According to Avogadro's law, 12 or more centesimal dilutions lead to a lack of any active substance in any amount of solution that a mouse can drink. It seems that AC is a disguised version of homeopathy and the authors have confused the reviewers with their vague description of AC.

Given the fact that there is no accepted mechanism of action for any treatment with such dilutions as in the case of AC and composition of initial AC solutions is unknown the simpler explanation for the observed antiviral effects is bias introduced by lack of proper randomization and blinding or the influence of undeclared contaminants.

In addition, this statement contradicts itself: «The authors declare no conflict of interest. Four authors have an affiliation to the commercial funders of this research study (OOO «NPF «MATERIA MEDICA HOLDING»)». «MATERIA MEDICA HOLDING» produces and markets AC, so the authors do have a conflict of interest. Even more, Oleg I. Epstein is the CEO of OOO «NPF «MATERIA MEDICA HOLDING».

The critical comment on this paper was published and can be found here: http://onlinelibrary.wiley.com/doi/10.1002/jmv.24761/full

On 2016 Oct 25, Anders von Heijne commented:

Among the suggestions of candidate measurements an established mechanism for providing feedback to previous clinicians about their diagnoses should provide feedback on all diagnoses, not only when a significant change in diagnosis has occured, in order to improve the sense of diagnostic accuracy for the idividual, the team and the caregiver. We need both positive and negative feedback!

On 2016 Dec 27, Richard Boyce commented:

The code is indeed available on github: https://github.com/OHDSI/StudyProtocols/tree/master/PGxDrugStudy

The SQL code is in a subfolder: https://github.com/OHDSI/StudyProtocols/tree/master/PGxDrugStudy/inst/sql/sql_server

It is an R package that should works with data in the OMOP common data model V4.5 or V5. Please contact me through github if you have questions about the code.

On 2016 Dec 21, Vojtech Huser commented:

This is an interesting study. The code to execute OHDSI studies are sometimes available on github. Are there any plans to release the study. The solution to excude topical drug is something our team could and the OHDSI collaborative re-use for other studies.

On 2016 Dec 20, Daniel Mietchen commented:

The article has been annotated as part of a journal club: https://via.hypothes.is/http://journals.plos.org/plosntds/article?id=10.1371/journal.pntd.0005023 .

On 2016 Oct 24, Sean Ekins commented:

This article was written to assist in finding additional collaborators that could participate. Please tweet us @openzika @collabchem @carolinahortago or email etc..

On 2016 Oct 24, Sean Ekins commented:

More details and press release are here http://www.collabchem.com/2016/05/19/zika-open-becomes-openzika-on-ibm-world-community-grid/

On 2016 Oct 24, Sean Ekins commented:

These attached slides also bring this project upto date http://www.slideshare.net/ekinssean/open-zika-presentation

On 2016 Oct 22, Atanas G. Atanasov commented:

It was a great conference, compliments to the organizers.

On 2016 Oct 30, Zvi Herzig commented:

The Holm at al. study was cited to show that Scandinavian smokeless tobacco (snus) delivers nicotine at speeds and quantities similar to those of smoking. Meta-analyses indicating that snus is not a significant cause of smoking-related disease are cited further below.

These should indicate that nicotine is not among the top risk compounds in tobacco smoke.

MOE may be a standard approach to minimize potential toxicological risks. But where there is epidemiological evidence from NRT and certain forms of smokeless tobacco showing that nicotine is not one of the major toxicological issues with cigarette smoke, then there is less reason to rely on MOEs.

On 2016 Oct 27, Dirk Lachenmeier commented:

Sorry, but I cannot find any evidence in your cited studies that would refute our conclusions. Especially the Holm et al. (1992) study is absolutely unsuitable to make such a conclusion. This was not even a short term trial, but blood nicotine was studied on a single day. Holm et al. (1992) conclude: "The snuff takers and cigarette smokers reported similar levels of subjective dependence on tobacco. Epidemiological study of Swedish snuff users could clarify whether the cardiovascular risks of tobacco are attributable to nicotine or to other smoke components". The long-term and chronic effects of nicotine were obviously not studied. As toxicologist it is also difficult to accept why a potentially toxic substance with a clear dose-response effect such as nicotine may not be assessed using internationally accepted indicators such as the margin of exposure. Obviously, benchmark dose data from epidemiology would be preferrable over animal data, but as we have detailed in our article, none of the epidemiology studies provided suitable dose-response information.

On 2016 Oct 26, Zvi Herzig commented:

The cited sources refute the report's conclusion that "nicotine is among the top risk compounds in tobacco smoke". They show that nicotine consumption in the context of snus—which delivers equal or more nicotine than smoking, and at similar absorption speeds Holm H, 1992—is not significantly associated with smoking-related disease

The MOE approach shouldn't be used to estimate risk where direct epidemiological evidence for the relevant dose is available. This should be self-evident.

On 2016 Oct 24, Dirk Lachenmeier commented:

Thank you for providing some further references on nicotine. However, none of these provide dose-response information required for quantitative comparative risk assessment. We have carefully screened through the literature to include any usable study, including human data (see table 1). It should also be noted that the European Food Safety Authority (EFSA) also used the Lindgren et al. study (which we have included) as point of departure for their risk assessment of nicotine.

On 2016 Oct 22, Zvi Herzig commented:

It is puzzling that the authors use the indirect margin of exposure (MOE) approach to evaluate harms, when epidemiological evidence is available, showing minimal risk of nicotine (at levels of consumption) in relation to cancer Lee PN, 2009, cardiovascular diseases Hansson J, 2012 Hansson J, 2014, other diseases Lee PN, 2013 or acute poisonings Royal College of Physicians, 2016.

On 2016 Oct 30, Erick H Turner commented:

Only published trials were examined in this study. This restriction seems problematic, considering the outcome of interest is time to publication. This departure from several past (and cited) studies on this topic may be a key reason for the authors' obtaining different conclusions.

On 2017 Mar 23, University of Kansas School of Nursing Journal Club commented:

Team Members: Miranda Hanchett, Katie Bolin, Lizzy Lothamer, Molly Meagher, Kathryn Noble, Alisa Schemmel, Amy Toth. [Class of 2017]

Background

In class, we learned that shared governance encompasses four main principles for nurses and other professionals. These principles include partnership, accountability, equity, and ownership. Each of these are necessary for team-based decision making in the realms of research, clinical matters, quality improvement, and others. However, one topic we did not explore in class was the impact that the level of nurse engagement in shared governance has on patient outcomes. Kutney-Lee et al’s (2016) article explored the different levels of nurse engagement across a variety of hospitals and how these levels affected patient satisfaction through appraisal of the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey data. This information is valuable to our knowledge as new graduate registered nurse (RN) professionals in order to understand that nurse involvement within the organization (beyond patient care) is vital to improving patient outcomes. This study also adds another dimension to our understanding of the four principles of shared governance and whom they affect.

Methods

Our team decided to select this article because it described the effects shared governance has on our patients in addition to how it affects nurses. Knowing these effects enables nurses to understand that involvement in hospital affairs can influence the quality of care and outcomes of our patients. The purpose of this study is to, “examine differences in nurse engagement in shared governance across hospitals and to determine the relationship between nurse engagement and patient and nurse outcomes” (Kutney-Lee et al., 2016, p. 605). This article is a cross-sectional observational study of three secondary data sources: the 2006-2007 Penn Multi-State Nursing Care and Patient Safety Survey of RNs from four states (California, New Jersey, Pennsylvania, and Florida), the 2007 American Hospital Association (AHA) Annual Survey of Hospitals, and the HCAHPS patient survey data from October 2006 to June 2007. The nurse survey was collected from large, random samples of RNs that were licensed in the four previously mentioned states. The AHA survey provided information about hospital characteristics and the HCAHPS survey provided information about overall patient experiences during hospitalization. Shared governance was measured using three items from the, “Participation in Hospital Affairs,” subscale of the Practice Environment Scale of the Nursing Work Index (PES-NWI). Nurse job outcomes and quality of care was measured using the Penn Multi-State Nursing Care and Patient Safety Survey of RNs. The HCAHPS survey provided information on patient measures while the AHA Annual Survey provided information about hospital characteristics, such as population density, teaching status, ownership, technology status, and size. Magnet recognition status was acquired from the American Nurses Credentialing Center (ANCC) website.

Findings

Shared governance has a large impact on patient outcomes. This study found that not only did shared governance contribute to more positive patient outcomes, but they also received a higher level of quality of care from their nurses. Nurses are in direct contact with the patients for the most amount of time and thus, are intimately in touch with patients’ wants and needs. They forge a personal relationship with the patient that no other health care provider can or will. Nurses are a valuable asset to hospital administration to improve the direction of resources and strategies to increase flow efficiency.

With the inclusion of floor nurses within the shared governance model, it has been shown that the reporting of poor patient outcomes, safety, and quality of care has declined. In effect, this has lowered the costs of penalties against the hospital for patient care problems, mismanaged discharge instructions and subsequent homecare, and readmission, and has increased reimbursement for quality of care. Hospitals have also saved millions of dollars by lowering their rate of nurse turnover (Kutney-Lee et al. 2016). In addition, the shared governance model has been shown to increase nurse satisfaction and reduce nurse burnout and the intent to leave. By incorporating floor nurses into the shared governance model, they feel more invested to contribute towards the system-level approach to improving both patient and nurse outcomes (Kutney-Lee et al. 2016).

Nursing Implications

Shared governance is extremely important to the nursing profession because it has been shown to increase employee engagement, which is related to an increase in job satisfaction, retention, profitability, and performance (Kutney-Lee et al. 2016). This study shows that nurses who worked at institutions where they had a greater opportunity to be engaged in shared governance were more likely to report better patient experiences and superior quality of care (Kutney-Lee et al. 2016). In our program, professionalism has been emphasized as a key factor in being a BSN-prepared nurse. A huge factor that relates to being a professional nurse is being actively involved in nursing boards, policies, and interprofessional teams, along with being leaders at the bedside. These factors help hospitals attain Magnet Recognition because they lead to structural empowerment for nurses in the workplace (American Nurses Credentialing Center, 2017). Through improving patient outcomes and satisfaction, the nursing satisfaction and reimbursement rates increase as well. From a micro- and macrosystem level, this is important because nurse turnover and low patient satisfactions scores tends to increase hospital cost. Hiring and training new staff is expensive and time consuming and can lower patient outcomes. These lower patient outcomes and satisfaction levels can result in lower HCAHPS scores, which reduces reimbursement amounts to the hospital. The study shows that nurses at hospitals where shared governance was promoted were less likely to report, “poor confidence in their patients’ ability to manage their care after discharge,” thus reducing readmission cost on the hospital (Kutney-Lee et al. 2016, p. 610). By increasing shared governance in hospitals, it is more fiscally responsible for the hospital and the nursing profession as a whole.

This information can benefit us as future nurses by helping us realize the importance of shared governance when looking at future employers. Being able to have a say and make decision in how we are allowed to practice gives us greater autonomy. This ability to practice autonomously and feel empowered to practice in a meaningful way leads to structural empowerment and greater job satisfaction (Laschinger, Finegan, Shamian, & Wilk, 2001). It will be important as new graduate nurses and as future nurse leaders to keep this information in mind for the well-being of our patients, employees, and self.

References

American Nurses Credentialing Center (2009). Announcing a new model for ANCC’s magnet recognition program. Retrieved from http://www.nursecredentialing.org/Magnet/NewMagnetModel.aspx

Kutney-Lee, A., Germack, H., Hatfield, L., Kelly, S., Maguire, P., Dierkes, A. Del Guidice, M., & Aiken, L. H. (2016). Nurse engagement in shared governance and patient and nurse outcomes. The Journal of Nursing Administration, 46(11), 605-612. doi:10.1097/nna.0000000000000412

Laschinger, H.K.S., Finegan, J., Shamian, J., & Wilk, P. (2001). Impact of structural and psychological empowerment on job strain in nursing work settings: Expanding Kanter’s model. The Journal of Nursing Administration, 31(5), 260-272. doi: 10.1097/NNA.0000000000000080

On 2017 Feb 13, David Reardon commented:

This analysis of perinatal psychiatric episodes by Munk-Olsen T, 2016¹ is flawed by the failure to examine the effects of prior pregnancy losses. Numerous studies have shown that prior fetal loss, either from miscarriage, stillbirth, or induced abortion, increases the risk psychiatric disorders during and after subsequent pregnancies.^2-7 There is even a dose effect, with multiple losses associated with elevated rates compared to a single loss.²

Notably, the heightened risk of mental illness following miscarriage and abortion have also been confirmed by several of Munk-Olsen’s own studies.^8-10 Unfortunately, while abortion was used as a control variable in two cases, the effects were not described.^8,10

In light of the literature, Munk-Olsen T, 2016's conclusion that it is not possible to “predict which women will become ill postpartum”¹ is an overstatement. There is strong evidence that prior fetal loss is risk factor.

