6,062 Matching Annotations
  1. May 2026
    1. On 2026-03-27 14:32:15, user Peter Ellis wrote:

      If you can validate this finding by some other method then this would be a truly remarkable finding. The Y chromosome contains numerous genes that are essential for spermatogenesis - it should not be possible for any cell lineage lacking the Y to give rise to mature sperm. The only possible point at which the Y could be lost (or Y-bearing cells could be lost) would be post-meiotic.

      Is it possible instead that there is some alteration in chromatin packaging which somehow selectively affects the extraction efficiency of Y chromosomal DNA sequences?

      Alternatively, how exactly is the calculation of Y content being done? If this is an aggregate measurement from bulk DNA, is it possible that rather than there being cells that have fully lost the Y, there is a mix of cell lineages present, each of which has a range of different Y microdeletions present?

      Given the known essentiality of the Y for sperm production, I think you will find it challenging to get this past peer review without some kind of per-cell analysis, which could be FISH-based or single-cell genotyping. In either case you'd need very high throughput to have statistical power to detect 1% of cells with LOY

    1. On 2026-03-26 15:44:14, user Peter J. Wolf wrote:

      As both a researcher and community cat caregiver, I’m very pleased to see this work being conducted!

      I was rather surprised to see the relatively low instances of secondary traumatic stress (i.e., 47% moderate, 10% high) reported in this study. I imagine this is the result of using the thresholds proposed by Stamm (2010). You might consider repeating your analysis using the revised thresholds proposed by De La Rosa et al. (2018).

      Literature cited<br /> De La Rosa, G. M., Webb-Murphy, J. A., Fesperman, S. F., & Johnston, S. L. (2018). Professional quality of life normative benchmarks. Psychological Trauma: Theory, Research, Practice, and Policy, 10, 225–228. https://doi.org/10.1037/tra0000263

      Stamm, B. H. (2010). The Concise ProQOL Manual (2nd ed.). https://proqol.org/proqol-manual

    1. On 2026-01-12 13:02:28, user Ryan wrote:

      The plot in figure 2 is great. However, providing a supplemental with the actual HR of testing would be helpful for others to do a tipping point analysis of your results and confirm the testing effect is or is not strong enough to nullify your results. This would greatly enhance the reproducibility of your research.

    2. On 2026-01-12 13:11:20, user Ryan wrote:

      I recommend leaving an HR for testing positivity or adding the positivity rate as adjusted variable, this will allow testing level to be compared and not just testing timing on the results. From the look of it hin the log ratios over time, it does not look like it will completely wash out the signal, however, it is hard to tell with giving the actual values.

    3. On 2025-12-12 17:45:56, user Ceejay wrote:

      Line 293: "This study’s inability to find a protective influence of influenza..." I think what is meant is protective influence *of vaccination* on influenza

    1. On 2026-03-30 13:27:10, user Sverre wrote:

      This is a very cool article, thanks for sharing it! Currently planning a kinda similar analysis. I just want to point out that Norwegian middle school GPA is not a 10-year cumulative measure: it just contains grades from year 10 (and a few from year 9).

    1. On 2026-03-29 15:01:54, user Ian Buller wrote:

      Quick note that your citation of the abstract by Brown & Vo et al. (2022; DOI: 10.1158/1538-7755.DISP21-PO-192) is now published as a manuscript in JNCI by Vo & Brown et al. (2025; DOI: 10.1093/jnci/djaf066). I am a co-author on both.

    1. On 2026-03-25 06:58:19, user Eugenio Forbes wrote:

      As part of the methodology, did anyone diagnose equipment and cables, plot the recordings to verify that it's not mostly noise?

    1. On 2026-03-25 02:47:47, user Tin Pham wrote:

      This paper could have been ameliorated by specifying the target trial specifications (eg. eligibility criteria, treatment and outcome, follow-up, causal contrasts) and the emulated analogues, according to the TARGET guideline (Cashin et al, 2025). Also, I suggest some sensitivity analyses be done (e.g. varying the lag time, different model specifications for calculating the propensity score, ITT vs PP treatment estimates) to verify the robustness of the findings.<br /> _____<br /> References: <br /> Cashin AG, Hansford HJ, Hernán MA, et al. Transparent Reporting of Observational Studies Emulating a Target Trial—The TARGET Statement. JAMA. 2025;334(12):1084–1093. doi:10.1001/jama.2025.13350

    1. On 2026-03-23 19:50:14, user Neville Calleja wrote:

      There is clearly a number of confounders here and the way it is written and summarised in the abstract does not give enough credit to this. The link with pre-existing EBV infection, potential infection before the vaccine took full effect etc has not been well described. A number of subset analyses have been carried out, which may border on data dredging, rather than formal multivariate analyses. Also clearly the involvement of a major antivax advocate has meant that the study has been highjacked.

    1. On 2026-03-23 18:21:15, user Evolutionary Health Group wrote:

      We at the Evolutionary Health Group ( https://evoheal.github.io/ ) enjoyed this paper. Here are our highlights:

      The saliva-based work presented here shows convincing equivalence to blood tests across multiple pathogens, cohorts, and age groups. The attention to real-world validation shows that the method is platform-level, opening the possibility of applying this type of assay in other contexts.

      Because saliva samples are self-collected, non-invasive, and stable, the authors demonstrate that it's possible to capture the daily resolution of important pathogens, addressing a major limitation in epidemiology where true dynamics can't be captured due to infrequent sampling.

      Blood-based studies frequently under-sample children, older adults, rural populations, and low-resource populations. Saliva testing removes many of the cost, commute, and invasiveness barriers and helps create more representative datasets for use in epidemiological inference and public health policy.

      Professionally collected blood samples benefit from consistent quality. Self-collected samples are more likely to suffer a loss of quality due to improper collection techniques. We would be interested to know how sample quality compares when collections are truly independent of any professional guidance.

    1. On 2026-03-14 00:41:31, user Lisa DeTora wrote:

      What an interesting study! I'd be curious about your views on more open-ended questions users might ask of LLMs about specific vaccines. I also wonder if you have advice for public health agencies or healthcare providers on using LLMs in this setting

      One small point: the vaccine hesitancy reference is pre-COVID. My understanding is that this problem has been somewhat worse since the pandemic, making the problems you seek to address even more critical.

    1. On 2026-03-13 17:56:20, user NomosLogic wrote:

      Important preprint out of Johns Hopkins — LLMs evaluated as a diagnostic safety net for correcting physician errors.<br /> The right question to ask alongside it: for which clinical decisions is "better probabilistic reasoning" the correct architectural answer, and for which decisions is determinism required?<br /> Drug-gene interactions have correct answers. They are computable. An LLM that reasons well about a CYP2C19 finding is still approximating what a deterministic rules engine computes exactly — every time, auditably, without session-level variance.<br /> The safety net shouldn't be a better guesser. It should be a system that cannot get the answer wrong.

    1. On 2026-03-10 21:27:15, user Abdul Harif wrote:

      Training a 160-input multilayer perceptron on a cohort of only 35 unique participants is highly prone to overfitting, even with the inclusion of dropout layers and early stopping. Also, the control group only provided a single breath specimen at one time point, whereas the patient group provided breath specimens before treatment and again 6 to 8 weeks later. This introduces unmitigated temporal confounding variables, such as seasonal changes or device drift over the 8-week period, which the control group does not account for.

    1. On 2026-03-09 13:24:18, user David Glasser, MD wrote:

      All authors are equity owners of the system studied. Were these the same experts that reviewed discordant cases and sided with the system they owned almost 4 times as often as the board certified clinicians who made the initial assessments?

      Ethical review was by the company that markets the system studied.

      MAJOR sources of bias and conflicts of interest here. I give them credit for being forthright about disclosing them.

    2. On 2026-02-20 13:58:16, user Peer Reviewer wrote:

      We requested the materials needed to reproduce the main results in this preprint. Although the manuscript states that “all data produced in the present study are available upon reasonable request,” a request from our group did not receive a response, and the requested materials have not been made available for independent replication.

    1. On 2026-02-27 17:03:20, user Deepak Modi wrote:

      The NGS dataset in this study is available with IBDC Study Accession: INRP000591 and INSDC (SRA) Project Accession: PRJEB108860

    1. On 2026-02-22 17:29:44, user Sue Hewitt wrote:

      How is it possible to review the supplementary tables, which do not seem to be included in the preprint? Will this research be submitted for peer review?

    1. On 2026-02-21 03:18:58, user Naoki Watanabe wrote:

      We are pleased to announce that this preprint has undergone peer review and has been published in a formal journal. Please refer to the final version of the article.

      Watanabe N, Watari T, Otsuka Y. Desulfovibrio Bacteremia in Patients with Abdominal Infections, Japan, 2020–2025. Emerg Infect Dis. 2026 Feb [cited 2026 Feb 21];32(2). Available from: http://dx.doi.org/10.3201/eid3202.251581

    1. On 2026-02-02 05:15:23, user S. Miyamoto wrote:

      Now published in Commun Med.

      Miyamoto S, Numakura K, Kinoshita R, Arashiro T, Takahashi H, Hibino H, Hayakawa M, Kanno T, Sataka A, Sakamoto R, Ainai A, Arai S, Suzuki M, Yoneoka D, Wakita T, Suzuki T. Serum anti-nucleocapsid antibody correlates of protection from SARS-CoV-2 re-infection regardless of symptoms or immune history. Commun Med (Lond). 2025 May 15;5(1):172. doi: 10.1038/s43856-025-00894-8. PMID: 40374831; PMCID: PMC12081900.

    1. On 2026-01-31 09:06:49, user Chris Morgan wrote:

      I understand from the Methods that the background population were required to have at least 12 months registration prior to each observation year and that PD events prior to 2007 were excluded. I am therefore assuming PD patients had to have the first diagnosis after this 12 months as a wash-in to ascertain true incident cases. As this is not explicit in the text and noting the higher incidence in the early years of the study, could the author just confirm this please

    1. On 2026-01-29 02:57:44, user Vanessa Haase wrote:

      Correction to my previous comment: The HQ calculations for heart rate increase are scientifically invalid. Heart rate increase from nicotinic agonists is the intended pharmacological effect, not an adverse outcome. The authors use an ARfD based on heart rate increases, but HQ methodology is designed to assess adverse health effects. Pharmacological receptor activation that produces the desired stimulant effect cannot be characterized as a toxicological hazard. By this logic, caffeine would have unacceptable HQs for increased alertness.<br /> Valid cardiovascular risk assessment requires identifying doses causing actual adverse outcomes like sustained tachycardia leading to arrhythmias, hypertensive crises, or cardiovascular events in vulnerable populations. The physiological response that constitutes the purpose of product use is not a safety threshold exceedance.<br /> The conclusions about exceeding safety thresholds rest on this fundamental mischaracterization of pharmacology as toxicity. This undermines legitimate regulatory concerns about unregulated nicotine analogues. Meaningful risk assessment requires identification of true adverse cardiovascular outcomes, not normal receptor-mediated responses.

    1. On 2026-01-26 18:38:43, user Johanna Karla Lehmann wrote:

      No single factor that could be associated with a deterioration in post-COVID-19 symptoms after a SARS-CoV2 vaccine dose was investigated (objectives, title, primary outcome). Factors that could have been considered include quantitative changes in spike production, ACE2 expression, Ang II and Ang 1-7 levels, immunological/ inflammatory markers or changes in the severity/extent of comorbidity (blood pressure behaviour, changes in blood circulation, glucose/lipid metabolism, blood clotting, etc.) and smoking habits.<br /> A deterioration and/or increase in symptoms comes as no surprise. Both the infection and the desired acquired immunity through a COVID-19 vaccination (vaccine indication) require SARS-CoV2 spike antigens (RBD of the spike S1 subunit). These are known to react with ACE2 and trigger pathophysiologically undesirable specific organ dysfunctions and symptoms via an Ang II increase and other reactions (bradykinin increase, etc.). For long Covid, an influence of spikes on nicotinic acetylcholine receptor reactions in the periphery and in the CNS is also being discussed. This was evident in the symptoms of coughing (probably dry cough!) and concentration problems, both of which increased significantly after vaccination.<br /> Vaccination (inclusion criterion) and coughing (a symptom!) were named as the ‘identified only factors’ for a worsening of post-COVID-19 symptoms. Neither of these can be described as factors underlying the worsening. The different risks associated with specific vaccines (mRNA, adenovirus, protein-based, attenuated) should be carefully examined in a representative, controlled, comprehensive clinical study.

    1. On 2026-01-26 15:25:00, user Veronica Ruiz wrote:

      In response to the question Should antigen-antibody rapid diagnostic tests be used to detect acute HIV infection? I believe that use of fourth-generation rapid tests should continue and be encouraged simply because reduce the window period, and it always should be incorporated into a complete diagnostic and confirmatory algorithm. Like other HIV diagnostic tools, its true usefulness lies in the interpretation framed within an algorithm, and I believe that the vast majority of people who work in the field of diagnosis understand this, and it is clearly outlined in all the guides or recommendations on the subject. In other words, fourth-generation rapid tests alone do not guarantee the detection of acute infection, but in combination of counseling applied to high-risk populations including continuous monitoring and application of other tests including NAT and Ac/Agp24 combo for the detection of the acute viremia phase and the information provided to users can considerably improve the chance of early detection.<br /> As has already been expressed in other comments, there is a huge bias in comparing studies that use different tests, including some that were not approved for use and whose sensitivity increased in updated versions, as well as including mixed populations of very different types in which the percentage of incidence of HIV infection is not comparable.<br /> However, I believe that the greatest bias in the sensitivity calculation is the failure to take into account the appearance of different markers throughout the evolution of the infection, a topic already described by Fiebig in Fiebig EW, Wright DJ, Rawal BD, et al. Dynamics of HIV viremia and antibody seroconversion in plasma donors: implications for diagnosis and staging of primary HIV infection. AIDS 2003; 17(13):1871–1879. <br /> This review conflates detection methods using tests that detect different markers (Table I) related to the time of infection. For example when compared to a NAT test, which has a shorter window period, a fourth-rate rapid test won't have the same diagnostic scope, just like tests that exclusively detect the p24 antigen, whose detection threshold is well-proven to be much lower than any rapid test.<br /> Is also a well-known and reported fact that fourth-generation rapid tests do not perform as well as instrumental methods, but is an inherent limitation of method. Therefore, comparisons should be made using methods with at least a similar detection threshold and window period.The discussion is always interesting and enriching, and I will seek to contact the authors to continue it.

    2. On 2026-01-23 13:21:44, user Dr Ali Johnson Onoja wrote:

      The analysis aggregates performance data from a heterogeneous mix of fourth-generation HIV rapid tests, including research-use-only products (e.g., SD Bioline HIV Ag/Ab Combo), discontinued devices (Combo RT, D4G, E4G, Geenius HIV-1/2, Bio-Rad GS HIV Combo), the FDA-approved U.S. version of Determine HIV-1/2 Ag/Ab Combo, and the WHO-prequalified Alere HIV Combo/Determine HIV Early Detect. In the Nigerian context, where national HIV testing algorithms approved by the Federal Ministry of Health (FMoH) and NACA restrict use to WHO-prequalified assays, pooling data from obsolete or non-programmatic tests without stratification by brand, version, or regulatory status may misrepresent the true performance of diagnostics currently available or deployable in Nigeria.<br /> Assumption of Class-Dependent Performance and Its Programmatic Implications in Nigeria<br /> The review assumes that diagnostic performance is determined primarily by test class (i.e., fourth-generation Ag/Ab RDTs), without sufficient consideration of infection kinetics, targeted biomarkers, assay technology, or specimen type. In Nigeria—where HIV testing is predominantly conducted using finger-prick whole blood in community, primary healthcare, and outreach settings—test performance during acute HIV infection (AHI) is heavily influenced by the timing of presentation and the biological stage of infection. Failure to account for Fiebig stage–specific detectability risks overgeneralizing performance expectations and may undermine rational decisions about where and how fourth-generation RDTs could add value within Nigerian testing strategies.<br /> Non-Standard Definitions of Acute HIV Infection and Relevance to Nigerian Epidemiology<br /> Definitions of AHI vary widely across included studies, spanning multiple Fiebig stages (I–III or I–IV), each characterised by distinct biomarker kinetics (HIV RNA -> p24 antigen -> antibody). In the Nigerian epidemic—where individuals often present late for testing but key populations and high-incidence sub-groups may test during early infection—averaging sensitivity across biologically heterogeneous stages obscures the specific window (notably p24-positive Fiebig II–III) in which Ag/Ab RDTs are theoretically expected to improve case detection. This limits the applicability of pooled sensitivity estimates for informing targeted AHI screening strategies in Nigeria, including among key populations, STI clinics, and PrEP entry points.<br /> Influence of Older Devices and Study Design on Applicability to Nigeria<br /> Lower pooled sensitivity estimates are largely driven by evaluations of older diagnostic devices and laboratory-based case–control studies. Approximately two-thirds of included studies rely on non-consecutive sampling, small AHI sample sizes (<100), and archived specimens—designs known to introduce spectrum and selection bias. For Nigeria, where HIV testing occurs primarily in real-world service delivery settings with operational constraints, such estimates may understate the potential performance of newer WHO-prequalified fourth-generation RDTs when integrated appropriately into national algorithms. Consequently, these findings should be interpreted cautiously when informing policy decisions, guideline updates, or pilot implementation of AHI screening in Nigeria.