It is strongly recommended that the authors of this most recent study¹ should publish a reanalysis showing the effects of prior pregnancy loss relative to (a) one or more abortions and (b) one or more miscarriages or other natural losses. These results could lead to improved screening to identify women who may benefit from additional care.

Editors and peer reviewers should be alert to the recommendation that all studies relative to the intersection between mental and reproductive health should always consider the effects of prior pregnancy loss.^11-13 In particular, both the Royal College of Psychiatrists¹⁴ and the American Psychological Association¹⁵ have lamented the lack of high quality studies examining the statistical associations between abortion and mental health. Record linkage studies from national data sets, such as that examined by Munk-Olsen, can help to fill this gap of knowledge . . . but only if they include analyses examining these effects.

References

1) Munk-Olsen T, Maegbaek ML, Johannsen BM, et al. Perinatal psychiatric episodes: a population-based study on treatment incidence and prevalence. Transl Psychiatry. 2016;6(10):e919. doi:10.1038/tp.2016.190.

2) Giannandrea SAM, Cerulli C, Anson E, Chaudron LH. Increased risk for postpartum psychiatric disorders among women with past pregnancy loss. J Womens Health (Larchmt). 2013;22(9):760-768. doi:10.1089/jwh.2012.4011.

3) Gong X, Hao J, Tao F, et al. Pregnancy loss and anxiety and depression during subsequent pregnancies: data from the C-ABC study. Eur J Obstet Gynecol Reprod Biol. 2013;166(1):30-36. doi:10.1016/j.ejogrb.2012.09.024.

4) Blackmore ER, Côté-Arsenault D, Tang W, et al. Previous prenatal loss as a predictor of perinatal depression and anxiety. Br J Psychiatry. 2011;198(5):373-378. doi:10.1192/bjp.bp.110.083105.

5) Räisänen S, Lehto SM, Nielsen HS, Gissler M, Kramer MR, Heinonen S. Risk factors for and perinatal outcomes of major depression during pregnancy: a population-based analysis during 2002-2010 in Finland. BMJ Open. 2014;4(11):e004883. doi:10.1136/bmjopen-2014-004883.

6) Montmasson H, Bertrand P, Perrotin F, El-Hage W. Facteurs prédictifs de l’état de stress post-traumatique du postpartum chez la primipare. J Gynécologie Obs Biol la Reprod. 2012;41(6):553-560. doi:10.1016/j.jgyn.2012.04.010.

7) McCarthy F, Moss-Morris R, Khashan A, et al. Previous pregnancy loss has an adverse impact on distress and behaviour in subsequent pregnancy. BJOG An Int J Obstet Gynaecol. 2015;122(13):1757-1764. doi:10.1111/1471-0528.13233.

8) Munk-Olsen T, Bech BH, Vestergaard M, Li J, Olsen J, Laursen TM. Psychiatric disorders following fetal death: a population-based cohort study. BMJ Open. 2014:1-6. doi:10.1136/bmjopen-2014-005187.

9) Meltzer-Brody S, Maegbaek ML, Medland SE, Miller WC, Sullivan P, Munk-Olsen T. Obstetrical, pregnancy and socio-economic predictors for new-onset severe postpartum psychiatric disorders in primiparous women. Psychol Med. 2017:1-15. doi:10.1017/S0033291716003020.

10) Munk-Olsen T, Agerbo E. Does childbirth cause psychiatric disorders? A population-based study paralleling a natural experiment. Epidemiology. 2015;26(1):79-84. doi:10.1097/EDE.0000000000000193.

11) Reardon DC. Lack of pregnancy loss history mars depression study. Acta Psychiatr Scand. 2012;126(2):155. doi:10.1111/j.1600-0447.2012.01880.x.

12) Sullins DP. Abortion, substance abuse and mental health in early adulthood: Thirteen-year longitudinal evidence from the United States. SAGE Open Med. 2016;4(0):2050312116665997. doi:10.1177/2050312116665997.

13) Coleman PK. Abortion and mental health: Quantitative synthesis and analysis of research published 1995-2009. Br J Psychiatry. 2011;199(3):180-186.

14) National Collaborating Centre for Mental Health. Induced Abortion and Mental Health: A Systematic Review of the Mental Health Outcomes of Induced Abortion, Including Their Prevalence and Associated Factors. London, UK: Academy of Medical Royal Colleges; 2011. http://www.aomrc.org.uk/wp-content/uploads/2016/05/Induced_Abortion_Mental_Health_1211.pdf.

15) Major B, Appelbaum M, Beckman L, Dutton MA, Russo NF, West C. Report of the APA Task Force on Mental Health and Abortion. Washington, DC: American Psychological Association; 2008. http://www.apa.org/pi/women/programs/abortion/mental-health.pdf.

On 2016 Dec 06, David Reardon commented:

None

On 2016 Dec 05, David Reardon commented:

None

On 2017 Sep 23, Christopher Southan commented:

With a reported IC50 of 28 μM, this compound can be neither potent nor selective

On 2017 Jan 24, GARRET STUBER commented:

• This post-publication peer review was written for a group assignment for NBIO733: Circuits and Behavior – A journal club course organized by Dr. Garret Stuber at the University of North Carolina at Chapel Hill. This critique was written by students in the course, and edited by the instructor.

The recent work by Kim, J. et al. provides excellent evidence of genetically, spatially, and functionally distinct cell types in the basolateral amygdala (BLA). Studies aimed at molecular marker discovery often turn to less precise experiments such as bulk mRNA sequencing or proteomics in heterogeneous tissues to profile cell markers. In this study the authors first identify changes in the transcriptional profiles of fos+, activated cells following controlled delivery of appetitive or aversive stimuli to correlate with transcriptional markers. The authors convincingly demonstrate that two distinct cell populations marked by the expression of unique genes (Ppp1r1b+ and Rspo2) are preferentially activated by reward-related or aversion-related stimuli.

In open discussion we discussed the use of Cartpt-Cre to represent Ppp1r1b+ cells (as opposed to using/generating a Ppp1r1b-Cre mouse). While the supplementary figures demonstrate that this Cre-driver mouse also labels several Ppp1r1b- cells (~23%), the authors demonstrate consistent functional properties of Cartpt-Cre cells across multiple behavioral and electrophysiological paradigms. This provides strong evidence that the use of this animal is justified and informative of their proposed circuit. Additional experiments to further verify the use of this animal could test positive and negative valence in the context of other sensory modalities (olfactory, gustatory, etc.) comparable to the initial c-Fos expression experiments.

An additional concept that would be interesting to explore is the relative ‘strength’ each population of neurons appears to have with respect to positive and negative valences. Behaviorally, it appears Rspo2 neurons may have a greater influence on their respective valence (Figure 4b-e) as well as having a larger antagonistic effect (Figure 5a-f). This is a difficult claim to make considering the opposing behavioral paradigms cannot be considered to have equal strength in their respective valence. However, stronger antagonistic silencing illustrated by c-Fos (Figure5g-i) and cell recordings (Figure 6a-h) bring up the possibility. Importantly, the fact that Rspo2 neurons outnumber Ppp1r1b neurons (Table1) in the BLA may contribute to this. The potential for antagonistic microcircuits through local inhibitory interneurons is an additional avenue to explore. Another factor in this circuitry is that Rspo2 cells form direct synapses with other Rspo2 cells and vice versa in to form a synchronous circuit upon stimulation. Establishing whether these cell types share connectivity with neurons of the same (or similar) identity would be informative of the dynamics of the circuit.

An important note the authors highlight is the likelihood that different cell subpopulations reside within the Rspo2+ and Ppp1r1b+ neuron groups. Further exploration of markers found in the initial microarray analysis may shed light on these subpopulations and provide insight as to how BLA cells are programmed to function. Additionally, next generation single cell RNA-sequencing techniques could provide the necessary acuity in transcriptomic profiling of these potentially heterogeneous cell types.

The diversity of projection termini from these neurons also suggest cell heterogeneity and highlight what are possibly the most interesting findings of this article. Kim, J. et al. build on previous evidence that diverging circuits and cell populations encode positive and negative valence information separately in the BLA1, 2. Here, the authors successfully label and characterize these cell populations, but note important differences in projection targets not realized in previous studies. Firstly, Kim, J. et al. found that positive valence-associated neurons project to the medial nucleus of the central amygdala (CeM), contrary to previous findings that found projections to this area are largely associated with aversive stimuli 1, 2. Secondly, Kim et al., found that both positive and negative valence BLA neurons project to the nucleus accumbens (NAc). This builds on previous by Namburi, P. et al. showed that inhibiting NAc projecting BLA neurons did not affect fear or rewarding behavior in the context of conditioned learning1. The proposed heterogeneity of NAc projecting BLA neurons described in the current paper may account for this.

To conclude, the recent findings of structurally, spatially, and functionally antagonistic neurons in the BLA provide an interesting and important avenue to further dissect circuit architecture underlying complex behaviors as well as providing a genetic entry point into better understanding these circuits.

[1] Namburi, P. et al. (2015). A circuit mechanism for differentiating positive and negative associations. Nature. 520, 675-678. [2] Beyeler, A. et al. (2016). Divergent routing of positive and negative information from the Amygdala during memory retrieval. Neuron. 90, 2, 348-361.

On 2017 Jan 15, Stephen Strum commented:

Important paper needing follow-up re durability of PSA response post RT. Would be helpful to have seen predictions of local vs systemic disease based on neural nets, nomograms & how they related to Axumin findings.

On 2016 Dec 29, Steve Alexander commented:

Oleamide (https://pubchem.ncbi.nlm.nih.gov/compound/5283387, called 9-octadecenamide here) has previously been investigated as a ligand at all three PPARs in vitro https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3414753/ Interesting to see the presence of other primary amides in the brain, notably palmitamide (https://pubchem.ncbi.nlm.nih.gov/compound/69421, called hexadecanamide here). An essential next couple of steps will be to identify the synthetic and degradative pathways associated with these ligands, and how/whether these compounds change with patho/physiological influences.

On 2016 Oct 24, Sean Ekins commented:

And here is post about why it took so long to reach Pubmed http://www.collabchem.com/2016/10/18/zika-homology-models-paper-makes-it-to-pubmed-6-months-after-publishing-in-f1000research/

On 2016 Oct 24, Sean Ekins commented:

more details and press release are here http://www.collabchem.com/2016/05/19/zika-open-becomes-openzika-on-ibm-world-community-grid/

On 2016 Oct 24, Sean Ekins commented:

Here is an update to the article in the form of slides - also goes into far more detail http://www.slideshare.net/ekinssean/open-zika-presentation

On 2016 Oct 18, Christopher Southan commented:

Ancilliary information https://cdsouthan.blogspot.se/2016/02/med-chem-starting-points-for-zika.html

On 2016 Nov 08, Jeffrey Gross commented:

Please be informed that the submission of our manuscript was cancelled within 24 hours of its initial submission to Experimental and Molecular Pathology. Unfortunately, and despite our many subsequent reminders, the journal went ahead and sent our paper for review and then published it online without our consent.

On 2017 Jan 12, Meg Waraczynski commented:

Thank you for your insights. In hindsight, we should have been much clearer in indicating that we were only strongly inferring the involvement of CaV1.3 channels in our behavioral results, but that we have no direct evidence for this. The inference was based on the facts reviewed in the Introduction, that (1) only CaV1 type channels have been linked to the activity of basal forebrain medium spiny neurons (reference 4); (2) of the CaV1 family, only 1.2 and 1.3 type channels are abundant in the brain (reference 3); and (3) the activation dynamics of 1.3 channels correspond much more closely to the activity state dynamics of medium spiny neurons than do the activation dynamics of 1.2 channels (reference 26). We hoped to use 1.3-specific drugs but, as noted in the Introduction, all such drugs we could find required the use of brain-toxic solvents. The dosages we used were selected based on dosages used by others who intracerebrally injected these drugs to affect behavior (references 1, 6, 7, and 16). We did not intend to focus on implicating CaV1.3 channels specifically in our observations, nor did we intend to have others use our paper as evidence that diltiazem and verapamil are CaV1.3-specific. We regret if this occurs. Our tentative conclusions as to the reward-relevant function of the system we are studying would remain the same even if it were found that our drug injections acted on mechanisms other than CaV1.3 channels specifically. We will be much more cautious when referring to this work in the future to emphasize these functional conclusions and not to implicate CaV1.3 channels specifically.

On 2017 Jan 03, Joerg Striessnig commented:

From a pharmacological point of view this is a very poor paper. It is common knowledge that verapamil and diltiazem have never been shown to be selective for Cav1.3 channels. Differential interaction with subdomains of the channel (their ref 19) has been studied with skeletal muscle channels and later with Cav1.2 (class C) L-type channels. Moreover, due to the higher concentrations for L-type channel block, verapamil and diltiazem also tend to inhibit other ion channels, such as Cav2 channels (PMIDs: 8574653, 10385261), at concentrations also inhibiting L-type channels. Here concentrations of 5 micrograms drug/0.5 microliter were infused, corresponding to about 20 mM concentrations, 10 times higher than the extracellular calcium concentration. There is no published evidence justifying their interpretation of a specific involvement of Cav1.3, as misleadigly mentioned even in the title. This interpretation by the authors and the failure by the reviewers to point out these limitations confuses readers and may even trigger further misleading experiments citing this paper as evidence for Cav1.3-selectivity of diltiazem and verapamil.

On 2016 Oct 26, Lydia Maniatis commented:

Natural scenes

The use of “natural scene statistics” is popular in current vision science and directly linked to its conceptual confusion.

In the words of the authors, “Biological systems evolve to exploit the statistical relationships in natural scenes….”

I want to first address the authors’ use of the term “natural scenes” and its implications, and move on to the problem of the validity and implications of the above quote in a subsequent comment.

“Natural scenes” is a very broad category, even broader given that the authors include in it man-made environments. In order to be valid on their own terms, the “statistics” involved – i.e. the correlations between “cues” and physical features of the environment – must hold across very different distances and orientations of the observer to the world, and across very different environments, including scenes involving close-ups of human faces.

Describing 96 photographs taken of various locations on the University of Texas campus from a height of six feet, a camera perpendicular to the ground, at distances of 2-200 meters as a theoretically meaningful, representative sample of “natural scenes” seems rather flakey. If we include human artifacts, then what count as “non-natural scenes” ?

The authors themselves are forced to confront (but choose to sidestep) the sampling problem when they note that “previous studies have reported that surfaces near 0° of slant are exceedingly rare in natural scenes (Yang & Purves, 2003), whereas we find significant probability mass near 0° of slant. That is, we find—consistent with intuition—that it is not uncommon to observe surfaces that have zero or near-zero slant in natural scenes (e.g., frontoparallel surfaces straight ahead).”

(Quite frankly, the authors’ intuition is causing them to confuse cause and effect, since we have a behavioral tendency to orient ourselves to objects so that we are in a fronto-parallel relationship to surfaces rather than in an oblique relationship to them, thus biasing the “statistics” in this respect).

They produce a speculative, technical and preliminary rationalization for the discrepancy between their distributions and those of Yang and Purves, leaving clarification to “future research.”

What they don’t consider is the sampling problem. Is there any doubt WHATSOEVER that different “natural scenes” - or different heights, or different angles of view, or different head orientations - will produce very different “prior probabilities”? If this is a problem, it isn’t a technical one.

On 2016 Oct 26, Lydia Maniatis commented:

Orientation isn’t prior to shape

The idea that slant/tilt are judged prior to shape, which Burge et al have adopted, suffers from the problems discussed above, and from empirical evidence that contradicts it.

The notion dates back at least to Marr’s 2.5D sketch. As Pizlo (2008) observes, “Marr assumed that figure-ground organization was not necessary for providing the percept of the 3D shape of an object” and asks “How could he get a way with this so long after the Gestalt psychologists had revolutionized perception by demonstrating the importance of figure-ground organization?”

Pizlo references experiments using wire objects (e.g. Rock & DiVita, 1987) that have shown that figure-ground organization is key to the shapes that actual 3D objects produce in perception, “even when binocular disparity or other depth cues are available.”

In general, if Marr had been correct in assuming that depth orientations of edges are prior to, and sufficient or necessary for, shape perception, then monocular perception would have no objective content, and 3D pictorial percepts would not occur, unless some special mechanisms had evolved just for this purpose.

In short, tilt-to-shape is not a principled, credible premise.

On 2016 Oct 26, Lydia Maniatis commented:

the right angle, and is linked to each of the other two, and each of the latter sit at the apex of acute angles, and are connected to each other. (What is being grouped are all the points inside of the perceptually constructed triangular outline).

If we now add a single point to the group, so that the set is compatible with a square, our rule will require that the connection between the two latter points be discarded, as both become the apex of right angles bounding a square and both are linked to the new point. This is, in fact, what happens in perception. So applying a rule to the three points, and a rule to the single point, locally, would not add up to the square contour, in principle; and does not, in perception.

Thus, when the authors assert that…

“the visual system starts with local measurements then combines those local measurements into the global representations; the more accurate the local measurements, the more accurate the global representation,”

…they are making an assertion that might sound simple and commonsensical to a layman (which is perhaps why it is so tenacious) but which is not justified for a vision scientist, any more than it is to say that we can build a house of cards one card at a time, or hear the sound of one hand clapping.

The use of “local cues” is a contemporary version of the reductionist approach to perception known as structuralism/introspectionism with its “sensory elements.” This approach couldn’t address the logical problem of organization of the visual field into shaped objects, discussed above, without invoking “experience” in a paradoxical and inconsistent fashion. “Cues” are similarly impotent.

On 2016 Oct 25, Lydia Maniatis commented:

As discussed, the best the authors could achieve in predicting measured “3D tilt” using their cues was not very good. Nevertheless, they describe their results as “complex,” “rich” and “detailed” in the sense that they feel able to discern some patterns in the generally inaccurate data that might be theoretically important or useful. For example, they say performance was often better when the three cues were in agreement. They propose to go on to compare performance of the model to performance of humans in psychophysical experiments. It seems to me that an important step to take prior to psychophysical testing is to test the model on its own terms; that is, to take a second set of “natural” images (perhaps of a different campus, or a national park) and test whether the ad hoc model derived from the first set will produce a qualitatively similar dataset. Will the two datasets, in all their richness and complexity, be mutually, statistically consistent? How will the authors compare them? If the data do not prove qualitatively repeatable, then p/p experiments would seem premature.

p.s. The open-endedness of the term "natural scene," in which the authors include man-made environments, imposes quite a serious replicability burden on the model. (The sampling problem (assuming the inductive approach was viable) includes the fact that arguably more time is spent by humans looking at human faces and bodies than at trees and shrubs). How many "scenes" should we test? Nevertheless, at least one attempt seems a minumum.

On 2016 Oct 22, Lydia Maniatis commented:

Part 1 This paper is all too similar to a large proportion of the vision literature, in which fussy computations thinly veil a hollow theoretical core, comprised of indefensible hypotheses asserted as fact (and thus implicitly requiring no justification), sometimes supported by citations that only weakly support them, if at all. The casual yet effective (from a publication point of view) fashion in which many authors assert popular (even if long debunked) fallacies and conjure up other pretexts for what are, in fact, mere measurements without actual or potential theoretical value is well on display here.

What is surprising in, perhaps, every case, is the willful empirical agnosia and lack of common sense, on every level – general purpose, method, data analysis - necessary to enable such studies to be conducted and published. A superficial computational complexity adds insult to injury, as many readers may wrongly feel they are not competent to understand and evaluate the validity of a study whose terms and procedures are so layered, opaque and jargony. However, the math is a distraction.

Unjustified and/or empirically false assumptions and procedures occur, as mentioned, at every level. I discuss some of the more serious ones below (this is the first of a series of comments on this paper).

1. Misleading, theoretically and practically untenable, definitions of “3D tilt” (and other variables).

The terms slant and tilt naturally refer to a geometrical characteristic of a physical plane or volume (relative to a reference plane). The first sentence of Burge et al’s abstract gives the impression that we are talking about tilt of surfaces: “Estimating 3D surface orientation (slant and tilt) is an important first step toward estimating 3D shape. Here, we examine how three local image cues …should be combined to estimate 3D tilt in natural scenes.” As it turns out, the authors perform a semantic but theoretically pregnant sleight of hand in the switch from the phrase “3D surface orientation (slant and tilt)” to the phrase “3D tilt” (which is also used in the title).

The obvious inference from the context is that the latter is a mere short-hand for the former. But it is not. In fact, as the authors’ finally reveal on p. 3 of their introduction, their procedure for estimating what they call “3D tilt” does not allow them to correlate their results to tilt of surfaces: “Our analysis does not distinguish between the tilt of surfaces belonging to individual objects and the tilt (i.e. orientation [which earlier was equated with “slant and tilt”]) of depth discontinuities…We therefore emphasize that our analysis is best thought of as 3D tilt rather than 3D surface tilt estimation.”

“3D tilt” is, in effect, a conceptually incoherent term made up to coincide with the (unrationalised) procedure used to arrive at certain measures given this label. I find the description of the procedure opaque, but as I am able to understand it, small patches of images are selected, and processed to produce “3D tilt” values based on range values collected by a range finder within that region of space. The readings within the region can be from one, two, three, four, or any number of different surfaces or objects; the method does not discriminate among these cases. In other words, these local “3D tilt values” have no necessary relationship to tilt of surfaces (let alone tilt of objects, which is more relevant (to be discussed) and which the authors don’t address even nominally). We are talking about a paradoxically abstract, disembodied definition of “3D tilt.” As a reader, being asked to “think” of the measurements as representing “3D tilt” rather than “3D surface tilt” doesn’t help me understand either how this term relates, in any useful or principled way, to the actual physical structure of the world, nor to the visual process that represents this world. The idea that measuring this kind of “tilt” could be useful to forming a representation of the physical environment, and that the visual system might have evolved a way to estimate these intrinsically random and incidental values, is an idea that seems invalid on its face - and the authors make no case for it.

They then proceed to measure 3 other home-cooked variables, in order to search for possible correlations between these and “3D tilt.” These variables are also chosen arbitrarily, i.e. in the absence of a theoretical rationale, based on: “simplicity, historical precedence, and plausibility given known processing in the early visual system.” (p. 2). Simplicity is not, by itself, a rationale – it has to have a rational basis. At first glance, at least the third of these reasons would seem to constitute a shadow of a theoretical rationale, but it is based on sparse, premature and over-interpreted physiological data primarily of V1 neuron activity. Furthermore, the authors’ definitions of their three putative cues: disparity gradient, luminance gradient, texture gradient, are very particular, assumption-laden, paradoxical, and unrationalised.

For example, the measure of “texture orientation” involves the assumption that textures are generally composed of “isotropic [i.e. circular] elements” (p. 8). This assumption is unwarranted to begin with. Given, furthermore, that the authors’ measures at no point involve parsing the “locations” measured into figures and grounds, it is difficult to understand what they can mean by the term “texture element.” Like tilt, reference to an “isotropic texture element” implies a bounded, discrete area of space with certain geometric characteristics and relationships. It makes no sense to apply it to an arbitrary set of pixel luminances.

Also, as in the case of “3D tilt” the definition of “texture gradient” is both arbitrary and superficially complex: “we define [the dominant orientation of the image texture] in the Fourier domain. First, we subtract the mean luminance and multiply by (window with) the Gaussian kernel above centered on (x, y). We then take the Fourier transform of the windowed image and comute the amplitude spectrum. Finally, we use singular value decomposition ….” One, two, three….but WHY did you make these choices? Simplicity, historical precedence, Hubel and Wiesel…?

If, serendipitously, the authors’ choices of things to measure and compare had led to high correlations, they might have been justified in sharing them. But as it turns out, not surprisingly, the correlations between “cues” and “tilt” are “typically not very accurate.” Certain (unpredicted) particularities of the data which to which the authors speculatively attribute theoretical value (incidentally undermining one of their major premises) will be discussed later.

On 2016 Oct 19, Jonathan Eisen commented:

Note - I am one of the authors of this paper.

There is a sentence in the paper that could be worded more carefully. I was pointed to this by a colleague. The sentence is "Gordonia sp. strain UCD-TK1 contains 5,032 coding sequences, and 64 noncoding RNAs."