    3. On 2025-12-24 04:25:36, user Dr Micah Matiang'i wrote:

      If the role of Ag/ab RDTs is not well understood in resource limited settings , then there is need to do more population based studies before WHO reaches a conclusion

    4. On 2025-12-19 17:14:16, user Cesar Ugarte wrote:

      The preprint by Fajardo et al. addresses an important evidence gap regarding the utility of combined antigen–antibody tests for detecting acute HIV infection. Although the authors adopt a valuable global perspective, the interpretation and synthesis of the data would benefit from greater nuance to enhance clinical relevance. The authors' QUADAS-2 assessment shows High Risk of Bias regarding patient selection and Unclear Risk regarding the conduct of the index test. In diagnostic epidemiology, such findings are not just descriptive but also signal a huge spectrum effect and possible threshold bias. Therefore, the summary estimates presented in Figures 3 and 4 may reflect a statistical average of disparate clinical realities rather than a reliable indicator of test performance (for example in Figure 3 there are 10 studies with a sensitivity less than 10%, including some with 0%, so the evaluation in detail of these studies should be done to see if these studies can be combined with the other ones). Another issue is the inclusion of "obsolete" diagnostic platforms that have been withdrawn due to suboptimal performance. A sensitivity analysis or subgroup stratification should be restricted to tests currently on the market. This would enable the reader to distinguish between the historical evolution of the technology and the expected performance in contemporary clinical practice.

      The interpretation of diagnostic performance also should be addressed in detail. Whereas sensitivity and specificity have usually been considered "intrinsic" to a test (so doesn´t depends on disease prevalence), evidence suggests significant variation across clinical settings. The underlying epidemiological status and operator expertise can affect the test’s accuracy. Finally, I agree with the authors that real-world evidence on cost-effectiveness and implementation barriers is lacking. However, we should be very careful to avoid having a biased meta-analytic estimate that leads to the premature abandonment of "imperfect" but viable diagnostic solutions. In the case of acute HIV infection, for which early detection is critical to ART initiation and reduction of secondary transmission, interpretation of this evidence needs to balance statistical rigor against the urgent public health need for early diagnosis.

    5. On 2025-12-17 21:30:43, user Norman Moore wrote:

      We have contacted the authors of the article Should antigen-antibody rapid diagnostic tests be used to detect acute HIV infection? A systematic review and meta-analysis of diagnostic performance by Fajardo et al. ( https://doi.org/10.1101/2025.10.14.25338004) . The primary limitation of this article is that it conflates the performance of 4th generation HIV tests that (1) were never launched, (2) that were earlier versions of tests that are no longer available in most parts of the world, and (3) tests that have received WHO pre-qualification (PQ), in a single analysis despite the known and significant differences in performance among them. This has resulted in lower performance representation of certain products over others. It would be more beneficial to the medical community to have a meta-analysis that includes HIV diagnostic tests that are both CE marked and have WHO PQ to maximize the real-world applicability of this systematic review.

    6. On 2025-12-13 03:02:23, user Missiani wrote:

      Title: Should antigen-antibody rapid diagnostic tests be used to detect acute HIV infection? A systematic review and meta-analysis of diagnostic performance<br /> Authors: Emmanuel Fajardo, Céline Lastrucci1, Pascal Jolivet1, Magdalena DiChiara1, Carlota Baptista da Silva1, Busi Msimanga1, Anita Sands2, Cheryl Johnson1

      The authors systematically searched six databases for studies evaluating Ag/Ab RDTs vs laboratory reference standards in individuals aged >=18 months. Out of 53 studies from 24 countries, they documented a pooled sensitivity of Ag/Ab RDTs for AHI to be 48% (95% CI: 34–62) with specificity of 97% (95% CI: 84–100). They concluded that Ag/Ab RDTs appear to have limited ability to detect AHI, missing more than half of AHI cases<br /> They also documented analytical sensitivity (detection of p24 antigen) at 31%, and antibody detection at 15% which was too low.

      I have three main comments that can improve the programmatic application of this manuscript <br /> 1. The study is presented negatively and concludes “Detection of AHI using Ag/Ab RDTs remains a challenge” despite the effort made and resources used. The study oversimplifies highly variable diagnostic data and assumes similarity between the studies, ignoring that a sensitivity of 48% and a specificity of 97% means that half of the kits performed better and almost all were specific. In Table 2, the authors examined region, study settings, and design, population, specimen, etc., but did not examine the group of kits whose sensitivity and specificity exceeded the pooled values. By examining this group of kits, they will successfully address the title of the article (Should antigen-antibody rapid diagnostic tests be used to detect acute HIV infection). Omitting this subgroup analysis presents one dimension of the data. We recommend they include this analysis as a way to address the gaps.<br /> 2. The authors present the p24 and Ag/Ab as a standalone approach rather than a combined or multiplex kit to address early diagnosis during AHI, which will provide an opportunity in low-income countries to reduce transmission, improve linkage to care and clinical outcome. Based on their sensitivity of 48%, multiplexing the test would improve diagnosis by the same margin, which is a substantial gain. We recommend adding a paragraph on the impact of incorporating p24 into a multiplex platform. Because many diagnostic tests are now packaged as multiplex platforms, incorporating this perspective will give the title greater depth and better reflect current testing practices<br /> 3. Some test kits reviewed in the study are either obsolete, recalled, or never progressed beyond early pre-evaluation stages. This raises significant concerns about the validity and current use of the findings. Manufacturers may have already recognized the kits’ poor sensitivity and, as a result, chose not to move forward with full production. Without acknowledging the discontinued or preliminary status of these kits, the study’s conclusions risk being misleading since the kits are not on the market. Recognizing the actual status of these products is essential, as it directly affects how their findings should be interpreted and whether they can responsibly inform policy or implementation decisions.

    1. On 2026-01-26 09:10:28, user Gail Davey wrote:

      The Neglected Tropical Diseases considered by the 2021-25 Ethiopian National Strategic Plan ( https://espen.afro.who.int/sites/default/files/content/document/Third%20NTD%20national%20Strategic%20Plan%202021-2025_0.pdf ) include podoconiosis. This is also considered among the skin-NTDs by WHO. Extensive information is available on the distribution and impact of podoconiosis, which has a greater burden than LF in Ethiopia. It would be helpful to include mention of this conditon within the manuscript.

    1. On 2026-01-23 19:44:39, user David Laursen wrote:

      Thanks for an interesting preprint, which I hope to read more carefully soon. I am not particularly well versed within causal inference so apologies if the question is unclear.

      I noticed your warning against conditioning on post-treatment belief (since it is a collider). Just wondering, does this reservation extend more generally to cautioning against testing for success of blinding at all, regardless of doing a stratified analysis of treatment effects by belief (in an estimation setting, this would probably be estimating differences in beliefs between arms, either with conventional 2x2 measures, or blinding indices). This appears to be a central discussion in many fields, so would appreciate your reflections.

    1. On 2026-01-19 13:00:58, user Gene C Koh wrote:

      Gene Ching Chiek Koh, Serena Nik-Zainal

      Department of Genomic Medicine, University of Cambridge, CB2 0QQ, UK.

      We commend Kanwal et al. for their timely evaluation of the in vivo mutagenic potential of CX-5461. This follows our report that CX-5461 induces substantial mutagenesis in cultured mammalian cells1. The authors analysed samples from four patients treated with CX-5461, including marrow aspirates, trephine biopsies, PBMCs, and skin lesions collected at early treatment timepoints (baseline; days 1, 2, or 9; and end-of-treatment of a 21-/28-day cycle), and used error-corrected duplex sequencing to detect low-frequency mutations. They concluded that CX-5461 exposure did not increase single-/ double-base substitution or indel burdens, nor reproduced the mutational signatures reported in our in vitro study. While we welcome their contribution, several methodological and interpretive shortcomings limit the conclusions that can be drawn.

      1. Data presentation<br /> Figures 1–3 present absolute mutation counts instead of frequencies normalized to total informative duplex bases per sample. In duplex sequencing, normalization is a basic requirement to account for variability in sequencing depth and library complexity; without it, true mutation accrual or fold-change differences versus controls (if any) cannot be assessed reliably.

      2. Experimental controls, assay sensitivity, and performance<br /> The study lacks essential positive and negative controls making it impossible to evaluate whether the sequencing and analytical processes used by the authors have worked. Clinical samples with known mutational signatures detectable through this approach should have been included to confirm assay sensitivity and substantiate a true negative finding. This is fundamental. Samples from patients unexposed to CX-5461 were also required as negative controls to establish background variability, affording confidence intervals and statistical robustness.<br /> Moreover, the authors have not shown awareness of the assay’s limit of detection (LOD). What is the smallest measurable fold-change at the reported sequencing depth? Without this, one cannot determine the smallest mutational differences that could have been missed. The authors have not disclosed quality-control metrics required to understand whether sufficient data quality was achieved for detecting differential mutagenesis. P/S: TwinStrand kit has an error rate ~0.5e-7 to 1e-7 depending on the protocols, and this can be considerably higher if DNA quality is low or from fixed biopsies.

      3. Lack of curation, comparisons to literature<br /> The reported mutation counts did not make sense (baseline values exceeding treated samples, patient samples sometimes lower than kit control). The authors should perform some ‘sanity check’ comparisons with published mutation frequencies of respective normal adult tissues from other duplex-sequencing studies2,3. Analytical rigour would include, for example, examining whether detected variants represent driver mutations from clonal haematopoiesis or occurred in genes under post-treatment selection. Such analyses would have demonstrated critical evaluation of data quality and biological relevance.

      4. Cell-type considerations, sampling window<br /> Most analysed compartments—PBMCs, MACS-sorted marrow fractions—are dominated by mature, non-dividing cells that rarely fix new mutations. A more relevant population for assessing mutagenicity is the haematopoietic stem and progenitor cells (HSPCs), typically <0.5% of marrow cells. A null result in the analysed compartments could just mean no widespread mutation fixation in mature immune cells; it does not exclude the possibility of mutagenesis in progenitors below the detection threshold of the current assay.<br /> In addition, samples were taken at very early timepoints (days 1, 2, 9, or EOT) of the first treatment cycle. At such intervals, mutagenic events are unlikely to have become fixed, as mutagen-induced DNA damage will need time to become embedded through DNA repair and replication. Exposure in terminally-differentiated cells might yield no detectable mutations. If exposure occurs on dividing cells, mutational footprints may only become detectable months or years after exposure. The current dataset lacks the temporal window necessary to assess cumulative in vivo mutagenicity.

      5. Expected evidence of prior treatments <br /> All four patients reportedly had “measurable, relapsed, or refractory advanced haematologic malignancies without any standard therapeutic options available”4. Although treatment histories were not provided, these patients likely received multiple prior therapies (e.g., doxorubicin, cyclophosphamide, etc) that could induce characteristic mutational signatures in normal haematopoietic cells5. Were signatures of prior therapy detected by the authors? Their absence raises concerns regarding the overall assay sensitivity and/or suggests that sampling strategy was suboptimal for detecting mutagenic exposures.

      6. Interpretation of model data<br /> While critical of our findings in cultured human cells as “not adequately representative of physiological human tissue” – a limitation we explicitly acknowledged in our manuscript’s title and discussion – the authors cited a C. elegans study6 in support of their argument of “low non-selective mutagenic potential of CX-5461”. This interpretation is incorrect: the worm study reported high copy-number aberrations, high SNV burdens, and a distinct A>T/T>A-rich signature after CX-5461 exposure, with survival requiring multiple repair pathways (homology-directed repair, microhomology-mediated end joining, nucleotide excision repair, and translesion synthesis). If anything, these cross-species findings reinforce rather than contradict our observations that CX-5461 is highly mutagenic. The concentrations used in that study were chosen to promote viability in the worms, not to minimise mutagenicity. Selective viability does not equate to selective mutagenicity.

      7. Clinical mutagenicity testing<br /> We agree that clinical safety assessments must be rigorous and physiologically relevant. The authors dismissed our experiments as not rivalling the “GLP-compliant, non-mutagenic” results of the CX-5461 drug development pathway. However, those mutagenicity data are not available in the public domain and have neither been shared by the authors nor the company that distributes CX-5461.

      We urge the authors to reconsider and not simply dismiss our findings. First, the primary clinical quality mutagenicity assay (required by agencies such as the US Food and Drug Administration (FDA), European Medicines Agency, and UK Medicines and Healthcare Regulatory Agency (MHRA)) referred to by the authors comprises the Ames test – a reverse gene mutation test performed in prokaryotes (e.g., E.coli, Salmonella).

      Second, according to the FDA’s ICH S2(R1) guidance for a standard battery of mutagenicity assays (Safety Implementation Working Group of the International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use), additional genotoxic assays should be performed in mammalian cells in vitro (where some of the more common assays include metaphase chromosome aberration assays, the micronucleus assay, and the mouse lymphoma L5178Y cell Tk (thymidine kinase) gene mutation assay (MLA)) or in in vivo studies as necessary.

      Third, the FDA guidance acknowledges that “no single test is capable of detecting all genotoxic mechanisms relevant in tumorigenesis” and that the standard battery serves primarily for hazard identification rather than comprehensive assessment of mutagenic potential. For negative in vivo results, the ICH S2(R1) guidance requires evidence of adequate target-tissue exposure (e.g., toxicity in the tissue, TK/PK data, or direct tissue concentrations) to validate interpretability. Without such data, negative findings have limited meaning, especially where in vitro systems demonstrate strong mutagenicity.

      Fourth, while the Ames test served its purpose for decades, there are well-described problems including false positives, false negatives and critically, a lack of human metabolism that even supplementation with rodent S9 mix cannot always overcome.

      Finally, a point also raised by the accompanying commentary to our publication is that perhaps the time has come to re-evaluate how mutagenicity assays are performed. Current assays cannot capture the genome-wide mutation patterns revealed by whole-genome sequencing in human cells, and as a community we should consider using unbiased, agnostic, modern genomic approaches capable of detecting all classes of mutational changes in human cells. This is not an attack on CX-5461; rather, it is a call to the community to consider re-evaluation of mutagenicity assays in drug development.

      8. Unsubstantiated claims<br /> The claim of potential contaminants accounting for the mutagenic outcomes we and others have observed is speculative and unsupported. The fact that multiple studies1,6 observed the same mutagenic outcomes using CX-5461 from independent sources suggests that this is unlikely. The authors showed no analytic chemistry (LC-MS/MS) and/or spiking experiments to substantiate this claim.

      9. Inadequate supporting material throughout <br /> There were many gaps in the methods/supporting information, including adequate clinical annotation, precise sampling times/total treatment cycle, and basic quality-control metrics. Experimental details (e.g., antibodies used for MACS sorting, essential for interpreting analysed subpopulations) were not provided. These omissions limit transparency, reproducibility, and the interpretability of the findings.

      10. Beneficence, non-maleficence, autonomy, justice<br /> First, in academia and medicine, we are guided by the principle of doing no harm. In identifying mutagenesis in experimental systems (an incidental finding), we acted in the best interest of the community – reporting an observation that could have an impact on patients and acknowledging the limitations of our system. We have no role in the (dis)continuation of clinical trials; we simply presented our data transparently and highlighted potential risk. <br /> Second, while the authors chose to discontinue their trial, several others remained active (e.g., NCT04890613, NCT06606990, NCT07069699, NCT07147231, NCT07137416). Their decision was conservative, and in our view, scientifically prudent. We commend their caution. However, it does not justify criticism of those of us reporting safety concerns in good faith.<br /> Third, as a community, we serve society better by being aware of issues, addressing the problems with robust experiments rather than polarising into groups “for” or “against” a compound, so that truly beneficial compounds can get to patients as quickly as possible. <br /> Finally, safety concerns may extend beyond mutagenesis and include tumour promotion effects. CX-5461’s interaction with TOP2B, for example, has been linked to serious, late-emerging toxicities, including therapy-induced leukaemia and cardiotoxicity7-10.

      Concluding remarks<br /> Given the experimental and analytical shortcomings outlined above, definitive conclusions regarding CX-5461’s in vivo mutagenicity cannot yet be drawn. The absence of evidence should not be taken as evidence of absence. Rigorous, longitudinal studies with appropriate controls and independent oversight are required to assess true medium- to long-term risks.