It would be more accurate to say "The annotation of Gordonia sp. strain UCD-TK1 contains predicted 5,032 coding sequences, and 64 putative noncoding RNAs."

This would make it more clear that we do not have experimental evidence regarding how many coding sequences or ncRNAs are present in this organism.

On 2017 Jul 02, Karsten Suhre commented:

I think these PubMed pages should be a bit more self-explanatory - I came to this page while searching for "Metabolomic profiles delineate potential role for sarcosine in prostate cancer progression."

On first view this looked to me as if there was a research misconduct problem with this paper. However, after reading through the linked PMC document, I learned that the contrary was the case: The papers listed here have been plagiarized in an NIH grant application.

These papers are the victims, not the perpetrators - but on first glance this web site suggests otherwise.

On 2017 Jun 02, Eric Robinson commented:

The method and analysis adopted in this research are flawed and the conclusions are incorrect.

Action before commenting on pubmed: I contacted the journal and the authors in question with my concerns. The journal ignored a number of my emails and after an internal review decided not to issue a correction or retraction. I requested the results of the internal review, but the journal has not responded. The first author appears to have been a Masters student at the senior author’s institute and I was unable to contact the first author. The senior author (BM Corfe) informed me on the telephone that he was not prepared to discuss the research and instead advised me to raise any points in a public domain. The senior author also advised me that he would not be considering a correction or retraction of the work and that the research team stood by all of the conclusions made. Given the journal’s position on the study and my frustration with their handling of this, I was not prepared to support the journal by publishing a letter to the editor.

Research question: The researchers wanted to examine whether there is a link between self-reported appetite (self-reported subjective feelings of hunger) and energy intake; do participants who report feeling hungry eat more than participants who report feeling less hungry? They conducted what they described as a ‘systematic’ review and examined over 400 articles.

Conclusions drawn: The authors conclude that self-reported appetite (e.g. subjective feelings of hunger) ‘does not predict energy intake’ (title of article) and an associated University press release stated that ‘there is no link between how hungry we feel and the amount of calories we consume’.

Is this a spoof or hoax article? At first I thought this article may be a hoax, because the conclusion that self-reported hunger is in no way predictive of how much a person eats, is odd. Previous research shows that self-reported hunger/appetite does predict how much a person eats, but as you might expect, the correlation between self-reported hunger and energy intake is not perfect. Recently, Sadoul et al. (1) show this to be the case in an analysis of 23 studies that assessed self-reported appetite and ad-libitum meal energy intake. Robinson et al. (2) show this to the case in an analysis of 31 studies that assessed self-reported hunger and ad-libitum intake of snack foods.

Flawed method: There are a number of texts on best practice for conducting systematic reviews and synthesising data from multiple studies. Rather than using standard meta-analytic methods (e.g. combining weighted correlation coefficients between self-reported hunger and energy intake from studies), the researchers scored each study in their review as either providing evidence of a ‘link’ or evidence of ‘no link’ between self-reported hunger and energy intake. The scoring system used was inappropriate in a number of ways. For example, if an experimental manipulation in a study led to a change in energy intake without a change in self-reported hunger, in the present review this constituted evidence that self-reported hunger does not predict energy intake. This line of reasoning is a logical fallacy because it is based on the premise that a) energy intake can only be affected by self-reported hunger and b) energy intake being affected by anything other than subjective feelings of hunger proves that subjective appetite is in no way related to energy intake. Energy intake can be increased or decreased by a multitude of factors and many of these will not act on energy intake by altering self-reported hunger.

Flawed analysis: The authors’ main analysis was dependent on counting studies that provided statistically significant findings vs. those that did not, which is not considered best practice as it ignores considerations of sample size, statistical power and how heavily each study should be weighted in analyses. The above points aside, the authors went on to report that approximately 49% of studies they surveyed found a ‘link’ and 51% found ‘no link’ between subjective self-reported hunger and energy intake. This is actually highly convincing evidence that there is a link between self-reported hunger and energy intake, because if there was ‘no link’ we would expect to see closer to only 5% of all studies finding a ‘link’ (typical alpha level of .05), as opposed to the 49% reported.

Invalid conclusions: A combination of flawed methods and analyses results in incorrect conclusions.

A sobering experience: I noticed this study because of the bizarre conclusion it made; hunger in no way relates to how much we eat. I reached out to the authors several times over email. In the end I had to ring the senior author’s office phone to speak to him, but as noted the senior author was not prepared to discuss his research or revise his position on this research. This to me is direct experiential evidence that some scientists do not appear to care about the quality and accuracy of research they conduct and publish.

(1) Sadoul BC, Schuring EA, Mela DJ, Peters HP. The relationship between appetite scores and subsequent energy intake: an analysis based on 23 randomized controlled studies. Appetite 2014; 83: 153-159

(2) Robinson E, Haynes A, Hardman CA, Kemps E, Higgs S, Jones A. The bogus taste test: Validity as a measure of laboratory food intake. Appetite 2017; 116: 223-231.

On 2017 Mar 20, Prajak Barde commented:

One of the objective of the meta-analysis by Bai B et al is to assess the relationship between human cytomegalovirus and colorectal cancer (CRC) risk. Author analyzed four studies to demonstrate that tumor tissues had a significantly higher rate of HCMV infection (OR = 6.59, 95% CI = 4.48–9.69), thus confirming that CRC tissue is significantly burdened with HCMV DNA as compared to adjacent normal tissues. However, it is not clear how the conclusion of an increased risk of CRC due to HCMV infection is drawn. As detection of HCMV DNA in CRC tissues by itself don’t provide adequate evidence to prove the causal role of HCMV in CRC, further explanation is needed to justify increased risk of CRC due to HCMV infection. In addition, in light of multiple etiological factors responsible for causation of CRC (e.g. insufficient activity, high-fat diets, smoking and living in a developed country)2, the increased risk associated with HCMV in relation to these risk factors also need to be ascertained and established.

There are diagnostic challenges in detecting HCMV, due to “hit and run” mechanism of virus, though author considered PCR technique that showed higher positive rate than In situ hybridization (ISH) and immunohistochemistry (IHC),3 the validity of “negative results” of studies considered for meta-analysis should be ascertained before drawing any conclusion based on these results.

On 2016 Nov 18, Daniel Corcos commented:

Welch et al. assume wrongly that breast cancer incidence has been stable after the advent of screening mammography, although they find 132 more cases of BC a year per 100,000 women in the period following screening implementation. To justify this strange assumption they argue: “Those who postulate such substantial increases in underlying incidence, however, must explain why the increase coincides temporally with the introduction of screening, and why the incidence of the most aggressive form of the disease — metastatic breast cancer — remains essentially unchanged”. It is perfectly possible to explain both observations by the fact that early treatment is protective against metastasis, as expected and known for a century, and by the fact that mammography screening induces breast cancer at a much higher rate and with a shorter delay than usually expected.

References

Bleicher RJ, Ruth K, Sigurdson ER, et al. Time to Surgery and Breast Cancer Survival in the United States. JAMA Oncol 2016;2:330-9. Mathews FS. The Ten-Year Survivors of Radical Mastectomy. Ann Surg 1933;98:635-43.

On 2017 Mar 20, Miguel Lopez-Lazaro commented:

Pancreatic cancer formation is gradual

It is widely accepted that cancer development requires the sequential accumulation of DNA changes over years or decades. In this article, Notta et al. challenge this dogma. They analysed the genomes of more than 100 pancreatic tumours and found that many DNA changes occur simultaneously as a consequence of massive genomic rearrangements associated with catastrophic mitotic events. The authors discuss that the formation of advanced pancreatic cancers is not gradual, and propose a new model in which the simultaneous accumulation of genetic alterations arising from mitotic errors rapidly leads to the development of invasive disease. However, cancer incidence data by age indicate that the time frame required for the formation of invasive pancreatic cancers is similar from other cancers in which these mitotic errors are rare, thereby indicating that the high frequency of catastrophic mitotic events in pancreatic tumours may be a consequence of the disease rather than a cause. In addition, the extremely low rates of pancreatic cancer in young people and the striking increase in its incidence with age strongly suggests that the formation of most invasive pancreatic cancers requires the gradual accumulation of DNA changes over several decades. This means that there is time and opportunity to detect and stop pancreatic carcinogenesis before the development of advanced disease.

Full text at http://dx.doi.org/10.13140/RG.2.2.16865.92009

On 2017 Sep 28, William Davies commented:

Further evidence for a link between STS activity and Ctgf/Ccn2 expression has recently been obtained from in vivo and in vitro models of colorectal cancer (Gilligan et al., Estrogen Activation by Steroid Sulfatase increases Colorectal Cancer proliferation via GPER J Clin Endocrinol Metab. 2017 doi: 10.1210/jc.2016-3716)

On 2017 Aug 08, Christopher Tench commented:

Could you possibly provide the coordinates analysed otherwise it is difficult to interpret the results.

On 2017 Jul 02, Suzy Chapman commented:

ICD-11 Beta draft: Rationale for Proposal for Deletion of proposed new category: Bodily distress disorder

March 8, 2017

Full text:

http://wp.me/pKrrB-4dc

References:

1 Creed F, Guthrie E, Fink P, Henningsen P, Rief W, Sharpe M, White P. Is there a better term than “medically unexplained symptoms”? J Psychosom Res. 2010 Jan;68(1):5-8. doi:10.1016/j.jpsychores.2009.09.004. [PMID: 20004295]

2 Fink P, Schröder A. One single diagnosis, bodily distress syndrome, succeeded to capture 10 diagnostic categories of functional somatic syndromes and somatoform disorders. J Psychosom Res. 2010 May;68(5):415-26. [PMID: 20403500]

3 Creed F, Gureje O. Emerging themes in the revision of the classification of somatoform disorders. Int Rev Psychiatry. 2012 Dec;24(6):556-67. doi: 10.3109/09540261.2012.741063. [PMID: 23244611]

4 Gureje O, Reed GM. Bodily distress disorder in ICD-11: problems and prospects. World Psychiatry. 2016 Oct;15(3):291-292. doi: 10.1002/wps.20353. [PMID: 27717252]

5 American Psychiatric Association. (2013). Somatic Symptom and Related Disorders. In Diagnostic and statistical manual of mental disorders (5th ed.). Washington, DC: Author.

6 Frances A, Chapman S. DSM-5 somatic symptom disorder mislabels medical illness as mental disorder. Aust N Z J Psychiatry. 2013 May;47(5):483-4. [PMID: 23653063]

7 Lam TP, Goldberg DP, Dowell AC, Fortes S, Mbatia JK, Minhas FA, Klinkman MS. Proposed new diagnoses of anxious depression and bodily stress syndrome in ICD-11-PHC: an international focus group study. Fam Pract. 2013 Feb;30(1):76-87. doi: 10.1093/fampra/cms037. Epub 2012 Jul 28. [PMID: 22843638]

8 Ivbijaro G, Goldberg D. Bodily distress syndrome (BDS): the evolution from medically unexplained symptoms (MUS). Ment Health Fam Med. 2013 Jun;10(2):63-4. [PMID: 24427171]

9 Goldberg DP, Reed GM, Robles R, Bobes J, Iglesias C, Fortes S, de Jesus Mari J, Lam TP, Minhas F, Razzaque B et al. Multiple somatic symptoms in primary care: A field study for ICD-11 PHC, WHO’s revised classification of mental disorders in primary care settings. J Psychosom Res. 2016 Dec;91:48-54. doi:10.1016/j.jpsychores.2016.10.002. Epub 2016 Oct 4. [PMID: 27894462]

10 Medically Unexplained Symptoms, Somatisation and Bodily Distress: Developing Better Clinical Services, Francis Creed, Peter Henningsen, Per Fink (Eds), Cambridge University Press, 2011.

11 Frances Creed and Per Fink. Presentations, Research Clinic for Functional Disorders Symposium, Aarhus University Hospital, May 15, 2014.

12 Rief W, Isaac M. The future of somatoform disorders: somatic symptom disorder, bodily distress disorder or functional syndromes? Curr Opin Psychiatry September 2014 – Volume 27 – Issue 5 – p315–319. [PMID: 25023885]

13 Chalder, T. An introduction to “medically unexplained” persistent physical symptoms. Presentation, Department of Psychological Medicine, King’s Health Partners, 2014. [Accessed 27 February 2017]

14 Schumacher S, Rief W, Klaus K, Brähler E, Mewes R. Medium- and long-term prognostic validity of competing classification proposals for the former somatoform disorders. Psychol Med. 2017 Feb 9:1-14. doi: 10.1017/S0033291717000149. [PMID: 28179046]

15 Fink P, Toft T, Hansen MS, Ornbol E, Olesen F. Symptoms and syndromes of bodily distress: an exploratory study of 978 internal medical, neurological, and primary care patients. Psychosom Med. 2007 Jan;69(1):30-9. [PMID: 17244846]

16 Carroll L. Alice’s Adventures in Wonderland. 1885. Macmillan.

On 2016 Dec 17, Suzy Chapman commented:

Firstly, it is to be welcomed that authors Gureje and Reed have published this progress report on the work of the ICD-11 Somatic Distress and Dissociative Disorders Working Group (S3DWG) in an open access journal. The revision of ICD cannot be described as a "transparent and inclusive" process when ICD revision Topic Advisory Groups and sub working groups publish progress reports and rationales for their proposals behind paywalls.

I note the paper discusses the S3DWG's rationale for not including the word "somatic" in the name it proposes for its prototype disorder.

There is, however, no discussion within the paper of the sub working group's rationale for proposing to use the disorder term "Bodily distress disorder (BDD)" when this term is already being used interchangeably in the literature [1-4] with "Bodily distress syndrome (BDS)" - a divergent construct and criteria set already operationalized in Denmark, in clinical and research settings [5].

Omission of consideration within this paper of the potential impact for maintaining construct integrity within and beyond ICD-11 is troubling.

The S3DWG's "Bodily distress disorder" construct, as defined for the ICD-11 core version, has strong conceptual congruency and characterization alignment with DSM-5's "Somatic symptom disorder (SSD)" and poor conceptual and characterization alignment with Fink et al (2010) "Bodily distress syndrome."

It is noted that "Somatic symptom disorder" is also inserted into the ICD-11 Beta draft under Synonyms for BDD.

In sum:

ICD-11's proposed BDD is more closely aligned with DSM-5's SSD (Gureje and Reed, 2016).

The term "BDD" is already used interchangeably in the field for the operationalized "BDS" disorder construct [1-4].

That DSM-5's SSD and Fink et al's (2010) BDS are "very different" concepts, with different criteria sets, capturing different patient populations has been acknowledged by SSD work group chair, Joel E Dimsdale, and by Per Fink, Peter Henningsen and Francis Creed [6][7].

The unsoundness of introducing into ICD a new disorder category that proposes to use terminology that is already closely associated with a different (and already operationalized) construct/criteria set and the potential for conflation between the two has yet to be acknowledged or addressed by the sub working group responsible for this recommendation.

The S3DWG's choice of nomenclature needs referral back to the ICD-11 Revision Steering Group (RSG) and Joint Task Force (JTF) for urgent consideration of the implications of this proposed name for disorder integrity.

References:

1 An introduction to "medically unexplained" persistent physical symptoms, Presentation, Professor Trudie Chalder, Department of Psychological Medicine, King’s Health Partners, 2014, Slide #3 http://www.kcl.ac.uk/ioppn/depts/pm/research/imparts/Quick-links/Seminar-Slides/Seminar-7/Trudie-Chalder-intro.pdf

2 Rief W, Isaac M. The future of somatoform disorders: somatic symptom disorder, bodily distress disorder or functional syndromes? Curr Opin Psychiatry September 2014 - Volume 27 - Issue 5 - p 315–319 Rief W, 2014

3 Ivbijaro G, Goldberg D. Bodily distress syndrome (BDS): the evolution from medically unexplained symptoms (MUS). Ment Health Fam Med. 2013 Jun;10(2):63-4. Ivbijaro G, 2013

4 Fink P, Toft T, Hansen MS, Ornbol E, Olesen F. Symptoms and syndromes of bodily distress: an exploratory study of 978 internal medical, neurological, and primary care patients. Psychosom Med. 2007 Jan;69(1):30-9. Fink P, 2007

5 Fink P, Schröder A. One single diagnosis, bodily distress syndrome, succeeded to capture 10 diagnostic categories of functional somatic syndromes and somatoform disorders. J Psychosom Res. 2010 May;68(5):415-26. Fink P, 2010

6 Medically Unexplained Symptoms, Somatisation and Bodily Distress: Developing Better Clinical Services, Francis Creed, Peter Henningsen, Per Fink (Eds), Cambridge University Press, 2011

7 Francis Creed and Per Fink. Presentations, Research Clinic for Functional Disorders Symposium, Aarhus University Hospital, May 15, 2014.

On 2016 Dec 20, David Keller commented:

Effects of Rheumatoid Arthritis, and Its Treatments, on Parkinson Disease

Sung and colleagues derived two new and independent hypotheses from this study. First, they observed an inverse association between rheumatoid arthritis (RA) and the risk of subsequent development of Parkinson disease (PD), consistent with the hypothesis that RA is protective against PD. Second, they observed that the risk of developing PD was reduced even more in RA patients treated with biological disease-modifying anti-rheumatic drugs (DMARDs) but not in patients treated without DMARDs or with non-biological DMARDs. [1] This led to their second hypothesis, that "biologic DMARDs appear to further reduce the PD risk" in RA patients (more so than treatment with non-biological DMARDs, or no DMARDs), suggesting a possible "role of biologic DMARDs in PD treatment".

In summary, the authors of this study propose two new hypotheses to fully explain their results:

Hypothesis #1: Rheumatoid Arthritis disease is protective against the onset of Parkinson disease.

Hypothesis #2: Biological DMARDS, as treatment for RA, confer additional protection against the onset of PD.

Sung and colleagues point out that Hypothesis #1 contradicts "the hypothesis that chronic inflammation in RA may increase the risk of developing PD", citing a recent study that, similarly, "identified an inverse association between PD and systemic lupus erythematosus", and another confirmatory study that "reported a 30% reduction in the risk of developing PD in patients with RA and systemic involvement" [Sung's references #23 and #24]. Sung mentioned that RA patients are more likely to take NSAIDs, and that certain NSAIDs have been found to be protective against PD, but Sung claims that the association of RA with protection from PD withstood controlling for NSAID use. In a separate comment, I will address the errors I believe Sung and colleagues made when correcting their data for NSAID use, and how those errors could falsely support Hypothesis #1, above.

However, suppose hypothesis #1 is true. The association of additional reduction of PD incidence with the use of biological DMARDs might not be due to their having an intrinsic neuroprotective effect, but, rather, to the fact that they are reserved for use in the most severe or refractory cases of RA. The increased level of RA disease activity and severity associated with the use of biological DMARDs could be the cause for the additional decreased risk of PD. The inability to distinguish whether the additional protection from PD was due to neuroprotective benefits of biological DMARDs, or due to the more severe RA which caused biological DMARDs to be "indicated" (medically needed), is an example of confounding by "indication bias".

Biological DMARDs have likely been compared with non-biological DMARDs in randomized clinical trials for treatment of rheumatoid arthritis. If these trials included data on the new onset of PD, they could be pooled in a meta-analysis to test Sung's Hypothesis #2.

Reference

Sung YF, Liu FC, Lin CC, et al. Reduced risk of Parkinson disease in patients with rheumatoid arthritis: A nationwide population-based study. Mayo Clin Proc. 2016;91(10):1346-1353.

On 2016 Dec 19, David Keller commented:

Only ibuprofen is associated with reduced PD risk - controlling for use of any NSAID introduces error

The following quote is from the above paper by Sung [1]:

"Previous studies [2,3] have reported that nonaspirin NSAIDs, particularly ibuprofen, may be associated with a reduced risk of developing Parkinson disease. However, after controlling for all comorbidities and NSAID use, patients with RA still exhibited a reduced risk of PD compared with patients without RA..." However, the two cited studies actually demonstrated that ibuprofen use is associated with significantly reduced risk of PD, but other commonly-used NSAIDs are not.

In a landmark paper published in 2011, but not cited by Sung, Gao and colleagues published a new observational trial, plus a comprehensive meta-analysis, which concluded that ibuprofen, but not other NSAIDs, is associated with a significant 38% reduction in risk for PD [4]. Therefore, the bolded phrase should be corrected to read: "ibuprofen, but not other NSAIDs, is associated with a significantly reduced risk of developing PD".

Sung and colleagues controlled for NSAID use, but not separately for ibuprofen use; their Table 3 presents 11 baseline variables, and the adjusted HR of each. NSAIDs are discussed together as a single group, exhibiting a small but significant 9% protective effect against PD, the result of diluting the larger 38% protection associated with ibuprofen with the lack of significant protection associated with other NSAIDs.

Thus, the HR of 0.91 used by Sung to control for the use of any NSAID systematically under-corrects for the protection from PD for patients who took ibuprofen, while systematically over-correcting when the NSAID used was not ibuprofen. To eliminate these systematic errors, the study data should be reanalyzed, and corrected specifically for ibuprofen use, rather than for the use of any NSAID.

Until this correction is made, it is unclear how much of the apparent protection associated with RA disease or with biological DMARDs was actually attributable to the use of ibuprofen.

In an unpublished reply to these arguments, Sung's group wrote: "[Keller's] criticism focuses on the issue whether [any] non-aspirin NSAID or ibuprofen only, has the truly protective effect against the development of PD". Sung and colleagues agreed that "ibuprofen was associated with decreased risk of PD, but not aspirin or other NSAIDs" and concluded that "ibuprofen use should be considered as an important covariable in future correlational research in PD." [5]

References

1: Sung YF, Liu FC, Lin CC, Lee JT, Yang FC, Chou YC, Lin CL, Kao CH, Lo HY, Yang TY. Reduced Risk of Parkinson Disease in Patients With Rheumatoid Arthritis: A Nationwide Population-Based Study. Mayo Clin Proc. 2016 Oct;91(10):1346-1353. doi: 10.1016/j.mayocp.2016.06.023. PubMed PMID:27712633.

2: Chen, H., Jacobs, E., Schwarzschild, M.A. et al, Nonsteroidal antiinflammatory drug use and the risk for Parkinson's disease. Ann Neurol. 2005;58:963–967.

3: Rees, K., Stowe, R., Patel, S. et al, Non-steroidal anti-inflammatory drugs as disease-modifying agents for Parkinson's disease: evidence from observational studies. Cochrane Database Syst Rev. 2011;:CD008454.

4: Gao X, Chen H, Schwarzschild MA, Ascherio A. Use of ibuprofen and risk of Parkinson disease. Neurology. 2011 Mar 8;76(10):863-9. doi:10.1212/WNL.0b013e31820f2d79. PubMed PMID: 21368281; PubMed Central PMCID: PMC3059148.

5: Sung YF, Lin CL, Kao CH, and Yang TY. Reply to Keller's unpublished letter to Mayo Clinic Proceedings. Received by email on November 22, 2016.

On 2018 Feb 02, Christopher Korch commented:

In this article by Binder et al. in PLOS ONE (Binder NK, 2016), the authors describe using the cell line ECC-1 as a model of endometrial tissue to study embryo implantation. Using recombinant human placental growth factor they found that treatment of ECC-1 cells increased their cellular adhesion to fibronectin-coated tissue culture plates. I wish to point out two major concerns about the authenticity of this cell line.

First, two of the three references which they cite for having characterized ECC-1 are not correct. References 23 and 24 refer to the cell line HES, which my colleagues and I showed in 2012, was actually the HeLa subline WISH; i.e., a cervical adenocarcinoma cell line (Korch et al. Korch C, 2012) and our finding was confirmed in 2014 by the originator of HES (Kniss & Summerfield Kniss DA, 2014). WISH was shown by SM Gartler in 1966-1970 to be HeLa cells (Gartler SM, 1967, Gartler SM, 1968, Auersperg N, 1970) and this has been confirmed numerous times thereafter (e.g., by Nelson-Rees during the 1970s-1980s Nelson-Rees WA, 1980, Nelson-Rees WA, 1981; Lavappa at the ATCC in 1978 Lavappa KS, 1978; and Masters et al. 2001, Sandler AN, 1992). No authentic sample of WISH is known to exist (see cell register of misidentified cell lines at the websites of the International Cell Line Authentication Committee (http://ICLAC.org) and of the Expasy Cellosaurus (https://web.expasy.org/cellosaurus/). In fact, ECC-1 is also listed on both websites as a misidentified cell line.