      We share the authors’ view that thorough, transparent evaluation of anticancer agents is essential. Given the authors’ vested interest in finding a negative result, we suggest independent individuals be involved in performing the analysis/interpretation of their studies to negate potential conflicts of interest. We remain open to collaboration in this effort, in the shared interest of patient safety and scientific integrity.

      1. Koh, G.C.C., Boushaki, S., Zhao, S.J., Pregnall, A.M., Sadiyah, F., Badja, C., Memari, Y., Georgakopoulos-Soares, I., and Nik-Zainal, S. (2024). The chemotherapeutic drug CX-5461 is a potent mutagen in cultured human cells. Nat Genet 56, 23-26. 10.1038/s41588-023-01602-9.
      2. Abascal, F., Harvey, L.M.R., Mitchell, E., Lawson, A.R.J., Lensing, S.V., Ellis, P., Russell, A.J.C., Alcantara, R.E., Baez-Ortega, A., Wang, Y., et al. (2021). Somatic mutation landscapes at single-molecule resolution. Nature 593, 405-410. 10.1038/s41586-021-03477-4.
      3. Machado, H.E., Mitchell, E., Obro, N.F., Kubler, K., Davies, M., Leongamornlert, D., Cull, A., Maura, F., Sanders, M.A., Cagan, A.T.J., et al. (2022). Diverse mutational landscapes in human lymphocytes. Nature 608, 724-732. 10.1038/s41586-022-05072-7.
      4. Khot, A., Brajanovski, N., Cameron, D.P., Hein, N., Maclachlan, K.H., Sanij, E., Lim, J., Soong, J., Link, E., Blombery, P., et al. (2019). First-in-Human RNA Polymerase I Transcription Inhibitor CX-5461 in Patients with Advanced Hematologic Cancers: Results of a Phase I Dose-Escalation Study. Cancer Discov 9, 1036-1049. 10.1158/2159-8290.CD-18-1455.
      5. Mitchell, E., Pham, M.H., Clay, A., Sanghvi, R., Williams, N., Pietsch, S., Hsu, J.I., Obro, N.F., Jung, H., Vedi, A., et al. (2025). The long-term effects of chemotherapy on normal blood cells. Nat Genet 57, 1684-1694. 10.1038/s41588-025-02234-x.
      6. Ye, F.B., Hamza, A., Singh, T., Flibotte, S., Hieter, P., and O'Neil, N.J. (2020). A Multimodal Genotoxic Anticancer Drug Characterized by Pharmacogenetic Analysis in Caenorhabditis elegans. Genetics 215, 609-621. 10.1534/genetics.120.303169.
      7. Pan, M., Wright, W.C., Chapple, R.H., Zubair, A., Sandhu, M., Batchelder, J.E., Huddle, B.C., Low, J., Blankenship, K.B., Wang, Y., et al. (2021). The chemotherapeutic CX-5461 primarily targets TOP2B and exhibits selective activity in high-risk neuroblastoma. Nat Commun 12, 6468. 10.1038/s41467-021-26640-x.
      8. Zhang, W., Gou, P., Dupret, J.M., Chomienne, C., and Rodrigues-Lima, F. (2021). Etoposide, an anticancer drug involved in therapy-related secondary leukemia: Enzymes at play. Transl Oncol 14, 101169. 10.1016/j.tranon.2021.101169.
      9. Cowell, I.G., Sondka, Z., Smith, K., Lee, K.C., Manville, C.M., Sidorczuk-Lesthuruge, M., Rance, H.A., Padget, K., Jackson, G.H., Adachi, N., and Austin, C.A. (2012). Model for MLL translocations in therapy-related leukemia involving topoisomerase IIbeta-mediated DNA strand breaks and gene proximity. Proc Natl Acad Sci U S A 109, 8989-8994. 10.1073/pnas.1204406109.
      10. Zhang, S., Liu, X., Bawa-Khalfe, T., Lu, L.S., Lyu, Y.L., Liu, L.F., and Yeh, E.T. (2012). Identification of the molecular basis of doxorubicin-induced cardiotoxicity. Nat Med 18, 1639-1642. 10.1038/nm.2919.
    1. On 2026-01-14 16:06:13, user Charles Tritt wrote:

      This is interesting and important work. However, the flaw I see in this study is that it included only 40 episodes of hypoxia (defined as a SpO2 of < 90%) out of 1760 measurements. Arterial saturation measurements are only clinically significant when they are significantly low, so the approach used doesn’t seem to answer the important question – are there systematic pulse ox errors that make a clinical difference?

      The linked protocols show induced hypoxia (I assume by subjects breathing air diluted with nitrogen). Of course, this couldn’t be done with critically ill patients. But I don’t see that the state of the patients being particularly important to the question of systematic pulse ox errors. Would it not be a better approach to test healthy individuals an induce hypoxia so their data set contains the clinically important information.

    1. On 2026-01-13 16:13:38, user Christine Stabell Benn wrote:

      Comment on “Non-specific effects of vaccines on all-cause mortality: a meta-analysis of randomized controlled trials (RCTs) 2012–2025”<br /> Christine Stabell Benn, Frederik Schaltz-Buchholzer, Sebastian Nielsen, Peter Aaby<br /> We commend the authors for addressing the important and contentious question of non-specific effects (NSEs) of vaccines on all-cause mortality. However, we have several major concerns regarding the framing, completeness, methodology, and interpretation of the preprint. Collectively, these issues undermine the conclusions drawn.

      1. Restricted research question and dismissal of large parts of the evidence baseThe authors explicitly restrict their review to randomized controlled trials (RCTs) published after the WHO review of non-specific effects(1). If the stated objective is to assess the evidence for NSEs on all-cause mortality in randomized trials, an updated meta-analysis incorporating all relevant RCTs, rather than an arbitrarily time-limited subset, would be more informative. The decision to exclude pre-2012 RCTs from the main analysis appears methodological rather than substantive and risks answering a narrow procedural question rather than addressing the broader scientific question.

      More importantly, NSEs represent a research area in which randomized trials are inherently difficult or impossible to conduct at scale, because the vaccines in question are already part of routine immunization schedules. As in other areas of public health - such as smoking, breastfeeding, or nutrition - causal inference therefore relies on triangulation across multiple study designs, including observational studies and natural experiments, supported by biological and immunological evidence.<br /> If the intention is to provide a meaningful update on the state of the evidence for NSEs, a comprehensive synthesis that acknowledges the strengths and limitations of all relevant study designs - or at minimum a clear and balanced justification for excluding them - is required.

      2. Incomplete identification of relevant randomized trialsDespite claiming a comprehensive search, the review misses several important randomized controlled trials that are directly relevant to NSEs, including recent RCTs published well within the stated search window (e.g. PubMed IDs: 39357573, 38350670, 33893799, 30256314). The omission of these trials raises concerns about the sensitivity of the search strategy and undermines confidence in the completeness of the evidence base.

      3. Extreme clinical and methodological heterogeneity invalidates the pooled meta-analysis<br /> The meta-analysis combines trials of three different vaccines (BCG, measles vaccine, and OPV) administered at vastly different ages (birth to 59 months), with follow-up periods ranging from days to five years, and using different randomization schemes and outcomes structures. This is not merely “heterogeneity,” but fundamentally different interventions addressing different biological hypotheses.

      Pooling these studies is not equivalent to combining “apples and bananas,” but rather apples and cars. The resulting pooled estimate does not correspond to a coherent causal treatment effect and is therefore not interpretable.

      4. Non-adherence with the WHO meta-analysis methodologyBy pooling all vaccines together, and furthermore by not focusing on the time window where a given vaccine is the most recent, the authors of the new meta-analysis violates the principles set out in the WHO meta-analysis, which emphasized vaccine-specific analyses and the importance of the most recent vaccine exposure.

      5. Overreliance on conservative confidence interval methods without adequate justificationThe authors emphasize the use of the Hartung-Knapp-Sidik-Jonkman (HKSJ) method as providing “more reliable and conservative control of type I error.” While HKSJ can be appropriate when few studies estimate the same underlying effect, its application here - given the very marked heterogeneity and conceptual incoherence of the pooled treatment effect - adds statistical conservatism without resolving the more fundamental problem of model misspecification. The resulting wide confidence intervals should not be interpreted as robust evidence against NSEs.

      6. Misinterpretation of heterogeneity statistics (I²)The statement that an I² of ~44% indicates that “approximately half the differences in the results are due to actual variations between studies” is misleading in this context. I² is meaningful only when studies estimate the same underlying causal association. When fundamentally different interventions are pooled, I² no longer has the interpretation implied by the authors.

      7. Speculation that early BCG effects are due to bias is unsubstantiatedThe manuscript repeatedly suggests that observed mortality reductions within the first 1–3 days after BCG vaccination may reflect bias due to lack of blinding. This speculation appears inconsistent with the design and reporting of the original trials. In Guinea-Bissau randomization occurred at discharge, and post-randomization care was not provided by study staff(2). In the Indian trial, the authors explicitly state that it is unlikely that the lack of blinding influenced the result. In previous open label randomized trials of BCG Russian strain in the same sites, no difference in neonatal mortality was found, which suggests that the lack of blinding did not bias the findings(3).

      Given these safeguards, attributing early effects to bias is unsupported by trial evidence and suggests that the original studies were not carefully read or adequately considered.

      8. Ignoring extensive mechanistic evidence for rapid BCG effectsThe authors further imply that effects within days are biologically implausible. This overlooks a substantial body of experimental and clinical evidence demonstrating that BCG induces trained innate immunity, including rapid functional reprogramming of myeloid cells and emergency granulopoiesis, which can occur within days and protect against severe infections such as sepsis(4, 5). These mechanisms provide a biologically coherent explanation for early effects and should have been discussed as plausible alternatives to bias.

      9. Failure to engage with established explanations for heterogeneous measles vaccine effectsThe manuscript notes heterogeneity across measles vaccine trials but does not engage with recent work offering compelling explanations for these differences, including interactions with OPV campaigns and vaccination sequence effects(6). Ignoring this literature leads to an oversimplified interpretation in which heterogeneity is treated primarily as noise rather than as potentially informative signal.

      10. Introduction of an a posteriori unifying hypothesisLate in the discussion, the authors invoke a new hypothesis that all live-attenuated vaccines should yield similar NSEs on all-cause mortality. This hypothesis appears post hoc and is not clearly justified biologically. It has never been a hypothesis within the NSE field and is biologically implausible, not least because baseline mortality differs substantially by age. Introducing this assumption only after the pooled analysis further weakens the inferential logic of the paper.

      Overall assessmentThe manuscript raises an important question, but its conclusions are undermined by:<br /> • an artificially restricted scope,<br /> • incomplete inclusion of relevant RCTs,<br /> • inappropriate pooling across fundamentally different interventions,<br /> • speculative dismissal of biologically plausible findings,<br /> • and inconsistent use of hypotheses introduced after the analysis.<br /> As currently written, the preprint does not provide a reliable basis for concluding that NSEs of vaccines on all-cause mortality are absent or unimportant. A substantially revised analysis - grounded in a comprehensive evidence base, clearer causal questions, and vaccine-specific syntheses - would be required to support such claims.

      References1. Higgins JP, Soares-Weiser K, Lopez-Lopez JA, Kakourou A, Chaplin K, Christensen H, et al. Association of BCG, DTP, and measles containing vaccines with childhood mortality: systematic review. BMJ. 2016;355:i5170.<br /> 2. Biering-Sorensen S, Aaby P, Lund N, Monteiro I, Jensen KJ, Eriksen HB, et al. Early BCG-Denmark and Neonatal Mortality Among Infants Weighing <2500 g: A Randomized Controlled Trial. Clin Infect Dis. 2017;65(7):1183-90.<br /> 3. Adhisivam B, Kamalarathnam C, Bhat BV, Jayaraman K, Namachivayam SP, Shann F, et al. Effect of BCG Danish and oral polio vaccine on neonatal mortality in newborn babies weighing less than 2000 g in India: multicentre open label randomised controlled trial (BLOW2). BMJ. 2025;390:e084745.<br /> 4. Kleinnijenhuis J, Quintin J, Preijers F, Joosten LA, Ifrim DC, Saeed S, et al. Bacille Calmette-Guerin induces NOD2-dependent nonspecific protection from reinfection via epigenetic reprogramming of monocytes. Proc Natl Acad Sci U S A. 2012;109(43):17537-42.<br /> 5. Brook B, Harbeson DJ, Shannon CP, Cai B, He D, Ben-Othman R, et al. BCG vaccination-induced emergency granulopoiesis provides rapid protection from neonatal sepsis. Sci Transl Med. 2020;12(542):eaax4517.<br /> 6. Nielsen S, Fisker AB, da Silva I, Byberg S, Biering-Sørensen S, Balé C, et al. Effect of early two-dose measles vaccination on childhood mortality and modification by maternal measles antibody in Guinea-Bissau, West Africa: A single-centre open-label randomised controlled trial. EClinicalMedicine. 2022;49:101467.

    1. On 2026-01-10 20:05:34, user Sequoia wrote:

      I'm interested in seeing if replacing the UPFs near the checkout with non-UPFs would result in an increase in non-UPFs consumption, especially if they were easy to eat with no preparation required (e.g. an apple or energy balls), and if so, by how much.<br /> I am delighted to know that research is being done on this critical subject.

    1. On 2026-01-09 08:47:36, user Janne Ruotsalainen wrote:

      The MVA vector is highly immunogenic as it has been used as a small pox vaccine. The ex vivo ELISPOT responses against MVA are pretty high raising the question whether the anti-vector T cell responses became immunodominant and thus suppressed some neoantigen specific T cell responses?

    1. On 2025-06-15 21:35:28, user CP wrote:

      Great paper! The text makes reference to a "Supplementary Notes" section that doesn't seem to be in the PDF - is this part of the material that will be made available after peer reviewed publication? Sorry if this is a naive question; I'm new to preprints.

    1. On 2025-12-30 06:02:44, user James P wrote:

      Really nice, timely methods paper. It puts clear names and concrete examples on two ways biomarker studies can look better than they truly are in practice: (1) enrichment/range restriction, where you study a highly selected group and results don’t transport to typical patients, and (2) “double dipping,” where the same biomarker data influence who gets included and how performance is judged, which can inflate accuracy.

      I also appreciated how the audit plus the simple simulation experiments make the problems intuitive rather than abstract. The recommendations are practical (be explicit about the intended target population/estimand, and separate discovery from confirmation or prespecify analyses) and feel immediately useful for trial-ready cohorts and clinical workflows.

    1. On 2025-12-27 04:25:05, user Anjum wrote:

      Hi <br /> This manuscript has been published at <br /> John A, V R Reshma, El-Hazimy K. Bridging the Nutrition Education Gap: From Theory to Practice- A Scalable Model for Nutrition Practicums in Medical Training. Journal of Teaching Innovation and Reform. 2025;1:11-25.

    1. On 2026-03-25 16:26:02, user Kristina Jakobsson wrote:

      The outcome of this interesting study was the absolute change in eGFR from the start of the work shift to the end of the work shift.

      Cross-shift fluctuations of biomarkers are valuable for group-level evaluation of work-related kidney strain. S-Cr and S Cystatin C can rise significantly during a hot, labor-intensive shift and normalize overnight (1,2)

      However, eGFR should not be used for cross-shift comparisons, since eGFR assumes a stable level of creatinine over days (i.e., steady-state) (3) Hence, conventional eGFR calculations are not valid if creatinine is rapidly changing. S-Cr change is the preferred metric for cross-shift evaluations.

      An alternative for estimation of rapid functional changes during clinical AKI has also been suggested; the “Kinetic eGFR” (4).

      REFERENCES: <br /> 1. Lucas RAI, Hansson E, Skinner BD, Arias-Monge E, Wesseling C, Ekström U, Weiss I, Castellón ZE, Poveda S, Cerda-Granados FI, Martinez-Cuadra WJ, Glaser J, Wegman DH, Jakobsson K. The work-recovery cycle of kidney strain and inflammation in sugarcane workers following repeat heat exposure at work and at home. Eur J Appl Physiol. 2025 Mar;125(3):639-652. doi:10.1007/s00421-024-05610-3. Epub 2024 Oct 5. PMID: 39369140; PMCID: PMC11889006<br /> 2. Hansson E, Lucas RAI, Glaser JR, Weiss U, Ekström U, Abrahamson M, Wesseling C, Wegman DH, Jakobsson K. Understanding changes in serum creatinine during work in heat. Kidn Int Report vol 10 issue 8, p2860-63, Aug 2025. <br /> 3. Waikar SS, Bonventre JV. Creatinine kinetics and the definition of acute kidney injury. J Am Soc Nephrol. 2009;20: 672–679. doi: 10.1681/ASN.2008070669<br /> 4. Chen S (2013). Retooling the creatinine clearance equation to estimate kinetic GFR when the plasma creatinine is changing acutely. Journal of the American Society of Nephrology DOI: 10.1681/ASN.2012070653

    1. On 2025-12-05 05:35:16, user Evolutionary Health Group wrote:

      We at Evolutionary Health Group ( https://evoheal.github.io/ ) really enjoyed this paper.