Next, the authors do not indicate whether they genetically authenticated their sample of ECC-1 (currently the method of reference is by STR genotyping). This cell line was developed by PG Satyaswaroop's group at the Hershey Medical Center in about 1985-1987 when it was established from a xenograft sample of the endometrial tumor EnCa101, which was being maintained by passaging in mice. The cell line ECC-1 was deposited at the ATCC by Bruce Lessey, who had received it from Dr. Sayaswaroop. It was STR genotyped by the ATCC. As described earlier by me and my colleagues (Korch C, 2012) from STR genotyping of numerous samples of ECC-1 and Ishikawa cells from various sources and comparison to the STR profile of the ATCC sample of this cell line, ECC-1 was found to be one of three cell lines - a derivative of the endometrial cell line Ishikawa developed by Nishida (see Nishida M, 2002 for history and dissemination of various subclones), the breast cancer cell line MCF-7, or a mixture of Ishikawa and MCF-7 cells. A complicating fact is that ISHIKAWA / ECC-1 is an MSI unstable cell line, giving rise to variable STR genotypes.

In an attempt to determine the expected STR profile of ECC-1 / EnCa101, the Hershey Medical Center was approached, but no samples of the original tumor (paraffin block, etc) could be found as the Satyaswaroop lab had been closed in 2002. Furthermore, none of the ECC-1 or Ishikawa samples matched any of the four samples of xenografts of the tumor EnCa101. This tumor has been maintained by VC Jordan since about 1987 (Gottardis MM, 1988). The earliest tumor sample that I could obtain of EnCa101was from 1987 and was found to be genetically unrelated to the three tumor xenograft samples of EnCa101 from 2009 (unpublished data) and the STR genotypes of these four samples did match the profile of any known cell line in several databases.

Therefore, these results using the cell line ECC-1 should be re-interpreted and used cautiously, because (1) this cell line is a misidentified cell line as shown earlier (Korch C, 2012) and is listed as a misidentified cell line on the ICLAC and Cellosaurus websites; (2) the true identity of the sample of ECC-1 that was used by the authors was not determined, (3) its provenance is confusing because two of the three references for its characterization are incorrect and refer to a different cell line, and (4) since the genetic identity of ECC-1 cell line was not checked, it could be one of at least five different cultures (three variants of Ishikawa and breast cancer cell line and a mixture of Ishikawa and MCF-7).

Hopefully, this will encourage others to authenticate their cell lines. Below are some suggestions of how this problem could be avoided in the future. They are based on ideas put forth earlier by Dr. Amanda Capes-Davis in a PubMed Commons Comment on another article of concern (see Xu HT, 2016).

Authors & Reviewers could use the aforementioned resources since: • STR genotyping is effective for authentication of human cell lines and is the consensus method for comparison of human cell line samples (American Type Culture Collection Standards Development Organization Workgroup ASN-0002., 2010, American Type Culture Collection Standards Development Organization Workgroup ASN-0002., 2010); and

• Checklists for the identity of cell lines used in manuscripts and grant applications are readily available at http://iclac.org/resources/cell-line-checklist/ and through Expasy Cellosaurus https://web.expasy.org/cellosaurus/.

Journals, their Editors, and Funding Organizations could: • Implement more stringent criteria for publication and funding of research because encouragement of authentication testing, although a step forward, is insufficient to stop use of misidentified cell lines.

• Develop an authentication policy for sake of reproducible research. For example, to meet this need the NIH has recently implemented requirements for authentication of key resources as part of grant applications (e.g. see NOT-OD-16-011, NOT-OD-16-012).

• Require testing using an accepted method of cell line authentication, which is effective as illustrated by the cell line authentication policy of the International Journal of Cancer (Fusenig NE, 2017). If the authors, the reviewers, or the journal editors had followed PLOS ONE's own guidelines (http://journals.plos.org/plosone/s/submission-guidelines#loc-cell-lines) and checked for the identity of this cell line on either the ICLAC website (http://iclac.org/databases/cross-contaminations/) or on the Cellosaurus website (https://web.expasy.org/cellosaurus/), the journal would have detected and avoided this problem prior to publication.

• Regularly review the efficacy of their policy on cell authentication testing to see whether it is adequate (as was done by the International Journal of Cancer), especially in light of such examples having been published by a journal that requires authentication of cell lines prior to submission of manuscripts.

On 2016 Nov 15, IRWIN FEINBERG commented:

Kurth et al base their study on a faulty premise, stated in the first sentence: “Brain networks respond to sleep deprivation or restriction (emphasis added) with increased sleep depth which is quantified as slow-wave activity (SWA) in the sleep electroencephalogram (EEG)…” In fact, there is now abundant evidence that sleep restriction does not increase SWA in subsequent sleep, although it produces strong increases in behavioral sleepiness. We first observed this result in a 1991 study of the effects of acute termination of the last 3.5 h of sleep. This decrease in sleep duration did not produce the expected increase in either visually scored or computer measured SWA Feinberg I, 1991. This result so surprised us that we immediately repeated the experiment in a new group of subjects and obtained the same result Travis F, 1991. Since it was well established that a night of total sleep deprivation increases SWA, we sought to determine how much sleep restriction is needed to increase SWA. We limited sleep to 100 min and found that this restriction increased SWA. However, the increased SWA after 100 min of sleep differed from that after a night of total sleep deprivation (TSD). Whereas TSD reduced both the amplitude and incidence of slow waves, restriction to 100 min of sleep increased SW incidence but not amplitude Feinberg I, 1988. Subsequently, an extensive study by van Dongen et al showed that sleep restriction to 4 h/night for 14 consecutive nights does not increase SWA although it produces intense sleepiness and impaired vigilance Van Dongen HP, 2003. The previous studies were done with young adults so that it remained possible that the young subjects Kurth et al studied might have a different SWA response to sleep restriction. This is not the case. Sleep restriction in late childhood also failed to increase SWA Campbell IG, 2016. An incidental but reliable observation in our previous studies was that TSD Feinberg I, 1979 and partial sleep deprivation by restricted sleep (studies cited above) both reduce eye movement density in REM sleep. None of this previous work is cited or discussed by Kurth et al. The omissions are especially regrettable for two reasons. First, the failure of sleep restriction to increase SWA illustrates one of the major predictive failures of recovery models of SWA (both our own Feinberg I, 1974 and the similar two-process model Borbély AA, 2016). Second, the suppressive effects of sleep loss on eye movement density holds major biological implications for interrelations among sleep depth, NREM and REM sleep. Kurth et al's disregard of previous, highly relevant, well-substantiated data impedes research progress.

On 2017 Mar 26, Harri Hemila commented:

Antioxidants are not equal: an example of the apples and oranges problem

In their network meta-analysis on treatments for contrast medium–induced acute kidney injury (CIAKI), Su X, 2017 pooled different vitamins to a single group of “vitamins and analogues” but in doing so ignored the fact that vitamin C is water soluble, whereas vitamin E is fat soluble, and therefore their effects might be quite different. This is an example of the apples and oranges problem. With an analogous reasoning, studies on ciprofloxacin and penicillin might be pooled together on the basis that they are ”antibiotics”, though they have quite different mechanisms and indications.

Su X, 2017 calculated an odds ratio (OR) = 0.64 for the effect of “vitamins and analogues” but they did not calculate the specific effects of vitamins E and C. From 9 vitamin C trials, Sadat U, 2013 concluded that vitamin C may prevent CIAKI and calculated a risk ratio (RR) = 0.67. From 3 vitamin E trials, Rezaei Y, 2017 calculated that vitamin E decreased the incidence of CIAKI with RR = 0.38 (95%CI 0.24-0.62). On the OR scale, the effect of vitamin E corresponds to OR = 0.34 (95%CI 0.20-0.58).

Thus, vitamin E seems to have a greater effect against CIAKI compared with vitamin C. These two different vitamins should therefore not be pooled into a single group of “vitamins and analogues”, but they should be analyzed separately. The point estimates also suggest that there is greater justification for further research on vitamin E than on vitamin C. Furthermore, vitamins E and C may also interact under some conditions; for example, Hemilä H, 2009 found that vitamin E decreased mortality of older males only when they had a high vitamin C intake, but vitamin E had no effect when vitamin C intake was low.

Finally, Su X, 2017 estimated the effect of “vitamins and analogues” on the OR scale. However, Altman DG, 1998 pointed out that OR should be avoided when events are common. In many CIAKI studies, the proportion of CIAKI cases has been so high that OR gives an exaggerated impression of treatment effect. Su et al. calculated that high-dose statin plus NAC decreased the risk of CIAKI by OR = 0.31 (95%CI 0.14-0.60) and they concluded that “high-dose statin plus NAC or high-dose statin alone were likely to be ranked the best or the second best for preventing CIAKI”. However, the upper 95%CI limit for the effect of vitamin E on the OR scale (0.58) is lower than the upper 95%CI limit for the effect of high-dose statin plus NAC (0.60). Thus, on the basis of trials published so far. there is no basis to consider that these treatments actually differ in efficacy. Furthermore, half of the patients of the three vitamin E trials were concomitantly administered statins, and thus the effect of vitamin E may be at least partly independent of the effects of statins.

Thus, had Su X, 2017 analyzed the vitamin E trials separately, they might have concluded that there is as strong evidence to further study vitamin E as to further study high-dose statin plus NAC.

On 2016 Nov 07, Cicely Saunders Institute Journal Club commented:

The Cicely Saunders Institute journal club discussed this paper on 02/11/2016

The need to address early rehabilitation in individuals with critical illness is an important clinical priority. In meeting the needs of this group, clinicians need to consider who is likely to benefit and to what extent, it is also important to address possible harms that may arise from intervention (see AVERT, Lancet 2015). This paper addresses an important area of practice in the surgical intensive care environment.

We valued our discussion of the paper, which generated a number of reflections on the methods used and the wider applicability of the intervention. In rehabilitation and palliative care practice goal-setting to target intervention is a widely used approach. In many rehabilitation contexts, goals are set in conjunction with patients, carers and the clinical team. The method used here was somewhat different and used the Surgical ICU Optimal Mobilisation Score (SOMS), which in our view was more akin to a protocol. Nevertheless, the SOMS facilitated targeting of intervention at the individual ability level and seemed appropriate to the ICU environment, where patient engagement in goal setting maybe less practical in some instances.

We noted that a significant difference was identified in delirium-free days in favour of the experimental group and that the mean difference was three days. We wondered if the authors had any further comment on this and its relationship to the beneficial outcomes identified, particularly reduced length of stay. We reflected that reduced delirium may result in improved ability to engage in rehabilitation practice.

Commentary by Lucy Fettes & Stephen Ashford

On 2016 Oct 14, Attila Csordas commented:

Authors finish the article with the following suggestion: "To further extend human lifespan beyond the limits set by these longevity-assurance systems would require interventions beyond improving health span, some of which are currently under investigation (15). Although there is no scientific reason why such efforts could not be successful, the possibility is essentially constrained by the myriad of genetic variants that collectively determine species-specific lifespan(16).”

Authors seem to be suggesting that all the current experimental efforts won’t be enough to increase maximum life span over a biological limit that has been reached already and overcome or counterbalance the collective effect of those “myriad of genetic variants”.

In fact, there is at least one experimental result that leaves this question definitely open, and this is the scenario of clearing-out senescent cells from the aging organism, see mouse studies summarised in http://www.nature.com/nature/journal/v530/n7589/full/nature16932.html where concerning maximum lifespan the results are mixed: “Maximum lifespan was significantly increased for mixed AP-treated males and females combined (P = 0.0295), but not for females and males individually. Maximum lifespan was not extended for C57BL/6 AP-treated animals, either combined or separately”. Indeed this is the approach taken by biotech companies like http://unitybiotechnology.com/ amongst others.

I wonder how a study, also appearing in Nature, earlier this year, February, 2016, have slipped through the attention of the authors?

The assumption of my argument is that mouse studies can be relevant for human trials. After all mice have those “myriad of genetic variants” too.

On 2016 Oct 26, Marco Lotti commented:

Please, see Video Comment at: https://youtu.be/ZJnt8wIvK14

You can find further resources at:

http://www.slideshare.net/MarcoLotti3/lotti-marco-md-the-laparoscopyenhanced-hipec-concept

http://online.liebertpub.com/doi/abs/10.1089/vor.2015.0315?journalCode=vor

http://www.journalofmas.com/article.asp?issn=0972-9941;year=2016;volume=12;issue=1;spage=86;epage=89;aulast=Lotti

On 2016 Oct 22, Marco Lotti commented:

Please, see Video Comment at:

https://youtu.be/ZJnt8wIvK14

On 2016 Oct 08, Marco Lotti commented:

Please find further resources at:

http://www.slideshare.net/MarcoLotti3/lotti-marco-md-the-laparoscopyenhanced-hipec-concept

http://online.liebertpub.com/doi/abs/10.1089/vor.2015.0315?journalCode=vor

http://www.journalofmas.com/article.asp?issn=0972-9941;year=2016;volume=12;issue=1;spage=86;epage=89;aulast=Lotti

On 2016 Oct 09, Preben Berthelsen commented:

Levosimendan for the Prevention of Acute Organ Dysfunction in Sepsis

Gordon AC et al. NEJM 2016.

A foregone conclusion - Levosimendan does not prevent organ dysfunction in sepsis – that is if you wait long enough before administering it.

Successful treatment of sepsis is based on the prompt administration of antibiotics, volume repletion and perhaps surgery.

In this RCT, levosimendan was first deployed a median of 15-16 hours after a diagnosis of sepsis had been established. In my opinion, it is very unlikely that any intervention - delayed for so many hours - will substantially influence the course of sepsis in a meaningful way.

This trial does not inform on a well-timed use of levosimendan in sepsis. Historically, however, single interventions in sepsis have a poor track record.

P.G.Berthelsen MD, MIA, DCAH. Charlottenlund, Denmark

On 2016 Oct 28, Lydia Maniatis commented:

It seems that the model being proposed by Snow et al (2016) was obsolete even as it was being constructed, as they decided to base it on assumptions of V1 neuron behavior known for decades to be invalid.

The choice is signaled by the authors when they explain in their introduction that they are going to “address primary visual cortex (V1) as a paradigmatic example and focus on orientation adaptation phenomena that are within the classical receptive field (RF).”

I think this is the only mention made in the article of the term “classical receptive field,” but it bears some elaboration.

The obsoleteness of the concept is reflected in quotes from two articles, below.

1. Graham (2011) "Beyond multiple pattern analyzers modeled as linear filters (as classical V1 simple cells): Useful additions of the last 25 years" Vision Research.

“The classical receptive field of a V1 simple cell is very small relative to the distances over which visual perception has to function. Indeed, the classical receptive field is typically composed of only a few inhibitory and excitatory sub-sections…

…non-classical responses of V1 simple cells can occur over a substantially larger area than the classical receptive field. This is one reason that non-classical receptive fields are now frequently invoked in explanations for perceptual phenomena.

…these non-classical receptive fields have been invoked to account for a number of psychophysical phenomena as well

…In addition to the possibility of non-classical suppressive (or facilitatory) effects from outside the classical receptive field, there is also a possibility that the same non-classical effects extend inside the classical receptive field. And perhaps there are different non-classical processes that exist inside the classical receptive field but not outside.”

1. Angelucci and Bullier (2003) Reaching beyond the classical receptive field of V1 neurons: horizontal or feedback axons? (Journal of Physiology)

“It is commonly assumed that the orientation-selective surround field of neurons in primary visual cortex (V1) is due to interactions provided solely by intrinsic long-range horizontal connections. We…conclude that horizontal connections are too slow and cover too little visual field to subserve all the functions of suppressive surrounds of V1 neurons in the macaque monkey. We show that the extent of visual space covered by horizontal connections corresponds to the region of low contrast summation of the receptive field center mechanism. This region encompasses the classically defined receptive field center and the proximal surround. Beyond this region, feedback connections are the most likely substrate for surround suppression. We present evidence that inactivation of higher order areas leads to a major decrease in the strength of the suppressive surround of neurons in lower order areas, supporting the hypothesis that feedback connections play a major role in center–surround interactions.”

Naturally, as the authors point out, the model can’t explain many things (that it should be able to explain):

“We have shown that our modeling approach can explain some classical adaptation effects as well as a more recent phenomenon of equalization. However, clearly the approach has limitations and does not capture the full set of phenomena for adaptation.

First, the model in its present form does not include surround influences and cannot capture disinhibition of the surround nor interesting data on facilitation and attractive shifts of tuning curves (Solomon & Kohn, 2014; Webb et al., 2005; Wissig & Kohn, 2012). It would be interesting to consider extensions of the model to capture both spatial (Coen-Cagli et al., 2012) and temporal contextual influences.”

The model is extremely involved. Evaluating it would require a significant amount of effort and time on the part of colleaugues. SInce even its authors already know its assumptions are false (i.e. lead to false predictions), why should anyone bother? Adding to a failed, ad hoc model is usually not the best way to achieve a better one.

The publication of models based on invalid assumptions and empirically falsified a priori is common in the vision literature and reflects a culture in which piecemeal, ad hoc (with respect to facts and techniques) and logically and/or empirically false models are treated as equivalent to coherent and insightful hypotheses that first have to be tested before we know they’ve failed.

The former, being much easier than the latter, overwhelmingly dominates the literature, which has become almost entirely self-referential and unprogressive, because its “theorists” don’t allow themselves to be challenged by inconvenient facts, but rather are satisfied with avoiding them, occupying themselves instead with mathematical prestidigitations.

On date unavailable, commented:

None

On 2017 Apr 24, David Eidelberg commented:

We thank Dr. Black for his interest in our work. To answer his basic questions:

1. The minimum time interval between the start of the levodopa infusion and the start of O15- water PET was 60 minutes. As detailed in the original paper by Feigin et al. (Neurology 2001; 57(11): 2083-2088), scanning under infusion commences when UPDRS motor ratings are stable within 5% on two successive examinations separated by 30 minutes. The levodopa infusion is maintained at a constant rate at that point.

2. As for the test-retest cohort, these PD subjects took their usual morning PD medications on the day of the scan. They maintained their routine antiparkinsonian medication regimen during the 8-week interval between test and retest imaging. The majority of the subjects were on daily carbidopa/levodopa, though a minority took a combination of levodopa and a dopamine agonist. Unfortunately, the exact dosing at the time of the scans (performed before 2008) is not currently available.

3. All levodopa infusion subjects reported in the study were scanned according to the protocol described in Hirano et al., J Neurosci 2008; 28(16): 4201-4209.

On 2017 Apr 08, KEVIN BLACK commented:

Authors: Please tell us the time interval between the start of the levodopa infusions and the start of the [O-15]water and FDG PET acquisitions.

Also, can you provide information about carbidopa intake on the day of the study? The supplement says only that the 8 PD test-retest subjects took "their usual morning dose of oral levodopa/carbidopa." The dose of LD and of CD is not given for those subjects. The reader is referred to the Ma et al 2007 paper, but I can't find dosing information there, either (nor how many were on CD-LD only and how many were on other dopaminergic drugs). In the infusion subjects, can you clarify whether the newly enrolled subjects had the same carbidopa dosing regimen as described in the Hirano et al 2008 methods?

On 2016 Nov 17, Jean-Jacques Letesson commented:

In the starting paragraph of the discussion the authors wrote "Metabolism and usage of erythritol are regulated by the 7.7‑kb ery operon, which consists of four genes eryA, eryB, eryC and eryD (EryA, 519 AA; EryB, 502 AA; EryC, 309 AA and EryD, 316 AA). The functions of these four proteases are similar to xylulose kinase (E. coli xylB), glycerol‑3‑phosphate"

I have two major comments on this sentences:

1- THESE GENES ARE NOT PROTEASES: ery is a kinase, eryB is a dehydrogenase, eryC an isomerase and eryD a transcriptional regulator

2- The EryABCD ery operon is by itself not sufficient to catabolize erythritol as published recently (Barbier T. et al., 2014.PNAS 111:17815–17820.) you need two more isomerase (eryH and eryI)

On 2016 Dec 14, Martine Crasnier-Mednansky commented:

In Escherichia coli, CRP stands for Cyclic AMP Receptor Protein, not Catabolite Repressor Protein. CRP is also known—and rightly so—as CAP, the Catabolite gene Activator Protein. Repression by CRP-cAMP, was recognized as early as 1972 (Prusiner S, 1972) and later on emphasized. See, for an elaborate discussion, Kolb A, 1993. In this context, the designation 'Cyclic AMP Receptor Protein' is fully appropriate and has been used preferentially over the more specific designation 'Catabolite gene Activator Protein'.

Glucose is not "a known inhibitor of CyaA activity". In fact, transport of glucose prevents activation of CyaA by a phosphorylated component of the phosphotransferase system (PTS). Such regulation is relevant to the present work because, upon glucose exhaustion (or some other PTS-transported carbon sources), there is a sharp increase in exogenous cAMP, which may act as a trigger at the onset of the infection.

On 2016 Oct 04, Peter Hajek commented:

It is misleading to classify people who tried one puff on an e-cigarette several weeks ago and never touched it again as 'current e-cigarette users'. The common practice of misreporting experimentation with e-cigarettes as 'current use' is responsible for the myth that large numbers of non-smokers are becoming daily vapers, when in fact this is extremely rare.

On 2016 Oct 05, Kausik Datta commented:

Congratulations to the authors for undertaking this informative study of physician behavior in Germany. I have a question about a study parameter: an important differentiating variable in this study seems to be the nature of the prescribed medication, pharmacological vs. phyto-pharmacological. However, the paper as published seems to lack any information on what medications the study considered pharmacological or phyto-pharmacological. It may likely be beyond the scope of this paper, but I'd like to have some information on the "phyto-pharmacological" medications prescribed by German GP-NPs evaluated in this study. May I request a pointer from the authors?

COI Disclosure: I have no direct competing interest with this study, the authors or their sponsors, but I am personally interested in science and ethnobotany, and am known to be skeptical of wide-ranging claims made by CAM practitioners.

On 2016 Oct 14, Daniel Jarosz commented:

None

On 2016 Oct 14, Daniel Jarosz commented:

We considered this possibility extensively, and expected to find it commonly. However, we did not observe a change protein levels for the hit proteins that we checked (see supplement), and it seems unlikely that such feedback mechanisms would be transmissible to other cells through protein alone, so strongly sensitive to transient inhibition, or stable over hundreds of generations, through freeze/thaw etc. Investigating whether any of the remaining phenotypic states that we did not test in this way could arise from feedback mechanisms like those described in this comment stands as a goalpost for our future studies.

On 2016 Oct 12, Peter Ellis commented:

The authors note that many of the proteins identified in this screen were transcription factors and RNA-binding proteins. The paper does not report whether they checked the mRNA and/or protein expression levels of the endogenous gene copy before and after transgenic overexpression of a given gene. In cases where a transcription factor promotes its own expression, or an RNA-binding protein promotes the translation of its own transcript, there is an obvious feedback mechanism through which the memory could be maintained.

On 2017 May 02, Lukasz Antoniewicz commented:

Thank you for your opinion.

On 2017 May 02, Zvi Herzig commented:

None of the sources with COIs relate to original data. The relevance of snus to this discussion is that it is the cleanest form of nicotine for which a wealth of long-term epidemiological evidence is available. As the American Heart Association notes Bhatnagar A, 2014:

Because most of the toxicity from cigarette smoking derives from combustion products, the health effects of smokeless tobacco could be examined to assess potential long-term adverse effects of nicotine without exposure to combustion products. Smokeless tobacco users take in as much nicotine as cigarette smokers, although not by the pulmonary route. The most extensive and rigorous epidemiological studies on smokeless tobacco use come from Scandinavia, where a large percentage of men use snus, a smokeless tobacco product that contains nicotine but relatively low levels of carcinogens and other toxins.

Rodu and Phillips do not present original data and their arguments cannot be refuted simply by citing their COIs. Similarly, Lee's meta-analyses do not present original data and are peer-reviewed. If any other meta-analysis refutes Lee's work, I'd be happy to cite it.

On 2017 May 02, Lukasz Antoniewicz commented:

As our study does not focus on snus I will not continue this debate. I do not question the scientific creditability of your citations, but I find it very interesting that among your citations some researchers clearly stated following:

Rodu B, 2015 Dr Rodu is supported by unrestricted grants from tobacco manufacturers to the University of Louisville, and by the Kentucky Research Challenge Trust Fund. Dr Phillips is partially supported by an unrestricted grant from British American Tobacco.

Lee PN, 2013 The author is a long-term consultant to the tobacco industry

Lee PN, 2009 PNL, founder of PN Lee Statistics and Computing Ltd., is an independent consultant in statistics and an advisor in the fields of epidemiology and toxicology to a number of tobacco, pharmaceutical and chemical companies.

On 2017 May 01, Zvi Herzig commented:

Snus users are exposed to equal or more more nicotine than smokers Holm H, 1992. If snus does not cause MI or stroke, this indicates that nicotine doesn't. This inference is also made by Benowitz and Burbank, as cited by Bates above.