      Here are our highlights:

      One of the strongest contributions is the introduction of a nonlinear null model of covariates that outputs a single scaler, which can be inserted into existing linear frameworks while adding the power of nonlinear modeling.

      The authors demonstrate that nonlinear covariate modeling consistently helps more than it harms: adding the null prediction rarely interferes with genetic inference and the gains are substantial for many traits, giving the method an encouraging risk-benefit profile.

      Instead of attempting to model exposures explicitly, the authors show that spatiotemporal information can capture complex environmental influences. Even though these features are non-causal, researchers can use such data to hypothesize environmental drivers without having to specify them individually in models.

      Using TreeSHAP-IQ, the authors show that nonlinear models find age-sex, seasonality-sex, and birth-home location interactions. These patterns are biologically credible and validated by external literature but cannot be captured by standard linear covariate adjustments. This shows that nonlinear covariate modeling doesn't just improve predictions, it produces interpretable biological insights.

    1. On 2021-12-01 22:19:03, user Kevin J. Black, M.D. wrote:

      You'll want to cite and discuss this article:<br /> Snowden JS, Craufurd D, Griffiths HL, Neary D. Awareness of involuntary movements in Huntington disease. Archives of Neurology. 1998;55(6):801-805.

    1. On 2020-12-03 21:43:53, user kdrl nakle wrote:

      It could also be that association is purely coincidental. Meaning people that die more often are older people and they are also more likely to be vitamin D deficient. So you really have nothing here.

    1. On 2020-04-05 22:12:33, user Soarintothesky wrote:

      What delay was used for the time adjustment. A 10 day delay for cases>deaths in the Aneirin Bevan University Health Board in Gwent in South Wales shows a 22% CFR.

    1. On 2020-06-04 00:48:58, user James Van Zandt wrote:

      Vitamin C is a common supplement. I suggest you track whether patients had taken vitamin C (and how much) before or in the early stages of their illness. If it is helpful, then we would like to know when it is most helpful.

    1. On 2020-04-10 00:26:48, user Brothers in arm wrote:

      Curious to know why the BCG vaccination last only about 20 years. I have had mine as an infant, without any further boosters. Still my skin tuberculin test remains reactive after almost 50 years. The reaction subsides before follow up check on day 3. This is read as negative for active TB. I assume the slight reaction as due to having had BCG, and I still have immunity. People who never had it do not get any reaction or erythema. Maybe any immunization can confer some cross immunity?

    2. On 2020-04-02 07:42:02, user japhetk wrote:

      I don't know why, but medrxiv keeps deleting my warning comments.

      So, I write brief comments again.

      This study doesn't control important variables as kept suggested in comments and probably the findings are due to spurious correlations.

      The one of uncontrolled important variable is "when the infection spread in the country". This study should have used the measure like "number of deaths or patients 10 days after 100th patients were detected in the country". Other analyses are doing that.<br /> The second uncontrolled important variable is "how long the country advanced BCG measure". UK, for example, advanced the BCG measure for more than 50 years. So, majority of nations are experienced with BCG.<br /> The third uncontrolled important variable is GDP. You can see the most of nations without BCG is Western rich countries which can do more tests, which are popular from tourists.<br /> I did analyses controlling these variables, and all the correlations between the length of BCG measure with coronavirus data (how fast the 100th patients were detected in the country, number of patients or deaths ten days after the 100th patients were detected in the country) are all insignificant. They did not even show the statistical tendencies.

      Many people have wrong ideas how effective BCG is though this preprint. Somebody has to give warnings. Please do not delete this warning.

    1. On 2020-06-30 08:50:43, user Simon Liebing wrote:

      I have 2 questions to the study:<br /> What explanation have the authors that only 2 of 5 indicators are positive?<br /> Why the virus vanishes after March 2019 again?

    1. On 2020-06-30 11:18:06, user Kevin McKernan wrote:

      Interesting work. Great to see the qPCR replicated at another lab and spike in controls.<br /> It would be very helpful to sequence the Amplicons to see if any variation exists that can augment the phylogenetkcs of the disease.

    1. On 2020-07-01 22:29:58, user John wrote:

      Loneliness is prevalent in COVID-19 crisis. Patients with Coronavirus are more lonely during the pandemic. Interesting findings for health psychology, psychological impact, public health, epidemiology and psychiatry.

    1. On 2021-12-28 00:53:06, user Drew wrote:

      Two issues need to be corrected for in the data before any real conclusions can be drawn. First, is there a relationship between age stratification, higher vaccination status and higher symptomatic disease - i.e., Simpson's Paradox. Second, was there a behavioral reason that impacted the results? For example, if vaccinations were required for admittance to crowded venue during the initial spike in Omicron cases, it would have skewed the results toward negative effectiveness.

    1. On 2020-07-07 14:29:11, user Anika Knuppel wrote:

      This article has been accepted for publication in the International Journal <br /> of Epidemiology, published by Oxford University Press.

    1. On 2021-01-26 03:42:24, user Terran Melconian wrote:

      Thanks for sharing this very interesting article.

      On page 6, for the definition of the x and z transforms, they are both given as sin(2*pi*t/tau). One of them is presumably meant to be a cosine, right?

    1. On 2020-06-21 11:55:23, user Dirk Monsieur wrote:

      Rough estimate: 1% infected at 12th of March; chances that 84 random people are not infected: 0,99^84 = 43%<br /> I'm not a statistical expert, but I think a power analysis would be good.

    1. On 2020-07-13 22:41:50, user Jim Coote wrote:

      I share the concerns expressed in the previous 2 comments. Surely the decrease in antibody levels would be entirely expected after the primary response. The acid test would surely be whether there was a good secondary response to any Covid19 based antigen. Any analysis of that should examine the cell based response as well as the humoral.

      Considering the concerns likely to be raised by their findings, I think it is a serious omission not to compare the data to antibody levels typically seen following primary responses to infections on which we have solid information on long term immunity, (both weak and strong). However this would be a completely academic consideration provided a good secondary response to Covid 19 antigen / virus was seen.

    1. On 2020-04-17 15:25:24, user Dr. James R. Baker wrote:

      Interesting approach and pretty convincing, but it does not take into account the number of asymptomatic infections associated with COVID. That is really substantial; some estimates of 30-50 percent. That would then double your number, wouldn't it?

    1. On 2019-11-12 00:51:39, user Guyguy wrote:

      EVOLUTION OF THE EPIDEMIC IN THE PROVINCES OF NORTH KIVU AND ITURI AT NOVEMBER 10, 2019

      Monday, November 11, 2019<br /> Since the beginning of the epidemic, the cumulative number of cases is 3,287, of which 3,169 confirmed and 118 probable. In total, there were 2,193 deaths (2075 confirmed and 118 probable) and 1067 people cured.<br /> 411 suspected cases under investigation;<br /> No new cases confirmed;<br /> No new deaths of confirmed cases have been recorded;<br /> 3 people healed from the CTE in North Kivu in Mabalako;<br /> No health workers are among the newly confirmed cases. The cumulative number of confirmed / probable cases among health workers is 161 (5% of all confirmed / probable cases), including 41 deaths;

      NEWS

      Awareness and vaccination day for Beni mototaxi drivers with the support of Unicef S / Coordination MVE Beni, Wednesday 06-11 - 2019 HIVUM room

      • There were many, about three hundred, the drivers of Mototaxi Beni invited to a day of awareness and vaccination against Ebola Virus Disease this Wednesday, November 06, 2019 in the HIVUM room.

      • This day is welcome for the city of Beni during this period of EVD epidemic which, unfortunately, displays a lethality of 86.3% among motorcyclists, as pointed out by Dr. Pierre ADIKEY, Coordinator of the response of Sub Coordination of Beni.

      • Thus, in his presentation, he focused his message on the risk of transmission of EVD among motorotaxi drivers and the conduct to be held in the exercise of their craft to protect themselves and the community.

      • He asked bikers more often to respect the measures of prevention, namely: washing hands regularly, stopping at checkpoints, not being bribed to divert checkpoints, not carrying suspicious parcels and reporting and / or direct any suspicions of illness to colleagues or the community.

      • In order to circumscribe the day, Dr. P. ADIKEY traced the path of the last Motard who died of EVD before his death confirmed at the CTE. To close his presentation, he made a reminder of the various events that prevented the teams of the response from working: among other things the days of the dead city, the fire of the vehicles of the riposte, the destruction of the structures of the care, the cases of resistance and others whose bikers were part of it.

      • Dr. Bibiche MATADY, as Epidemiologist and Chair of the Monitoring Commission, introduced to the participants the importance of accepting to be listened to if you are in contact with a case, to let yourself be followed for the entire period indicated and to orient in a management structure as soon as the first sign appears. She also emphasized the collaboration between the bikers and the teams of the response.

      • To justify this day again, one of the 3 Hikers shared his testimony and urged his colleagues to collaborate and follow the recommendations of the response teams starting with vaccination.

      • Vaccination is one of the preventive measures against EVD, said Dr Adonis TERANYA, the Chair of the Immunization Subcommission. In his presentation, he explained the evolution of the vaccination protocol, the current targets, the side effects and the action to take in the event of an adverse event. Before calling for the voluntary vaccination of participants, he spoke about vaccines currently used in the DRC.

      • In his words, the President of Bikers reiterated to the Coordinator the commitment of his organization and all its members to support the interventions of the response, while affirming its availability to any solicitation for the fight against the disease to Ebola virus in the city of Beni and its surroundings.

      • The day ended with the vaccination of 100 Bikers and some of their dependents.

      VACCINATION

      • Since vaccination began on 8 August 2018, 250,234 people have been vaccinated;
      • The only vaccine to be used in this outbreak is the rVSV-ZEBOV vaccine, manufactured by the pharmaceutical group Merck, following approval by the Ethics Committee in its decision of 20 May 2018.

      MONITORING AT ENTRY POINTS

      • Since the beginning of the epidemic, the total number of travelers checked (temperature rise) at the sanitary control points is 115,778,240 ;
      • To date, a total of 111 entry points (PoE) and sanitary control points (PoCs) have been set up in the provinces of North Kivu and Ituri to protect the country's major cities and prevent the spread of the epidemic in neighboring countries.

      As a reminder, the recommendations of the MULTISECTORAL COMMITTEE OF THE RESPONSE TO EBOLA VIRUS DISEASE are as follows:

      1. Follow basic hygiene practices, including regular hand washing with soap and water or ashes;
      2. If an acquaintance from an epidemic area comes to visit you and is ill, do not touch her and call the North Kivu Civil Protection toll-free number;
      3. If you are identified as a contact of an Ebola patient, agree to be vaccinated and followed for 21 days;
      4. If a person dies because of Ebola, follow the instructions for safe and dignified burials. It is simply a funeral method that respects funerary customs and traditions while protecting the family and community from Ebola contamination.
      5. For all health professionals, observe the hygiene measures in the health centers and declare any person with symptoms of Ebola (fever, diarrhea, vomiting, fatigue, anorexia, bleeding).<br /> If all citizens respect these health measures recommended by the Secretariat, it is possible to quickly end this 10th epidemic.
    2. On 2019-11-15 16:53:09, user GuyguyKabundi Tshima wrote:

      EVOLUTION OF THE EPIDEMIC IN THE PROVINCES OF NORTH KIVU AND ITURI AS AT NOVEMBER 13, 2019

      Thursday, November 14, 2019

      • Since the beginning of the epidemic, the cumulative number of cases is 3,292, of which 3,174 are confirmed and 118 are probable. In total, there were 2,193 deaths (2075 confirmed and 118 probable) and 1067 people cured.<br /> • 527 suspected cases under investigation;<br /> • 1 new case confirmed in North Kivu in Mabalako;<br /> • No new deaths of confirmed cases have been recorded;<br /> • No cured person has emerged from ETCs;<br /> • No health worker is among the new confirmed cases. The cumulative number of confirmed / probable cases among health workers is 163 (5% of all confirmed / probable cases), including 41 deaths;

      NEWS

      Ebola Virus Disease Response Co-ordination Announces Three Road Traffic Accident in Bunia, Ituri

      • The overall coordination of the response to the Ebola Virus Disease epidemic in North, South Kivu and Ituri was informed on Thursday 13 November 2019 of the tragic traffic accident between two motorcycles, one of which carried three agents of the riposte;<br /> • These three officers, who work for the Epidemiological Surveillance Commission at the Point of Entry and Control, were returning from Bunia to Mambasa, where they are respectively delivering;<br /> • This accident occurred around Marabo in Bunia on the evening of Wednesday 13 November 2019;<br /> • The balance sheet reports an officer who died at the scene and two others who were seriously injured, including one in a coma. The two wounded were taken to the Nyakunde Reference General Hospital in Ituri for appropriate care;<br /> • The overall coordination of the response sends its deepest condolences to the grieving family and expresses all its compassion and solidarity to the injured officers, while wishing them a quick recovery.

      Effective start of Johnson & Johnson vaccination in two Goma health areas

      • Ebola vaccination with the Ad26.ZEBOV / MVA-BN-Filo vaccine, produced by Janssen Pharmaceuticals for Johnson & Johnson, began on Thursday, November 14, 2019 in two Karisimbi health areas in Goma City , North Kivu Province;<br /> • The Epidemic Response Coordinator for Ebola Virus Disease in North, South Kivu and Ituri. For this purpose, Prof. Steve Ahuka Mundeke visited the vaccination sites to inquire about the evolution of activities in the field. He was satisfied with the work of the teams;<br /> • He took the opportunity to invite the population of the targeted areas to be vaccinated in order to protect themselves from the resurgence of the Ebola virus;<br /> • Several people were present in Majengo and Kahembe health areas to get vaccinated. The first person to be vaccinated is a Kahembe community leader who has been protected against the Ebola virus today and also in case of a possible new Ebola outbreak. This community leader has appealed to all residents of his community and sites targeted to come take this second vaccine. "This is an opportunity not to be missed, because it is said that prevention is better than cure, " he said;<br /> • The logistics of this vaccination are provided by the international non-governmental organization Médecins Sans Frontières of France (MSF / France).<br /> • Approved October 22, 2019 by the Ethics Committee of the School of Public Health of the University of Kinshasa and October 23, 2019 by the National Ethics Committee, this second vaccine, called Ad26.ZEBOV / MVA-BN -Filo , is produced by Janssen Pharmaceuticals for Johnson & Johnson;<br /> • This new vaccine complements the first, the rVSV-ZEBOV, the vaccine used until then in this epidemic. Manufactured by the pharmaceutical group Merck, after approval of the Ethics Committee on May 20, 2018, it was recently approved.

      Closing of the training workshop for media professionals in Beni on the role and responsibility of journalists during public health crises

      • The Deputy Mayor of the city of Beni, Muhindo Bakwanamaha Modeste, closed this Thursday, November 14, 2019 in Beni in the province of North Kivu the training of media professionals on the role and responsibility during public health crises;<br /> • The coordinator of the Beni Ebola Ebola response sub-coordination, Dr. Pierre Adikey, on behalf of the Coordinator-General of the Response, Prof. Steve Ahuka, wished to see these kinds of trainings be organized, not only in other sub-Coordination of the response, but also throughout the Democratic Republic of the Congo so that journalists from all over the country are ready to face any possible epidemic crisis;<br /> • This training, he said, is part of the zero-case Ebola strategy and strengthening the health system of tomorrow;<br /> • The focal point of Beni's journalists, Moustapha MULONDA, reaffirmed the commitment of journalists to combat Ebola Virus Disease through various programs and publications disseminated and published by their respective media thanks to the new tools acquired during this period. training;<br /> • This training was organized by the Ministry of Health in collaboration with the World Health Organization and benefited from the facilitation of the overall coordination of the response, UNICEF, CDC Africa and MSF.

      VACCINATION

      • Since the start of vaccination on August 8, 2018 with the rVSV-ZEBOV vaccine, 251,637 people have been vaccinated;

      • Vaccination with the second Ad26.ZEBOV / MVA-BN-Filo vaccine, produced by Janssen Pharmaceuticals for Johnson & Johnson, began on Thursday November 14, 2019 in Goma. This vaccine was approved on 22 October 2019 by the decisions of the Ethics Committee of the School of Public Health of the University of Kinshasa and 23 October 2019 of the National Ethics Committee;

      • Until then, only one vaccine was used in this outbreak. This is the rVSV-ZEBOV vaccine, manufactured by the pharmaceutical group Merck, after approval of the Ethics Committee in its decision of 20 May 2018 and which has recently been approved.

      MONITORING AT ENTRY POINTS

      • A 27-year-old woman from Butembo for Goma, an escaped suspect from Makasi Hospital in Butembo, North Kivu, was intercepted at the Kanyabayonga checkpoint in Kayna. When she was intercepted, she experienced signs such as fever at 38.4 ° C, severe asthenia, abdominal pain and vaginal bleeding. It was sent to the KAYNA Transit Center.

      • Since the beginning of the epidemic, the total number of travelers checked (temperature rise) at the sanitary control points is 116,622,388 ;

      • To date, a total of 112 entry points (PoE) and sanitary control points (PoCs) have been set up in the provinces of North Kivu and Ituri to protect the country's major cities and prevent the spread of the epidemic in neighboring countries.

      As a reminder, the recommendations of the MULTISECTORAL COMMITTEE OF THE RESPONSE TO EBOLA VIRUS DISEASE are as follows:

      1. Follow basic hygiene practices, including regular hand washing with soap and water or ashes;
      2. If an acquaintance from an epidemic area comes to visit you and is ill, do not touch her and call the North Kivu Civil Protection toll-free number;
      3. If you are identified as a contact of an Ebola patient, agree to be vaccinated and followed for 21 days;
      4. If a person dies because of Ebola, follow the instructions for safe and dignified burials. It is simply a funeral method that respects funerary customs and traditions while protecting the family and community from Ebola contamination.
      5. For all health professionals, observe the hygiene measures in the health centers and declare any person with symptoms of # Ebola (fever, diarrhea, vomiting, fatigue, anorexia, bleeding).<br /> If all citizens respect these health measures recommended by the Secretariat, it is possible to quickly end this 10th epidemic.
    3. On 2019-11-30 17:00:40, user Guyguy wrote:

      EVOLUTION OF THE EPIDEMIC IN THE PROVINCES OF NORTH KIVU AND ITURI AT NOVEMBER 27, 2019

      Thursday, November 28, 2019<br /> • Since the beginning of the epidemic, the cumulative number of cases is 3,309, of which 3,191 are confirmed and 118 are probable. In total, there were 2,201 deaths (2,083 confirmed and 118 probable) and 1077 people healed.<br /> • 443 suspected cases under investigation;<br /> • 5 new confirmed cases, including:<br /> o 4 in Ituri in Mandima;<br /> o 1 in North Kivu in Mabalako;<br /> • 2 new deaths of confirmed cases, including:<br /> o 2 new community deaths in Ituri in Mandima;<br /> o No deaths among confirmed cases in CTEs;<br /> • No cured person has emerged from CTEs;<br /> • No health worker is among the new confirmed cases. The cumulative number of confirmed / probable cases among health workers is 163 (5% of all confirmed / probable cases), including 41 deaths.

      NEWS

      Three members of the Ebola Virus Epidemic response killed during an attack in Biakato, Ituri

      • Following the attack on the sub-coordination of the Biakato response in Ituri on the night of Wednesday 27th to Thursday 28 November 2019, three members of the Ebola response teams in this sector lost their lives ;<br /> • It is a provider and a driver of the vaccination committee and another driver;<br /> • In addition to these three deaths, there are 7 wounded and 6 others with psychological disorders and extensive material damage.<br /> • A good number of these teams from Biakato were evacuated in three waves to Goma. As soon as they arrived, they were greeted by a coordination team led by Prof. Steve Ahuka, general coordinator, who also visited the wounded before going to inquire about the security conditions and accommodation of evacuees. He did not fail to comfort them.

      VACCINATION

      • The vaccination commission is in mourning. A service provider and a driver of his team were killed on the night of Wednesday 27 November 2019 following attacks at the Biakato base in Ituri;<br /> • 2nd day without vaccination activity with the 2nd J & J vaccine following the disorders initiated by young people related to the security situation in Beni;<br /> • 724 people were vaccinated, until Tuesday, November 26, 2019, with the 2nd Ad26.ZEBOV / MVA-BN-Filo vaccine (Johnson & Johnson) in the two health zones of Karisimbi in Goma;<br /> • Since the start of vaccination on August 8, 2018 with the rVSV-ZEBOV vaccine, 255,373 people have been vaccinated;<br /> • Approved October 22, 2019 by the Ethics Committee of the School of Public Health of the University of Kinshasa and October 23, 2019 by the National Ethics Committee, the second vaccine, called Ad26.ZEBOV / MVA-BN -Filo, is produced by Janssen Pharmaceuticals for Johnson & Johnson;<br /> • This new vaccine is in addition to the first, the rVSV-ZEBOV, vaccine used until then (since August 08, 2018) in this epidemic manufactured by the pharmaceutical group Merck, after approval of the Ethics Committee on May 20, 2018. has recently been pre-qualified for registration.

      MONITORING AT ENTRY POINTS

      • Sanitary control activities are disrupted in the towns of Beni and Butembo in North Kivu province following demonstrations by the population which decries killings of civilians;<br /> • Since the beginning of the epidemic, the total number of travelers checked (temperature measurement ) at the sanitary control points is 121,159,810 ;<br /> • To date, a total of 109 entry points (PoE) and sanitary control points (PoCs) have been set up in the provinces of North Kivu and Ituri to protect the country's major cities and prevent the spread of the epidemic in neighboring countries.

      As a reminder, the recommendations of the MULTISECTORAL COMMITTEE OF THE RESPONSE TO EBOLA VIRUS DISEASE are as follows:

      1. Follow basic hygiene practices, including regular hand washing with soap and water or ashes;
      2. If an acquaintance from an epidemic area comes to visit you and is ill, do not touch her and call the North Kivu Civil Protection toll-free number;
      3. If you are identified as a contact of an Ebola patient, agree to be vaccinated and followed for 21 days;
      4. If a person dies because of Ebola, follow the instructions for safe and dignified burials. It is simply a funeral method that respects funerary customs and traditions while protecting the family and community from Ebola contamination.
      5. For all health professionals, observe the hygiene measures in the health centers and declare any person with symptoms of # Ebola (fever, diarrhea, vomiting, fatigue, anorexia, bleeding).<br /> If all citizens respect these health measures recommended by the Secretariat, it is possible to quickly end this 10th epidemic.
    1. On 2021-10-10 03:44:51, user kdrl nakle wrote:

      The results on T-cells is quite murky here, without much explanation. You really need a bigger sample to be able to see this better, your sample of 46 is too small.

    1. On 2020-01-25 23:16:22, user White_Runner wrote:

      There is another study that puts the R0 value in 1.4 to 2.6, still very high.<br /> However not apocaliptic levels like the one described here.<br /> Also, this R0 value can change in time as long as the adequate restrictions are taken in place.<br /> So, yeah guys, expect the best, have some precautions and happy lunar new year.

    1. On 2022-01-28 20:26:41, user Hussein Turfe wrote:

      Was there any relation found between those who had THC in their urine and coming into the ED stating that they had a suicidal ideation?

    2. On 2022-01-28 20:33:22, user Mohamad Kabbani wrote:

      Fantastic article! Very informative and the ideas are easy to understand. This is a good baseline to get a better understanding on how different things have become during and after covid-19. We can learn what a pandemic can do to a population and compare it to this data as a reference point.

    1. On 2020-03-22 15:56:46, user Sinai Immunol Review Project wrote:

      Main findings<br /> The authors characterized the immune response in peripheral blood of a 47-year old COVID-19 patient. <br /> SARS-CoV2 was detected in nasopharyngeal swab, sputum and faeces samples, but not in urine, rectal swab, whole blood or throat swab. 7 days after symptom onset, the nasopharyngeal swab test turned negative, at day 10 the radiography infiltrates were cleared and at day 13 the patient became asymptomatic.

      Immunofluorescence staining shows from day 7 the presence of COVID-19-binding IgG and IgM antibodies in plasma, that increase until day 20. <br /> Flow cytometry on whole blood reveals a plasmablast peak at day 8, a gradual increase in T follicular helper cells, stable HLA-DR+ NK frequencies and decreased monocyte frequencies compared to healthy counterparts. The expression of CD38 and HLA-DR peaked on T cells at D9 and was associated with higher production of cytotoxic mediators by CD8+ T cells.<br /> IL-6 and IL-8 were undetectable in plasma.<br /> The authors further highlight the presence of the IFITM3 SNP-rs12252-C/C variant in this patient, which is associated with higher susceptibility to influenza virus.

      Limitations of the study<br /> These results need to be confirmed in additional patients.<br /> COVID-19 patients have increased infiltration of macrophages in their lungs{1}. Monitoring monocyte proportions in blood earlier in the disease might help to evaluate their eventual migration to the lungs.<br /> The stable concentration of HLA-DR+ NK cells in blood from day 7 is not sufficient to rule out NK cell activation upon SARS-CoV2 infection. In response to influenza A virus, NK cells express higher levels of activation markers CD69 and CD38, proliferate better and display higher cytotoxicity{2}. Assessing these parameters in COVID-19 patients is required to better understand NK cell role in clearing this infection. <br /> Neutralization potential of the COVID-19-binding IgG and IgM antibodies should be assessed in future studies.<br /> This patient was able to clear the virus, while presenting a SNP associated with severe outcome following influenza infection. The association between this SNP and outcome<br /> upon SARS-CoV2 infection should be further investigated.

      Relevance<br /> This study is among the first to describe the appearance of COVID-19-binding IgG and IgM antibodies upon infection. The emergence of new serological assays might contribute to monitor more precisely the seroconversion kinetics of COVID-19 patients{3}. Further association studies between IFITM3 SNP-rs12252-C/C variant and clinical data might help to refine the COVID-19 outcome prediction tools.

      References<br /> 1. Liao, M. et al. The landscape of lung bronchoalveolar immune cells in COVID-19 revealed by single-cell RNA sequencing. http://medrxiv.org/lookup/d... (2020) doi:10.1101/2020.02.23.20026690.<br /> 2. Scharenberg, M. et al. Influenza A Virus Infection Induces Hyperresponsiveness in Human Lung Tissue-Resident and Peripheral Blood NK Cells. Front. Immunol. 10, 1116 (2019).<br /> 3. Amanat, F. et al. A serological assay to detect SARS-CoV-2 seroconversion in humans. http://medrxiv.org/lookup/d... (2020) doi:10.1101/2020.03.17.20037713.

      Review by Bérengère Salomé as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn school of medicine, Mount Sinai

    1. On 2021-10-29 15:37:00, user Rogerblack wrote:

      I find refreshing the repeated ''these associations did not survive correction for multiple comparisons'.<br /> An interesting paper.

    1. On 2020-05-20 16:50:49, user Peter Ellis wrote:

      Table 1 presents the data, showing 40 positive tests and 689 negative tests, i.e. an average prevalence of 5.49% across the course of the study. Elsewhere in the manuscript, the sensitivity is given as 100% (meaning none were missed) and the specificity as 98.3% (meaning there is a 1.7% false positive rate.

      This being the case, can the authors please explain:

      1) Why the caption for Table 1 reports 789 patients given that 40 + 689 = 729?

      2) How they adjusted for false positives. 40 / 729 = 5.49%, which minus the 1.7% false positive rate leaves around 3.79% positive across the course of the whole study.<br /> [A Bayesian adjustment would be more accurate, this will suffice for now]

      3) Given that the true positive rate in the samples they measured is around 3.79% across the whole study, how do they calculate a population prevalence of 4.6% at the start, rising to 7.1% at the end of the study. The methodology for this is entirely lacking.

    1. On 2020-04-22 02:20:27, user Mike wrote:

      This was certainly an interesting paper. It's done a lot of work and the findings are notable. IMHO it warrants as much attention as the pro-HCQ study via Dr. Raoult. While it is entertaining, I will add that it is not conclusive, nor without fault. A double-blind study is still required, but it is worth the read.

      Observations/Questions:

      1. "hydroxychloroquine, with or without azithromycin, was more likely to be prescribed to patients with more severe disease”<br /> 2. "we cannot rule out the possibility of selection bias or residual confounding”<br /> 3. demographic: 100% male, 66% black, median age ~70 (59 youngest)<br /> 4. uses PSM, which despite a common practice, could be considered controversial (https://gking.harvard.edu/f... "https://gking.harvard.edu/files/gking/files/psnot.pdf)")<br /> 5. Unless I missed it, I didn't see any specifics about how the treatments were administered.<br /> - How long before death were patients treated? <br /> - What was the quantity/frequency of the treatments? <br /> - Were the treatments consistent between hospitals?<br /> 6. The rate of ventilation was less in HC+AZ (half of the HC and no-HC rates). Why was that and what does that suggest?<br /> 7. Although they were statistically insignificant, what was the result of the 17 women not included in the study?<br /> 8. Why does the paper seem to address political points? It seems like the Abstract is editorialized, which I'm not accustomed to. The Conclusions portion (and page after) seeming to address topical issues of the times. Perhaps this introduces my own subjective bias, but I infer potential for analysis/deciphering bias when the study shows awareness of other controversial studies being conducted, rather than being a standalone independent study of its own; essentially, it leaves me to question motivations of the author, rather than that motivation being scientific discovery. I don't mind such commentary in the Discussion section, I'm just not as accustomed to seeing it in the Abstract.

    1. On 2022-10-28 07:00:48, user Sujoy Ghosh wrote:

      This manuscript has now been published as follows: <br /> Ghosh, S., Roy, S.S. Global-scale modeling of early factors and <br /> country-specific trajectories of COVID-19 incidence: a cross-sectional <br /> study of the first 6 months of the pandemic.<br /> BMC Public Health 22, 1919 (2022). https://doi.org/10.1186/s12...

      Kindly update the link in medrxiv. Regards, Sujoy Ghosh

    1. On 2022-11-07 06:03:04, user Daniel Corcos wrote:

      I don't see any adjustment for the date of infection. There is a high probability that nirmatrelvir treatment was used on average at a different time, against infections with a different ratio of viral variants.

    1. On 2022-12-12 06:18:02, user Stephanie Byrne wrote:

      This article has been accepted for publication in the International Journal of Epidemiology, published by Oxford University Press. A DOI and link to the published article will be available soon.

    1. On 2022-12-29 19:11:31, user tshann wrote:

      Given the stated benefits of these vaccines, why are we doing modeling studies rather than real RCT's. It's been over 2 years with these products, when will we see the science instead of more modeling studies?

    1. On 2023-11-21 02:18:06, user Marco Confalonieri wrote:

      The finding that high glucose levels can predict glucorticoids (GCs) benefit surprised most of us. All we who performed the included RCTs thincked to hyperglycemia as an adverse effect of GCs, not paying attention to glucose blood level at admission. Nevertheless, there are several reports pointing out hyperglycemia but not diabetes alone associated with increased in-hospital mortality in community-acquired pneumonia (BMJ Open Diab Res Care 2022;10:e002880). It should be noted that AI doesn't have the same prejudices than human researchers.

    1. On 2020-04-16 00:27:24, user Adam Danischewski wrote:

      China has a BCG Vaccination policy and there may be other aspects that may cause Chinese results to differ from the United States.

    1. On 2024-10-16 16:27:20, user CDSL JHSPH wrote:

      I quite enjoyed this article. I found it very interesting as it proposed significant thoughts to how we can improve antibiotic treatment. I wanted to comment about some of the thoughts I had while reading this article. I first wanted to see if the results that were found in TB could be translated into other bacteria infections, such as staph. or strep. species. I also wanted to see if the results found for antibiotics in this article could be translated to other pathogenic treatments including antivirals or antifungals. Finally, in terms of future approaches could we see a systemic or ordered approach when it came to treatment duration whether bacterial, viral, or fungal in nature, or is it mostly going to be drug/ species specific?

    2. On 2024-10-23 00:04:57, user Mohammad Shah wrote:

      Hello!

      Thank you for sharing this preprint. I really enjoyed reading it. Your application of techniques like MCP-Mod and FP for duration-ranging trials provides valuable insights into detecting duration-response relationships much more effectively than traditional approaches. I also appreciate how you highlight the risks of underestimating the MED in smaller sample sizes and suggest using conservative thresholds to mitigate those risks—this is such a critical point.

      One thing that really stood out to me was how you clearly lay out the limitations of traditional duration-response methods, while proposing model-based techniques, like MCP-Mod, as a better alternative. Your comparison of different models and how they behave with varying sample sizes and regimen responses is especially insightful for optimizing TB treatment duration.

      Like others have mentioned, it’d be fascinating to see how this approach could be applied to other chronic diseases, such as HIV or hepatitis. Is that something you’re considering or perhaps already working on? Additionally, applying these model-based techniques to real-world patient data, where comorbidities and adherence issues add more complexity, seems like a natural next step. It would be interesting to see how that plays out in practice.