The conclusions of the Arefalk study have been refuted by Rodu B, 2015 (see also Rodu's follow-up link).

The other possible effects of snus are unrelated to EPC mobilization. In any case, the link to type 2 diabetes is inconsistent, e.g. Rasouli B, 2017. Rodu shows that the correlation is consistent at >6 cans per week link, but "more research is needed to confirm a link" in his opinion, because of other known factors. With regards to cancers see Lee PN, 2009 Lee PN, 2013.

On 2017 Apr 28, Lukasz Antoniewicz commented:

I agree that we may speculate that nicotine mobilizes EPCs. But I do not agree on the last statement: I find it very interesting that Zvi Herzig cites studies on the tobacco product called snus and equates it to pure nicotine. As a tobacco product, Swedish snus is not comparable to pure nicotine. Swedish snus increases the risk for type 2 diabetes Carlsson S, 2017 and pancreatic cancer Luo J, 2007 and seems even to increase the risk for other types of cancer Zendehdel K, 2008, Song Z, 2010, Hirsch JM, 2012, Nordenvall C, 2013. As cited by Zvi Herzig, case fatality was increased among patients with myocardial infarction and stroke. This was confirmed by Arefalk, wo observed a 50% mortality reduction upon snus-cessation following myocardial infarction Arefalk G, 2014. In conclusion: Swedish snus is not the same thing as pure nicotine or a NRT. Swedish snus is probably not as safe as suggested by the comment of Zvi Herzig.

On 2017 Apr 25, Zvi Herzig commented:

Farsalinos and Polosa explicitly write:

Obviously, we are not implying that the elevation in EPCs that follows acute exposure to nicotine-containing e-cigarette reported by Antoniewicz et al. is beneficial to cardiovascular health.

Rather, they show that EPC mobilization doesn't indicate cardiovascular harm, as it occurs with activities known to be safe. Moreover, they note:

acute effects on EPC number could be related to a documented direct effect of nicotine.

See also Heeschen C, 2006:

Administration of nicotine increased markers of EPC mobilization.

Thus, the findings of the Antoniewicz el al appear to be a function of nicotine. Decades of epidemiological evidence don't indicate that nicotine causes myocardial infarction Hansson J, 2012 or stroke Hansson J, 2014, even though there might be some detrimental effects, as noted above.

On 2017 Apr 24, Lukasz Antoniewicz commented:

A lot of reviews explain the function of endothelial progenitor cells (EPCs). I will repeat some key points from our letter (https://www.ncbi.nlm.nih.gov/pubmed/28159320): EPCs are cells that participate in vascular repair. The trigger that releases EPCs from a pool into the blood stream is hypoxia in the vascular wall. This fact is well described in plenty reviews and studies. We know that smoking a single cigarette causes a sudden mobilization of EPCs, but this effect is temporary and EPCs return to baseline values during 24 hours (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3938677/). If the vasculature is exposed to chronic stress (like in the case of daily cigarette smoking) this causes frequent releases of EPCs into the blood stream resulting in a diminished pool of EPCs. So, when analyzing EPCs in chronic smokers (but not directly after smoking a cigarette) the amount of EPCs is lower compared to non-smokers. Upon smoking cessation, this pool seems to be replenished and the amount of EPCs increases. The cited studies investigating the effects of red-wine consumption or Mediterranean diet investigate the pool of EPCs in a ”steady state” so the terms long-term and short-term are relative. We investigated the sudden effects (during hours) on EPC-release following smoking and e-cigarette inhalation.

Physical activity causes physiological stress on muscles and vessels resulting in physiological hypoxia causing a sudden mobilization of EPCs following only hours of physical activity. It is important to highlight that this mobilization is triggered as a physiological response following exercise. It is hard to argue that smoking a couple of cigarettes a day with several EPC releases has a beneficial effect on health.

Our study shows that e-cigarette inhalation has the potential to mobilize EPCs that are needed for vascular repair. We will see if daily mobilization diminishes the pool of EPCs. It remains to be shown if daily e-cigarette inhalation causes chronic changes to the vascular wall. Maybe in 20 or 30 years we will get a clear answer if the number of e-cigarette users continues to increase and epidemiological studies on myocardial infarction and stroke will give us more information. Until then, there will be a lively debate.

On 2016 Dec 23, Clive Bates commented:

In their published reply to this article, Endothelial progenitor cell release is usually considered a beneficial effect: Problems in interpreting the acute effects of e-cigarette use, Farsalinos and Polosa point out that the measured increase in endothelial progenitor cells (EPCs) is usually associated with beneficial effects, and not necessarily a cause for the concerns expressed by the authors.

Farsalinos and Polosa point out that several problem conditions are associated with lower EPC levels:

However, the increase in EPC levels is largely interpreted in the scientific literature as a beneficial effect while a reduction is interpreted as an adverse prognostic marker. Several risk factors for cardiovascular disease, such as ageing, hyperlipidemia, hypertension, obesity and diabetes, are associated with reduced levels and functional impairment of EPCs. Similar associations were found with “non-classic” risk factors such as high C-reactive protein and homocysteine, and low vitamin D levels. Smokers have lower levels of EPCs compared to nonsmokers.

They also point out that many positive conditions are associated with higher EPCs:

Various short-term or acute interventions are associated with elevated EPCs. Consumption of red wine, switching to Mediterranean diet and acute exercise are associated with elevated number of circulating EPCs in healthy subjects. EPCs increase shortly after smoking cessation (especially in light smokers), with nicotine patch users having slightly higher (but not statistically significant) elevation in EPCs after smoking cessation compared to non-users. Short-term administration of green tea also caused an increase in EPCs in young healthy smokers. In all the above-mentioned interventions, the increase in EPCs was interpreted as a beneficial effect and there was no suggestion that it was a response to vascular injury caused by the intervention

This is merely the latest of several recent analyses in which observations of acute effects of nicotine have been uncritically assumed to be a marker for a chronic cardiovascular disease risk (see Vlachopoulos C, 2016 and Carnevale R, 2016 for example), generating alarming news headlines as a result (see: E-cigs: The incendiary truth... Just 10 puffs increases your risk of heart disease, Daily Mail, 3 Dec 2016).

Please see Benowitz NL, 2016 for a more credible and complete account of the cardiovascular effects of nicotine as they relate to e-cigarettes. Benowitz and Burbank review the relevant evidence and summarise the current state of knowledge as follows:

The cardiovascular safety of nicotine is an important question in the current debate on the benefits vs. risks of electronic cigarettes and related public health policy. Nicotine exerts pharmacologic effects that could contribute to acute cardiovascular events and accelerated atherogenesis experienced by cigarette smokers. Studies of nicotine medications and smokeless tobacco indicate that the risks of nicotine without tobacco combustion products (cigarette smoke) are low compared to cigarette smoking, but are still of concern in people with cardiovascular disease. Electronic cigarettes deliver nicotine without combustion of tobacco and appear to pose low-cardiovascular risk, at least with short-term use, in healthy users.

The absence of serious disease risk when nicotine is consumed through NRT or smokeless tobacco (i.e. without the products of combustion of tobacco leaf) should be a basis for reassuring and encouraging smokers considering switching to vaping.

I hope the authors and journal will take care that any misunderstandings generated by their work and the media attention that followed will be corrected and placed in context. The problem is that alarming but baseless statements about vaping risks can easily have the unintended effect of encouraging continued smoking and cause harm as a result.

On 2017 Aug 11, Jos Verbeek commented:

Unfortunately this review did not adhere to the following important Cochrane standards. There was no published protocol. The authors combined the results of all RCTs and case series thus artificially inflating the effect. They pooled very different interventions in one pooled result even though heterogeneity was as high as 82%. I believe that calculating one pooled effect size for interventions as different as meditation, communication skills or improved work schedules does not make sense. It is also not a methodological standard to loosely asses the quality of the evidence as moderate without further justification and not using the GRADE approach. Another interesting item is the claim that the results are ‘clinical meaningful reductions’. It is not clear what the authors refer to. With patient-reported outcomes one would be interested in a minimally clinically relevant difference or reductions that patients perceive as an improvement. However, for the Maslach Burnout Inventory this difference has never been established as far as we know. Thus we don’t know what the clinical meaning of reductions on this scale is. It is very well conceivable that these will not be perceived as improvements by an individual health care worker. West et al neither discuss the problem of small studies that is apparent with the average number of participants being less than 50 in the 15 included trials but again loosely refer to the funnel plot as not indicating publication bias. I believe that physicians are put on the wrong foot with the conclusion of moderate quality evidence of clinically meaningful reductions in burnout based on this review.

On 2016 Oct 08, Lydia Maniatis commented:

The authors state in their conclusion that: "At each contrast level, the perceived depth first increases with the magnitude of disparity modulation up to a critical value and then decreases gradually with further increases in the magnitude of disparity modulation. "

As is well-known, perceived depth is largely mediated by stimulus structure. The stimuli used by Chen et al (2016) have a structure that produces a 3D impression. Do the "single-cycle" and the "corrugated" version, produce the same depth impression as forms, i.e. viewed in outline monocularly? Then this would have to be factored in to the conclusions re disparity and contrast. Are the authors claiming that they have controlled for this factor? Then this should be stated. Otherwise, their results can only be said to hold for the particular stimuli they employed, not for perception in general. In this case, there are no principles being investigated here; results have no predictive value; conclusions are purely ad hoc.

The most simple illustration of the role of luminance structure (combined with the principles instantiated in the visual system) is what happens in a completely homogeneous visual field, produced by a flat surface - in other words one with zero contrast. In this case, we perceive a cloudy, 3D space. A small luminance variation on that surface collapses the perceived fog into a flat perceived surface. If we converted that surface into an image that we would refer to as trompe l'oeil, we would have a very strong impression of 3D structure with zero disparity. At low contrast or high contrast, if structure implies depth, we'll see depth, and vice versa.

With respect to subjects, as is very common in psychophysics, there were very few - three here - and one of them was an author. The participation of authors is odd especially in light of the fact that we're told explicitly that the other two subjects were "naive to the purpose of this study." If it's important that the observers be naive, then why is an author a subject; if it's not important, why mention it?

Other points: The authors fail to consider the distinction between luminance contrast and perceived constrast. They modulate the luminance contrast between dots and background. We know that small elements, like thin lines, tend to produce assimilation with the background, i.e. lowers perceived contrast.

The authors mention that previous studies, using various stimuli and conditions, have produced inconsistent results. Despite these studies, we're told, " it is still difficult to infer the effect of luminance contrast effect on perceived depth in a scene..." So the question the authors seem to be asking is "what is the effect of luminance contrast on perceived depth in a scene?" But if the previous studies show anything, it is that conditions matter; so a search for "the effect" seems inappropriate. Picking a set of conditions out of a hat and testing them will only tell us about the luminance contrast effect under those conditions. The set of possible conditions is infinite.

The authors state in their intro that "as shown in signal detection theory (Green & Swets, 1966; Chen & Tyler, 2001), the threshold measurement constituting stereoacuity depends not only on the intensity of the stimulus but also on the internal noise." Neither Green & Swets (1966) nor Chen & Tyler (2001) have shown that there is noise in the visual system, and they have certainly not shown that this putative internal noise is maintained and expressed in the percept. The "internal noise" claim has generated no evidence (it is not even clear what this evidence would look like, including in terms of physiological measurements), and is maintained on the basis of studies employing a narrow set of conditions generating crude datasets whose results are highly overinterpreted.

On 2016 Oct 11, Lydia Maniatis commented:

The authors of this study transparently misrepresent and/or misunderstand the theoretical situation with respect to the subject they are investigating.

The most blatant oversight involves the failure to note that the type of stimulus being used here – a checkerboard configuration – was shown decades ago to reduce, rather than enhance, perceived contrast in adjacent surfaces. (see demo here: https://www.researchgate.net/figure/225158523_fig5_Fig-5-The-DeValois-and-DeValois-Checkerboard-stimulus). This fact was similarly overlooked by Maertens, Wichmann and Shapely (2015), who simply assumed the opposite.

The checkerboard configuration is clearly a special and unusual case, in the sense that it elicits the impression of adjacent surfaces rather the more typical experience of surfaces on top of backgrounds (figure/ground relationships). It is unlikely that an ad hoc model that involves averaging of luminances would work for both the former and the latter sets of conditions.*

The Missing Segmentation Step