      I also found your discussion on model selection particularly thought-provoking. Your suggestion of using MCP-Mod alongside Fractional Polynomials under different assumptions opens up an exciting possibility for integrating multi-model approaches in early-phase trials. I wonder if combining these models, maybe in a hybrid MCP-Mod/FP approach, could improve adaptability, especially in trials with more heterogeneous patient populations—those with comorbidities or fluctuating adherence, for example.

      Lastly, your use of simulations to predict treatment efficacy in the face of sample size imbalances touches on a key challenge in trial design. Have you thought about how this framework might be extended to adaptive trial designs? It seems like interim analyses could help adjust treatment durations dynamically based on early patient responses, which could make trials even more efficient.

      Overall, this was a great article, very informative and forward-thinking!

    1. On 2020-05-01 10:56:16, user Ivan Berlin wrote:

      Rentsch CT et al. Covid-19 Testing, Hospital Admission, and Intensive Care Among 2,026,227 United States Veterans Aged 54-75 Years. <br /> medRxiv preprint doi: https://doi.org/10.1101/202... version posted April 14, 2020<br /> Comment of the results concerning smoking related issues. Corrected Version. Please ignore the previous one.<br /> Ivan Berlin, Paris, France<br /> The title is somewhat misleading. Only 3789 persons were tested for SARS-CoV-2, no data on the 2,022,438 are reported.<br /> Data are extracted from the Veteran Administration (USA) Birth Cohort born between 1945 and 1965 electronic database. Between February 8 and March 30, 2020, 3789 persons were tested for SARS-CoV-2. Among them 585 were tested SARS-CoV-2 positive (15.4%) and 3204 SARS-CoV-2 negative. (Remark: the authors frequently confound testing for SARS-CoV-2 and having the disease: COVID-19 +.)<br /> Testing used nasopharyngeal swabs, 1% of the testing samples was from other unspecified sources. Testing was performed “in VA state public health and commercial reference laboratoires”, page 7. No further specification about the testing method is provided. Data are analyzed as if no between test-sources variability existed. However, it is unlikely that between test-source variability would influence the findings.<br /> It seems that only individuals with symptoms were tested, however this is not clearly stated.<br /> Data extraction included diagnostics by diagnostic codes of comorbidities, non-steroid inflammatory drug (NSAID), angiotensin converting enzyme inhibitor (ACE) and angiotensin II receptor blocker (ARB) use, vital signs, laboratory results, hepatic fibrosis score, presence or absence of alcohol use disorder and smoking status.<br /> Smoking status data, never, former, current smokers were extracted using the algorithm described in McGinnis et al. Validating Smoking Data From the Veteran’s Affairs Health Factors Dataset, an Electronic Data Source. Nicotine & Tobacco Research, Volume 13, Issue 12, December 2011, Pages 1233–1239, https://doi.org/10.1093/ntr... used for HIV patients. According to this paper, the algorithm correctly classified 84% of never-smokers 95% of current smokers but only 43% of former smokers. The reported overall kappa statistic was 0.66. When categories were collapsed into ever/never, the kappa statistic was somewhat better: 0.72 (sensitivity = 91%; specificity = 84%), and for current/not current, 0.75 (sensitivity = 95%; specificity = 79%). Thus, classification error cannot be excluded in particular in classifying former smokers. <br /> In unadjusted analyses (Table 1) factors associated significantly with SARS-CoV-2 positivity were: male sex, black race, urban residence, chronic kidney disease, diabetes, hypertension, higher body mass index, vital signs but not NSAID or ACE/ARB exposure. It is to note, that among the laboratory findings, severity of hepatic fibrosis was associated with positive SARS-CoV-2 tests. <br /> Among those with positive SARS-CoV2 alcohol use disorder was reported by 48/585 (8.2%), versus 480/3204 (15%) among those with negative SARS-CoV-2 test. Among those with alcohol use disorder, 9.1 tested positive. <br /> Among SARS-CoV-2 positives there were 216/585 (36.9%) never smokers vs 826/3204 (25.8%) among SARS-CoV-2 negatives. 20.7% tested positive among never smokers. Among SARS-CoV-2 positive persons 179 (30.6%) were former smokers vs 704 (22%) among SARS-CoV-2 negatives. 20.3 % tested positive among former smokers. Among SARS-CoV-2 positive individuals 159 (27.7%) were current smokers vs 1444 (45.1%) among SARS-CoV-2 negative individuals. 9.9% tested positive among current smokers. Expressed otherwise, among SARS-CoV-2 negative individuals, there were less never smokers, less former smokers and more current smokers. Among individuals with SARS-CoV-2 positivity there were 338/585 (61%) persons with smoking history (former + current smokers=ever smokers) and among those with SARS-CoV-2 negativity 2149/3204 (72%) were ever smokers. <br /> COPD, known to be strongly related to former or current smoking, was more frequent among SARS-CoV-2 negative (28.2%) than among SARS-CoV-2 positive (15.4%) individuals.<br /> In multivariable analyses (Table 2), male sex, black ethnicity, urban residence, lower systolic blood pressure, prior use of NSAID but not ACE/ARB use and obesity were associated with SARS-CoV-2 positive test; current smoking (OR: 0.45, 91% CI: 0.35-057), alcohol use disorder (OR 0.58, 95% CI: 0.41-0.83) and COPD (OR: 0.67, 95%CI: 0.50-0.88) were associated with decreased likelihood of SARS-CoV-2 positive test. No association with age and SARS-CoV-2 positive test was observed. The association with hepatic fibrosis with SARS-CoV-2 positive tests remained significant in the multivariable analysis and the authors point out (page 15) that the “pronounced independent association with FIB-4 (fibrosis) and albumin suggest that virally induced haptic inflammation may be a harbinger of the cytokine storm.”, page 15. <br /> The main risk factors for hospitalization or ICU among SARS-CoV-2 positive persons are those that associated with worse clinical signs (status). This is expected: clinical decision about severity is based on current clinical signs and not on previous history. <br /> Neither co-morbidities, nor smoking status or alcohol use disorder were associated with hospitalization/ICU. Surprisingly, age was inversely associated with hospitalization (Table 4) among SARS-CoV-2 positive individuals.<br /> Conclusion

      To the best of our knowledge, this is the first report showing that there are less current smokers among SARS-CoV-2 positive persons. However, looking at smoking history (former + current smoking=ever smokers), less subject of classification bias, the difference seems to be less. It is not known what is the percent of former smokers who were recent quitters; duration of previous abstinence from smoking is a crucial variable in assessing associations with smoking status. There is no report of biochemical verification of smoking status. <br /> It is not known when smoking status is reported with respect of the SARS-CoV-2 testing. It is likely that individuals with clinical symptoms stopped smoking some days before testing and considered themselves as former smokers.

      The fact that alcohol use disorder, which is frequently associated with tobacco use disorder, is also less frequent among SARS-CoV-2 positive individuals raises the question of the specificity of the smoking finding and raises the contribution of substance use disorders overall i.e. the finding about current smoking is part of a cluster of various previous or current substance use disorders e.g. cannabis use, potentially associated with SARS-CoV-2 negative test directly or through associated health disorders e.g. hepatic disorders as a consequence of alcohol use. <br /> COPD as well as current smoking are being reported to be more frequent among SARS-CoV-2 negative individuals raising the possibility that reduced respiratory function (entry of SARS-CoV-2 is by the respiratory tract) is associated with lower likelihood of SARS-CoV-2 positive tests. <br /> It seems that all individuals included were tested because they had symptoms suggestive of COVID-19. It is surprising that only 585/3789 (15.4%) tested positive. This should be discussed.<br /> The paper does not report on analyses of smoking by clinical signs/co-morbidities interactions. It is likely that former smokers or those with alcohol use disorders are more frequent among individuals with comorbidities. Based on previous knowledge about smoking associated health disorders, one can assume that more severe clinical signs were associated with current smoking or among recent quitters; the smoking x clinical signs interaction is not tested. <br /> The authors conclude on page 14 “To wit, we found that current smoking, COPD, and alcohol use disorder, factors that generally increase risk of pneumonia, were associated with decreased probability of testing positive. While they were not associated with hospitalization or intensive care, it is too early to tell if these factors are associated with subsequent outcomes such as respiratory failure or mortality.”<br /> The reduced current smoking rate among SARS-CoV-2 positive individuals is an interesting but preliminary finding. It is likely that it is part of a more complex symptomatology and not specific to current smoking. Smoking status should have been assessed on a more detailed manner. The current findings, from a retrospective, cross sectional analysis, are insufficient to support the hypothesis that current smoking protects against SARS-CoV-2 positivity.

    1. On 2020-04-16 21:17:49, user Sinai Immunol Review Project wrote:

      Key findings:

      The authors wanted to better understand the dynamics of production SARS-CoV-2-specific IgM and IgG in COVID-19 pneumonia and the correlation of virus-specific antibody levels to disease outcome in a case-control study paired by age. The retrospective study included 116 hospitalized patients with COVID-19 pneumonia and with SAR-CoV-2 specific serum IgM and IgG detected. From the study cohort, 15 cases died. SARS-CoV-2 specific IgG levels increased over 8 weeks after onset of COVID-19 pneumonia, while SARS-CoV-2 specific IgM levels peaked at 4 weeks. SARS-CoV-2 specific IgM levels were higher in the deceased group, and correlated positively with the IgG levels and increased leucocyte count in this group, a indication of severe inflammation. IgM levels correlated negatively with clinical outcome and with albumin levels. The authors suggest that IgM levels could be assessed to predict clinical outcome.

      Potential limitations:

      There are limitations that should be taken into account. First, the sample: small size, patients from a single-center and already critically ill when they were admitted. Second, the authors compared serum IgM levels in deceased patients and mild-moderate patients and found that the levels were higher in deceased group, however even if the difference is statistically significant the number of patients in the two groups was very different. Moreover, receiving operating characteritics (ROC) curves were used to evaluate IgM and IgG as potential predictors for clinical outcome. Given the low number of cases, specially in the deceased group, it remains to be confirmed if IgM levels could be predictive of worst outcome in patients with COVID-19 pneumonia. The study did not explore the role of SARS-CoV-2-specific IgM and IgG in COVID-19 pneumonia.

      Overall relevance for the field:

      Some results of this study have been supported by subsequent studies that show that older age and patients who have comorbidities are more likely to develop a more severe clinical course with COVID-19, and severe SARS-CoV-2 may trigger an exaggerated immune response. The study seems to demonstrate that the increase of SARS-CoV-2-specific IgM could indicate poor outcome in patients with COVID-19 pneumonia, however given the very small sample size, the results are not yet conclusive.

      Review by Meriem Belabed as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn school of medicine, Mount Sinai.

    1. On 2020-04-16 22:12:24, user Amy E. Herr wrote:

      During the COVID-19 pandemic, we are grateful for the authors’ urgency in assessing N95 respirator decontamination methods. It is in this spirit of collegiality that we draw attention to an aspect that could (unintentionally) cause confusion: the PS19Q thermopile sensor mentioned in the Methods section does not appear to be suited to detect the virus-killing UV-C light emitted from the source. The authors are aware of the possible confusion and are working diligently to check into and, if needed, address the concern.

      As background: from the manufacturer’s specifications, the PS19Q thermopile sensor mentioned in the preprint appears to only detect wavelengths as low as 300 nm, which is above the UV-C germicidal wavelength range (<280 nm). Low-pressure mercury UVGI bulbs emit a 253.7 nm peak [EPA]. 260 nm is the peak UV-C germicidal wavelength for inactivating virus via DNA and RNA damage [Kowalski et al., 2009, Ito and Ito, 1986]. The germicidal efficacy arises primarily from the UV-C dose, with the UV-B dose (280-320 nm) providing significantly lower germicidal efficacy. At 300 nm, UV light is ~10x less effective at killing pathogens than at 254 nm [Lytle and Sagripanti 2005]. UV-A dose (320-400 nm) is considered minimally germicidal [Kowalski et al., 2009; Lytle and Sagripanti 2005; EPA]. We are concerned about the potential adverse health outcomes that might stem from use of the PS19Q thermopile sensor not matched to the UVGI wavelengths for N95 FFR decontamination.

      As best practices, all researchers working on UV-C methods are encouraged to use a calibrated, NIST-traceable, UV-C-specific radiometer to report not just UV-C irradiance, but also UV-C specific dose, as a minimally acceptable UV-C dose of 1.0 J/cm^2 is sought on all N95 FFR surfaces. For additional detail from the peer-reviewed literature, please see the 2020 scientific consensus summaries on N95 FFR decontamination at: n95decon.org

      Again, we thank the authors for their timely research and quick action to confirm suitability of their experimental design, all of which aim to better inform decision makers working to protect the health of heroic front-line healthcare professionals during the COVID-19 pandemic.

      References cited: <br /> • Manufacturer’s specifications, the PS19Q thermopile sensor: https://www.coherent.com/me...<br /> • EPA: ULTRAVIOLET DISINFECTION GUIDANCE MANUAL FOR THE FINAL LONG TERM 2 ENHANCED SURFACE WATER TREATMENT RULE: https://nepis.epa.gov/Exe/Z...<br /> • Kowalski et al., 2009: https://link.springer.com/c...<br /> • Ito and Ito, 1986: https://onlinelibrary.wiley...<br /> • Lytle and Sagripanti 2005: https://www.ncbi.nlm.nih.go...

    1. On 2022-01-26 22:15:44, user Siguna Mueller, PhD, PhD wrote:

      Does the "fully vaccinated" group ALWAYS include those with (partial) natural immunity (i.e., those previously infected? This is at least what Table 1 says: these belong into the same group. Yet, throughout, this group is referred to as the "fully vaccinated." This does not seem to affect the conclusion that vaccination is in large part responsible for driving O's increased transmissibility (because the incr. OR is seen for the booster group as well). Apart from this, I am struggling to see how the other results are obtained. I seem to be missing how the factor of previously infection gets incorporated in the study. It would be helpful if this could be made explicit, please. Thanks!

    1. On 2022-02-03 17:13:18, user Brian R Wood wrote:

      Has the study accounted for the fact that if Omicron has less severe symptoms than Delta or COVID-19 Classic, the number of reported infections is likely to be significantly lower? Additionally, I would speculate that those who got vaccinated and boosted are also more likely to be tested than those who did not, but just speculation, no data to back it up.

    1. On 2022-02-08 21:10:34, user Sara wrote:

      Thank you for your comment, unfortunately, I did not receive your comment once you replied. 1- we are in the era in the big data, more projects are aimed at generation of large cohort that we can depend upon to derive our clinical decision. <br /> The analysis used the data from US, the model will be deployed and can be used after that to predict the survival time of small cohorts. <br /> 2- We investigated the hazards assumption, we agree with you, we should add the results in the manuscript<br /> 3- SEER database identify the surgery as the surgical removal of the tumour.<br /> 4- I agree with you on the grade, it was on the old grading system for glioblastoma which is mentioned on SEER guidelines. Updated version will be posted and will update the analysis removing this one<br /> 5- we agree with you, we will change it in the updated comments<br /> 6- It is not insane! Developing models that consider these cases is a challenge. These models will be deployed for survival prediction of different cases of glioblastoma with different survival times.

      7- we are developing a model that can be used for the routine data "we use", in this case US cancer data. We have a model that performed well so it can be deployed in the future for the clinical use for our routine data. the model is trained on large sample size that we believe it will achieve accurate prediction results for any routine data. The deployment of the model and its use in clinical practice is the goal. I hope you see the full picture.

      Thank you for your comments.

    1. On 2022-02-09 01:07:23, user Avi Bitterman wrote:

      This paper dichotomizes a continuous variable to get a barely statistically significant result (P=0.044). But this is just dichotomania. Time to treatment is a continuous variable, not a binary variable. The appropriate test for this continuous variable is a regression along the continuous variable. Not a dichotomized sub-group analysis.

      Using the same numbers this author uses from Table 1, we ran a regression which failed to show a significant effect of treatment delay on outcome P=0.13

      Aside from being the appropriate test, another advantage of a regression here is it avoids the possibility selective dichotomization along the proposed moderator variable to get the desired result (a barely significant P value the authors just so happen to have found).

      I would also be happy to have a discussion with the authors to elaborate on the above as well as discuss numerous other critical errors with this analysis as well.

    2. On 2021-06-23 21:55:50, user David Wiseman PhD wrote:

      Summary:<br /> Regarding the continued and unnecessary confusion related to the Argoaic and Artuli comments.<br /> 1. These are in reality distractions from the central issue that the original NEJM paper remains uncorrected in NEJM as to shipping times. Although a secondary issue, also uncorrected is the "days" nomenclature that is the reason for confusion in the Argoaic and Artuli comments on this forum. Also uncorrected in the original paper is the exposure risk definition which were informed were also incorrect. Together, these issues controvert the conclusions of the original study.<br /> 2. The incorrect nomenclature for "days" in the NEJM paper as well as in a follow up work (Clin Infect Dis, Nicol et al.) inflates the number of "elapsed time" days. This has not been corrected by the original authors. We on the other hand have corrected this by providing the correct information in our preprint.<br /> 3. Dr. Argoaic seems to have been given a wrong and earlier version (10/26) of the data which, although contains a variable that is supposed to correct the above problem, does not. In fact one cannot come to any conclusion that there is a discrepancy based on this incorrect 10/26 version, unless you have some preconceived notion.<br /> 4. Other post hoc analyses reported in follow up works (including social media) by the original authors looking at time from last exposure, or using a pooled placebo group, although flawed for a several reasons, when examined closely, nonetheless support our conclusions that early PEP prophylaxis with HCQ is associated with a reduction of C19.

      Detail:<br /> Any confusion about "days" would disappear once the original authors correct the NEJM June 2020 paper as well as a follow up letter in Dec 2020 Clin Infect Dis (see upper red graph in Nicol et al. pubmed.ncbi.nlm.nih.gov/332... "pubmed.ncbi.nlm.nih.gov/33274360/)"). These errors inflate the "DAYS" by 1 day because the nomenclature for describing "days" was incorrect. As far as we know those corrections have not been made in the journals where these errors appear and in a way that can be retrieved in pubmed etc..

      As far as we can tell, anyone who has cited the NEJM paper (NIH guidelines, NEJM editorial, many meta-anlayses etc., our protocol in preprint version) also misunderstood the "days" to mean the inflated figure. So the authors need to correct this. As far as we know we are the only ones to do this. After we were informed of this error by the PI (who was unaware of the problem himself) we described this problem very clearly in our preprint, distinguishing between elapsed time and the day on which a study event occurred. For the benefit of those who remain confused, we will endeavor to make it even clearer in a future version. You can read our correspondence log referenced in the preprint to verify that the incorrect "days" nomenclature was unknown to the PI, at least until 10/27 when he informed us about it.

      You are confusing "DAY ON which an event occurred" with "DAYS FROM when an event occurred." For example the original NEJM Table 1 says "1 day, 2 days etc." for "Time from exposure to enrollment". This falsely inflates the number of elapsed time days by 1, and as the authors informed us (documented in our preprint), this really means DAY ON which enrollment occurred, with Day 1 = day of exposure, so you need to subtract 1 from the days to get elapsed time FROM exposure. The same error is repeated in Nicol et al. (note: we discuss other unrelated issues relating to time estimates in our preprint).

      To confuse matters further, the problem is not even corrected in the dataset linked (datestamp 10/26/20) in the Argoaic comment. In column FS there is a variable "exposure_days_to_drugstart." This appears to indicate elapsed time (ie DAYS FROM) when it actually means the "DAY ON" nomenclature. We were only informed of the nomenclature error on 10/27/20 and later provided with a new version of the dataset on 10/30 where an additional variable "Exposure_to_DrugStart" (column GR) was provided that corrects this error by subtracting 1 from all the values.

      Why the Argoaic comment does not link to the correct 10/30 version is unclear, but in this incorrect 10/26 version, the values for the new variable "Exposure_to_DrugStart" (column GR) are IDENTICAL to those in the "exposure_days_to_drugstart" (column FS) variable (they should be smaller by 1). Accordingly, unless Drs. Argoaic and Artuli had a preconceived notion (without checking the data) that some alteration had occurred, it is impossible to draw such a conclusion (albeit one that is incorrect for other reasons) from this incorrect 10/26 dataset. A number of colleagues have downloaded the 10/26 dataset from the link provided in the Agoraic comment, and have verified this problem.

      So in addition to the original data set released in August 2020, as well as the three revisions (9/9, 10/6 and 10/30) we describe in our preprint there is this incorrect 10/26 version. I don't know how many people this affects but it would be appropriate for them to be notified that the version they have may be an incorrect one. An announcement on the dataset signup page covidpep.umn.edu/data would also be in order (nothing there today).

      Regarding the possibly higher placebo rate of C19 on numbered day 4 (18.9%). This is matched by a commensurate change in its respective treatment arm, yielding RR=0.624 similar to that for numbered days 2 (0.578) and 3 (0.624), justifying pooling. We don't know if the 18.9% represents normal variation or has biological meaning.

      Although they used enrollment time data (completely irrelevant to considering whether or not early prophylaxis is beneficial), the original authors (Nicol et al.) in a post hoc analysis, used a pooled placebo cohort to compare daily event rates (red bar graph). This would mitigate possible effects of an outlying value in the placebo cohort. We applied this same pooled placebo method to the data that correctly takes into account shipping times. This method is still limited because it may obscure a poorly understood relationship between time and development of Covid-19. Although at best this would be considered a sensitivity analysis, we did it to answer the Artuli question. This approach yields the same trends as our primary analysis. Using 1-3 days elapsed time of intervention lag (numbered days 2-4) for Early prophylaxis, there is a 33% reduction trend in Covid-19 associated with HCQ (RR 0.67 p=0.12). Taking only 1-2 days elapsed time intervention lag, we obtain a 43% reduction trend (RR 0.57 p=0.09). This analysis appears to reveal a strong regression line (p=0.033) of Covid-19 reduction and intervention lag.

      We also looked at the post hoc analysis provided by the original authors (Nicol et al.) that used “Days from Last Exposure to Study Drug Start,” a variable not previously described in the publication, protocol or dataset, so we have no way of verifying it from the raw data. As in a similar PEP study (Barnabas et al. Ann Int Med) this variable has limited (or no) value, as we are trying to treat as quickly as possible from highest risk exposure, not an event (ie Last Exposure) that occurs at an undefined time later. (even the use of highest risk exposure has some limitation, which the authors pointed out to us and which we discuss in our preprint). Further the Nicol analysis used a modified ITT cohort, rather than the originally reported ITT cohort. with these limitations, pooling data for days 1-3 and comparing with the pooled placebo cohort (yields a trend reduction in C19 associated with HCQ (it is unclear which "days" nomenclature is used) after last exposure from 15.2% to 11.2% (RR 0.74, p=0.179).

      Taken together with these "sensitivity" analyses inspired by the original authors' methodology, suggests that this is not an artifact of subgroup analysis. It could be said that any conclusions made by the sort of analyses conducted by Nicol are equally prone to the "subgroup artifact" problem. (also note that in our paper, the demographics for placebo and treatment arms in the early cohort match well).

      Mention has been made elsewhere of two other PEP studies (Mitja, Barnabas) which concluded no effect of HCQ. It is important to note that the doses used in these studies were much lower than those used in the Boulware et al. NEJM study. Further, according to the PK modelling of the Boulware group (Al-Kofahi et al.) these doses would not have been expected to be efficacious (the Barnabas study used no substantial loading dose). So citing the Mitja and Barnabas studies to support claims of HCQ inefficacy in the Boulware et al paper is unjustified. On the contrary, taken together three studies suggest a dose-response effect. We discuss this in detail in our preprint.

      Lastly it is important to note the since the original NEJM study was terminated early, the entire original analysis can be thought of as a subgroup analysis, with all of the attendant problems referenced by the original authors (and us). There is certainly a great deal of under powering and propensity to Type 2 errors, among the issues inherent in a pragmatic study design. The study was not powered as an equivalence study and so no definitive statement can be made that the HCQ is not efficacious. Along with the still uncorrected (in the original journal) issues of shipping times, "days" nomenclature and exposure risk definitions, there are are certainly many efficacy signals that oppugn the original study conclusions,and controvert the statement made in a UMN press release (covidpep.umn.edu/updates) "covidpep.umn.edu/updates)") that the study provided a "conclusive" answer as to the efficacy of HCQ.

      _________________<br /> Please note that despite our offer to Dr. Argoaic to contact us directly to walk though the data to try to identify any issues, we have not been contacted.That offer is still extended to anyone who remains confused. We have also attempted to locate both Drs. Argoaic and Artuli to try to clear up their confusion, but these names do not exist in the mainstream literature (i.e pubmed, medrxiv), nor do they appear to have any kind of internet footprint.

      With regard to Table 1 of our preprint, the reason why there are no patients for “Day 1” is that there were no patients who received drug the same day as their high-risk exposure. This is consistent with the PIs comment on 8/25/20 (p10 of email log) (at a time when he thought that there was a “Day zero”) “Exposure time was a calculated variable based date of screening survey vs. data of high risk exposure. Same day would be zero. (Based on test turnaround time, I don’t think anyone was zero days).”

      We notice an obvious typo in the heading for the second column of our Table 1, which says “To”. But it should say “nPos”, to match the 5th column (and other tables). It is patently absurd that there should be a category of “1 to 0” days or “7 to 5” days etc. “From” makes no sense either and these typos have absolutely no effect on the analysis, interpretation or conclusions. This will be corrected in a later version.

    1. On 2022-02-09 11:30:32, user Felix Schlichter wrote:

      The authors explain that the data was gathered from community testing. They further note that mass testing has been available to "Dutch citizens experiencing COVID-19 like symptoms or who have been in contact with someone testing positive for SARS-CoV-2".

      If one assumes that the inmune status affects the intensity and probability of exhibiting symptoms, wouldn't the sample be biased? Even if the real odds of being positive for individuals with primary vaccionation and booster were equal, the ones with booster would be underrepresented as they would not test as often if they tend to exhibit less symptoms. Is this not a limitation of the study?

      Could the authors not show the results separated by the reason for testing (contact vs symptomatic) to account for this limitation? if the reason for testing was having been a contact, this limitation would not be there.

    1. On 2025-11-11 03:32:18, user Evolutionary Health Group wrote:

      We at the Evolutionary Health Group ( https://evoheal.github.io/) "https://evoheal.github.io/)") really enjoyed this paper.

      Here are our highlights:

      In the week after the Jan 7 ignitions, virtual (clinic) respiratory visits jumped 41% in highly exposed areas and 34% in moderately exposed areas, totaling 3,221 excess visits, a clear, short-term signal health systems can act on.

      Virtual cardiovascular visits rose by ~35% across exposure groups in that first week (~2,424 excess visits), pointing directly to surge planning for virtual care during wildfire weeks.

      On the day of ignition (Jan 7) in highly exposed areas, outpatient neuropsychiatric and injury visits were about 18% higher than expected, evidence that mental-health demand starts immediately, not just respiratory care.<br /> The exposure framing is reproducible: simple proximity bands (<20 km vs >=20 km within LA County) applied to a 3.7-million-member health system and a five-category visit dashboard (all-cause, cardiovascular, injury, neuropsychiatric, respiratory) that others can copy.

      Scaled to all LA County residents, the estimates imply ~16,171 excess cardiovascular and ~21,541 excess respiratory virtual visits in the week after ignition, strong justification to expand virtual capacity during major fires.

    1. On 2022-02-17 20:55:31, user RT1C wrote:

      Table 3 (bottom) contains HR for boosted vs. non-boosted at various times (<6, 6-9, >=9 months). Aside from the minor labeling issue (hopefully not actual analysis issue!) that 6-9 months and >=9 months are not distinct subsets, overlapping at 9 months, I don't see how you could have made this analysis in the first place unless you have incorrectly defined POIC. You wrote, "we defined the proximate overt immunologic challenge (POIC) as the most recent exposure to SARS-CoV-2 by infection or vaccination." That means POIC for boosted subjects would be time since the booster dose as that is the most recent vaccination. Yet, considering how recently boosting began, how could you have boosted subjects with 6-9 or >=9 months POIC? (In your text you wrote, "For those boosted, the median time to being boosted was 16 days prior to the study start date (IQR -38 to 6 days).")

    2. On 2022-02-17 21:29:51, user RT1C wrote:

      You state, "For those boosted, the median time to being boosted was 16 days prior to the study start date (IQR -38 to 6 days)." Is that a typo or did you truly mean a positive 6? i.e., did you mean -38 to -6 days, or -38 to 6 days? If the latter, you actually included subjects who were vaccinated with boosters after the study period began? If that's the IQR, then I assume the full range extends much further into the study range. Those are VERY recently boosted. In your discussion, you should not say, "boosting with a vaccine designed for an<br /> earlier variant of COVID-19 still provides significant protection against infection with the Omicron variant." without also providing a time associated with that. For example, you might add to that sentence "for a period of at least 1 month" or whatever. It seems important to stress the limitation of the study in this manner, to avoid giving the impression that the booster provides long-lasting protection against infection when that is not shown by your study.

      Finally, on a related matter, how did you treat individuals who tested positive before 7 days after their booster? If, as some research suggests, vaccination temporarily increases susceptibility to infection (for about 2 weeks), by including subjects who were vaccinated within the study period, you may have biased findings against those without boosters.

    1. On 2022-03-04 16:06:11, user Tracy Beth Høeg, MD, PhD wrote:

      The peer reviewed version including numerous international datasets estimating rates of post vaccination myocarditis is now available. We have included risk-benefit calculations for children with a history of infection and used overall infection hospitalization risks (rather than just 120 days risks) both pre and during omicron. http://doi.org/10.1111/eci....

    1. On 2022-03-28 18:14:47, user August Blond wrote:

      Dear colleagues,<br /> I am having difficulty understanding figure 3, the two graphs that are plotted with GFP/EGFR.<br /> Zooming in on the four ovals - red, blue, black, green - I see that the scattered-plots are themselves contained in a smaller perfect ovoid.<br /> Can you explain how you manage the computer processing of your samples?<br /> In reference 13, the method for doing multiplex FACS, these close to perfect ovals do not appear. There are still points that are not perfectly integrated into the "virtual" geometrical structure.<br /> As is the case with all FACS using gating.<br /> Would it be possible to generate point clouds that have not been "artificially" modified after gating?<br /> Best regards,<br /> August Blond

    1. On 2022-06-08 17:08:32, user Ted Gunderson wrote:

      Should this be considered a scientific study or an advertisement?

      What evidence is there that what the authors refer to as "(non-variola orthopoxvirus and monkeypoxvirus specific)" actually causes the disease that is currently being diagnosed all over the world as "monkeypox".

      This is a paper funded by Roche that says "Our tests work!"

      "ML and DN received speaker honoraria and related travel expenses from Roche Diagnostics."

      Roche has gotten lots of press recently about their monkeypox tests.

      https://medicalxpress.com/n...

    1. On 2022-06-09 20:11:19, user John Doe wrote:

      Interesting paper that confirms and complements prior molecular findings on this devastating malignancy. A strength of this study is the inclusion of a relatively large series of patients (n = 47) considering the rareness of the disease. The results suggesting a diverse origin of BPDCN are of special interest, and the figure on potential therapies against the disease is visually appealing. However, data analysis and data interpretation have certainly problems and inconsistencies. In particular, the results on CNV pathogenicity produced by X-CNV are highly questionable and dubious, and I would strongly advise against using those results to guide data interpretation. Among deleted regions (suppl. data) classified as non-pathogenic by X-CNV are: 1p36.11 (ARID1A), 5q33.1 (NR3C1), 7p12.2 (IKZF1) and 9p21.3 (CDKN2A–B). All these are well-known tumor suppressors with demonstrated pathogenicity in numerous human cancers. Besides, prior studies back up the recurrent deletion and pathogenicity of these cancer genes in BPDCN [refer to papers by Lucioni M et al. Blood. 2011;118(17), Emadali et al. Blood. 2016;127(24), Bastidas AN et al. Genes Chromosomes Cancer. 2020;59(5), Renosi F et al. Blood Adv. 2021 9;5(5)].

      Puzzling enough, despite claiming the use of the X-CNV results to determine pathogenicity of CNVs, it appears that the authors chose to highlight anyway some deleted and gained regions classified as non-pathogenic by X-CNV (ARID1A, CDKN2A) as well as other regions not even formally called by GISTIC (e.g. TET2). This is even harder to comprehend considering that 7p12.2 (IKZF1) is clearly one of the most conspicuous peaks in the analysed cohort (Figure 3A); yet, completely ignored in the text and figure!? Quite baffling. In short, the paper would greatly benefit and improve from re-interpreting and discussing the data considering the existing literature on BPDCN genetics.

    1. On 2020-04-21 21:10:27, user Bruno Vuan wrote:

      Article says, page 7,

      "This study had several limitations. First, our sampling strategy selected for members of Santa Clara County with access to Facebook and a car to attend drive-through testing sites. This resulted in an overrepresentation of white women between the ages of 19 and 64, and an under-representation of Hispanic and Asian populations, relative to our community. Those imbalances were partly addressed by weighting our sample population by zip code, race, and sex to match the county. We did not account for age imbalance in our sample, and could not ascertain representativeness of SARS-CoV-2 antibodies in homeless populations. Other biases, such as bias favoring individuals in good health capable of attending our testing sites, or bias favoring those with prior COVID-like illnesses seeking antibody confirmation are also possible. The overall effect of such biases is hard to ascertain."

      In summary sample has

      Overrepresentation white woman 19-64<br /> Age imbalance not accounted <br /> Partial weighting by zip code, race and sex<br /> Biased favoring good health individuals and those seeking antibody confirmation

      Conclusion: "overall effect of such biases is hard to ascertain"

      1. Not balanced by age is a signal of impossibility of weighting by age without significative umbalance in the other dimmensions, as mentioned "result in small-N bins". Ignoring age balancing in a phenomena which is strongly age related is something that may bring a strong source of additional errors.
      2. If authors recognize that these biases are hard to ascertain, and no further discussion appears, is that this uncertainty is not included in error range. So, error range of this experiment appears to be totally unknown for the authors.

      Additionally

      There is no discusion on sampling effect by facebook ads, as answering rates, impact of facebook ads algorithm which is optimized to get maximum amount of answers. It is well known that this convenience samples are non probabiistical, so this has to be included in error range evaluation, (1)

      1. Baker R. et al, Non-Probability Sampling, AAPOR, June 2013 https://www.aapor.org/Educa...
    1. On 2022-06-24 22:03:50, user Charles Warden wrote:

      Hi,

      Thank you very much for posting this preprint. This certainly represents a large amount of work and careful consideration!

      I have some questions / comments:

      1) Is there a way for me to calculate enhanced scores for myself?

      For example, I would like to learn more, but I was not very satisfied with the PRS that I listed for my own genomic sequence in this blog post:

      https://cdwscience.blogspot...

      2) In the blog post link above, there seemed to be a noticeable disadvantage to the PRS without taking the BMI into consideration for Type 2 Diabetes.

      In this paper, age is an important factor in Figure 1 for the PRS.

      If other non-genetic factors are known, do you have a comparison for non-PRS models? <br /> For example, I wonder how performance of age + BMI (+ other established factors) compares to the plot for Type 2 diabetes in Figure 1.

      3a) I see that the percent variance explained is sometimes provided (such as Supplemental Figure 5), but sometimes it is not.

      For example, in Figure 3, the effect per 1 SD of PRS is higher for LDL cholesterol than height. However, how does the ability to predict an individual's height from genetics alone compare to the ability to predict an individual's LDL from genetics alone?

      After a certain age (as an adult), the exact value for my own LDL has varied more than my height. However, I was not sure how that variation by year compared to others and/or the variation over decades.

      In general, I would like to have a better sense of how absolute predictability compares for height versus disease scores. I also understand that there are complications with binary versus continuous assignments, but it is something that I thought might be helpful.

      3b) I see AUC statistics in Supplemental Figure 2, described as for AUROC. However, am I correct that some of the cases are not well balanced with controls?

      If so, should something like AUPRC be provided (possibly as a complementary supplemental figure)? I believe the idea is described in Saito and Rehmsmeier 2015; the application is very different, but you can see the inflated AUROC values in Figure 1A of Xi and Yi 2021. I expect that there are other good ways to illustrate the differences with PRS in cases and controls of varying proportions, but that was one thought.

      In the context of genomic risk, I might expect that high predictability in a small number of individuals may be preferable over a small difference in low predictability in a large number of individuals. There is emphasis on thresholds like top/bottom 3% (in many but not all figures), which I thought might be consistent with that opinion.

      So, I think something like Figure 1 was helpful. In order to try and capture how false positives change when sensitivity increases, I am not sure if something similar for positive predictive value might help? I would consider that very important if the PRS might be used for screening purposes.

      4) In the Supplemental Methods, I believe that you have a minor typo:

      Current: 100,000 Genomes Project (100KGP). The 100,00 Genomes Project, run by Genomics England,<br /> Corrected: 100,000 Genomes Project (100KGP). The 100,000 Genomes Project, run by Genomics England,

      Thank you very much!

      Sincerely,<br /> Charles

    1. On 2022-08-06 11:55:02, user Dieter Mergel wrote:

      I have a question concerning the following passage:

      "Previous work demonstrated that vaccination reduces severe COVID-19 and hospitalisation 46 and also the risk of Long COVID 7, 47. However, we did not observe evidence of qualitatively different symptom clustering in vaccinated vs. unvaccinated individuals, with either alpha or delta variants."

      Does it mean: <br /> (a) Vaccination does not reduce the risk of Long Covid.<br /> or<br /> (b) Vaccination reduces the risk of Long Covid, but if (!) vaccinated people get Long Covid, then (!) the symptoms are similar to those of unvaccinated people.

    1. On 2022-08-14 15:08:34, user Peter J. Yim wrote:

      The trial registration at ClinicalTrials.gov listed three primary endpoints:<br /> 1. Number of hospitalizations as measured by patient reports. [ Time Frame: Up to 14 days ]<br /> 2. Number of deaths as measured by patient reports [ Time Frame: Up to 14 days ]<br /> 3. Number of symptoms as measured by patient reports [ Time Frame: Up to 14 days ]

      The publication reports the outcomes for none of those endpoints. (the endpoints were changed after publication on ClinicalTrials.gov)

      1. The rate of hospitalization was reported at 28 days. That was registered as a secondary outcome.
      2. Mortality was reported at 28 days. That was registered as a secondary outcome.
      3. The number of symptoms was only reported at baseline.

      This article is close to irrelevance on the question of the efficacy of ivermectin in COVID-19.

    1. On 2021-08-11 10:34:14, user Apriyano Oscar wrote:

      I am sorry, I am just a layman. I want to ask about the 1.8% tested positive (608 people). Does it mean that the effectiveness of the Pfizer vaccine in this study is 98.2% ? And is this also the same as what is called as 'efficacy' ?

    1. On 2021-05-24 16:53:32, user Gustavo Bellini wrote:

      Congratulations on the work! It would be interesting to analyze the action of vitamin D in the MHC complex, MICA / MICB.

      • A subgroup of lupus patients with nephritis, innate T cell activation and low vitamin D is identified by the enhancement of circulating MHC class I-related chain A<br /> https://doi.org/10.1111/cei...

      "Indeed, immune cells significantly up-regulate vitamin D receptor (VDR) transcription upon activation and proliferation (reviewed in [28]). In turn, through the binding of VDR, vitamin D induces the expression of anti-proliferative/pro-apoptotic molecules, thereby evoking immune tolerance 29, 30. Interestingly, recent data showed that MICA stands as a VDR-sensitive molecule, through which vitamin D renders tumour cells susceptible to NK cytotoxicity 31. According to this view, in our patients the gene expression of MICA in T cells was not associated with the up-regulation of TLR or ISG, as could have been expected, but paralleled levels of vitamin D instead. All these observations suggest that vitamin D could help to restore homeostasis of the immune system during flares, and that its deprivation may jeopardize MICA-dependent cell growth control."

      In addition, the inverse relationship between circulating sMICA and vitamin D found in our cohort suggests that the vitamin could prevent MICA shedding. Alternatively, sMICA impairment of NK functions could promote the uncontrolled proliferation of immune cells which, in turn, would facilitate the depletion of vitamin D.

      In summary, we propose a particular disease pheno-type characterized by the disruption of MICA-dependent cytotoxicity in patients with innate activation of T cells and possibly facilitated by low vitamin D levels."

      "Basically all cellular components of PBMCs belong to the innate and adaptive immune system. Therefore, it is not surprising that the immunologically most important region of the human genome, the HLA cluster, also highlights as a “hotspot” in the epigenome of PBMCs.<br /> However, it is remarkable that the HLA cluster is also a focused region of the vitamin D responsiveness of the epigenome. This observation provides a strong link to the impact of vitamin D on the control of theimmune system.<br /> In conclusion, in this proof-of-principle study we demonstrated that under in vivo conditions a rather minor rise in 25(OH)D3 serum levels results in significant changes at hundreds of sites within the epigenome of human leukocytes."

      The study below has shown evidence that the vitamin D endocrine system is dysregulated in sars-cov-2 infection.

    1. On 2020-11-24 09:59:43, user Lee Rague wrote:

      This paper has been recently published:<br /> Labrague LJ, De Los Santos JAA. Prevalence and predictors of coronaphobia among frontline hospital and public health nurses. Public Health Nurs. 2020 Nov 23. doi: 10.1111/phn.12841. Epub ahead of print. PMID: 33226158.

    1. On 2021-12-13 11:53:14, user Undertow of Discourse wrote:

      The summary of findings in the abstract is defective in relation to PIMS-TS. It says “ The overall PIMS-TS rate was 1 per 4,000 SARS-CoV-2 infections”. Rate of what? Occurrence of PIMS-TS? Hospitalization with PIMS-TS? Death from PIMS-TS?

    1. On 2023-05-09 17:56:41, user Dr. Gerald Zincke wrote:

      I am missing indication at which point in time after the vaccination an infected patient was counted to the vaccinated group.

      (For the importance of this, please refer to Prof. Norman Fenton's description of the statistical illusion that can occur when vaccinated people are counted as unvaccinated for a period of time after the shot. https://youtu.be/Gkh6N-ZL3_k )

    1. On 2021-08-14 17:37:30, user Uwe Schmidt wrote:

      The study states a hospitalisation rate of 6% for children.

      This rate needs to be strongly questioned as it is internationally significantly higher than any other rate observed. In fact, it is higher by roughly factor 10-12. E.g. in Germany, at the peak of the pandemic in week 51/2020, less than 100 children were hospitalised nationwide, 1/3 of them newborn, who just stayed in hospital a little longer. The number of positive tested children in that week was ~20,000. For July 2021, the number of hospitalised children is less than 10, no ICU.<br /> In England, one out of 200 (0.5%) children are hospitalised.<br /> In Israel, no patient below the age of 30 is in critical condition.

      Questions for the authors:<br /> 1. Does the total number of children tested positive really consist of ALL PCR-positive or only a subgroup reported by certain institutions?<br /> 2. Of those 5,213 hospitalised, how many were hospitalised because of COVID-19 and how many because of other conditions?

    1. On 2020-07-25 23:24:04, user BannedbyN4stickingup4Marjolein wrote:

      I'm not a bio-mathematician but I've had a similar idea in my head for some time. I'm not comfortable with all of the maths so to an extent I have to take some of this on trust.

      But the basics of it, as I understand it, is that transmission takes place when some yet to be defined criteria are satisfied (through air, via a surface, without a mask, indoors, whilst singing, who knows?) through a temporal network. It would certainly help to understand this mechanism better, but that's not the focus of the paper.

      Early infection removes the easiest nodes from this network - those people most easily susceptible overlapping with those peole with the most contacts. The mechanism of node removal is death in a few cases and post infection immunity in the majority.

      Just a couple of notes of caution then:

      One obvious one is how long does immunity last? Suppose some kind of herd immunity is achieved at 20% infection of the population, but that a typical population (not a densely populated city like New York) is not infected to this level until infection acquired immunity starts to wane?

      The second - and I am disappointed not to see more mention of this in the paper - what if a significant element of node removal is down not to post infection immunity but to changes in social behaviour in response to the epidemic?

      R is a function not just of the pathogen but of the population it infects - its density is relevant, but so is its behaviour. This applies whether one models the population as a simple homogenous mass (SIR type models) or as a set of discrete interconnected agents.

      Then no sooner does everyone revert gung ho to their previous pattern of behaviour (we're at herd immunity, we're safe!) then infection takes off again.

    1. On 2020-12-28 18:05:42, user Rogerio Atem wrote:

      The 3 preprints of this series on COVID-19 epidemic cycles were <br /> condensed into a single article that summarizes our findings using the <br /> analytical framework we developed. The framework provides cycle pattern <br /> analysis, associated to the prediction of the number of cases, and <br /> calculation of the Rt (Effective Reproduction Number). In addition, it <br /> provides an analysis of the sub-notification impact estimates, a method <br /> for calculating the most likely Incubation Period, and a method for <br /> estimating the actual onset of the epidemic cycles.

      We also offer an innovative model for estimating the "inventory" of infective people.

      Check it at:

      (Revised, not yet copy-edited)<br /> https://doi.org/10.2196/22617

    1. On 2020-08-12 11:44:27, user My Opinion wrote:

      In my opinion...this supports the explanation why certain facilities (e.g. nursing homes, prisons, cruise ships, church gatherings) experience large numbers of individuals who become infected....I have never believed that the primary mode of transmission was a cough or sneeze....in some prison facilities....we have seen 80% of the population inside the facility become infected, including prison guards....the virus spreads too efficiently to blame it on a cough or sneeze....for example, we know that small pox can be spread through exhaled respiration...this research appears to be the first published study to definitively prove COVID-10 can float in the air and infect people quite distant from the infectious source (17-feet)....this explains how large numbers of people can become infected quickly...it is in the air...Thomas Pliura, M.D., Le Roy, IL

    1. On 2021-06-13 21:16:52, user thomas wrote:

      I am not in the health field (that may be obvious from the questions I have) but I am very interested in this study because my parents (in their 70's) both had and recoverd from covid. They have not received a vax yet.

      1. Why wouldn't having the infection give immunity? Is there something about this specific virus, or this type of virus in general, that it wouldn't be expected to give immunity?

      2. If infection doesn't give immunity, how will the vaccines work? I realize some vaccines are mRNA or viral vector, but at least the two Chinese ones, the Indian one, and a new one the French are working on are all based on using a dead/weakened virus. Shouldn't recovering from an actual infection work just as good as the simulated infection of a vaccine?

      3. Is 1,359 subjects really considered small? How big where the sample sizes for the initial vaccine studies? What would be an acceptable size? My background is more in the social sciences, and we often see samples in the hundreds.

      4. Is it really correct to assume that people who had COVID would be more careful afterwards? I know with my parents, they were almost consumed with fear about catching the disease, but once they did and recovered, much of that went away. I wasn't around to see their behavior, but just based on conversations, I find it hard to believe they were more careful.

      When my parents saw the doctor after recovering, he told them they could not get the vaccine for at least 3 months and that they didn't need to get it until after 6 months. So this study seems in line with what the medical establishment was already saying (they had COVID back in March).

    1. On 2020-07-08 11:38:25, user peter kilmarx wrote:

      Congrats on your bibliometric analysis. Here's a reference for you: Grubbs JC, Glass RI, Kilmarx PH. Coauthor Country Affiliations in International Collaborative Research Funded by the US National Institutes of Health, 2009 to 2017. JAMA Netw Open. 2019 Nov 1;2(11):e1915989. doi: 10.1001/jamanetworkopen.2019.15989.

      We found that publications coauthored by US-affiliated and non-US-affiliated investigators had a higher mean citation index (1.99) than those whose authors were only US affiliated (1.54) or non-US affiliated (1.35).

    1. On 2024-07-24 16:07:33, user Jim Woodgett wrote:

      A sobering study! I have a couple of questions about the population evaluated and timing of the study. In Methods the "Pandemic" group (G1) included subjects with scans before and after pandemic onset (N =404; 247 female), further split into "Pandemic–COVID-19" (G3, N = 121; 75 female) and "Pandemic–No-COVID-19" (G4, N = 283; 172 female). So there were 121 who had (at least one?) Covid-19 infection and 283 who had no infection. This seems an unusual sampling ratio given known serological analysis and overall penetrance of infection. How long after infection were the MRIs performed and at what point were subjects classified as Covid infected or not (presumably, the majority became infected during the study)? Were there sufficient subjects and data to assess degree of brain aging vs multiplicity of infection? Is there data on subjects self-reporting long Covid effects?

    1. On 2021-01-27 06:59:12, user Peter Hessellund Sørensen wrote:

      In the graph showing mortality vs COVID19 cases as a function of T cell imunity. In Singapore 95% of the cases were in migrant workers in their 20s and 30s. Similar problems are probably present in the other countries in the sense that the way of counting cases and deaths is not the same and different population groups are infected in different countries. <br /> Allready with Singapore removed the statistical significance of the graph has vanished.

    1. On 2020-04-30 19:12:43, user Sinai Immunol Review Project wrote:

      Main findings<br /> This report describes the use of systemic tissue plasminogen activator (tPA) to treat venous thromboembolism (VTE) seen in four critically ill COVID-19 patients with respiratory failure. These patients all exhibited gas exchange abnormalities, including shunt and dead-space ventilation, despite well-preserved lung mechanics. A pulmonary vascular etiology was suspected.

      All four patients had elevated D-dimers and significant dead-space ventilation. All patients were also obese, and 3/4 patients were diabetic.

      Not all patients exhibited an improvement in gas exchange or hemodynamics during the infusion, but some did demonstrate improvements in oxygenation after treatment. Two patients no longer required vasopressors or could be weaned off them, while one patient became hypoxemic and hypotensive and subsequently expired due to a cardiac arrest. Echocardiogram showed large biventricular thrombi.

      Limitations<br /> In addition to the small sample size, all patients presented with chronic conditions that are conducive to an inflammatory state. It is unclear how this would have impacted the tPA therapy, but it is likely not representative of all patients who present with COVID-19-induced pneumonia. Moreover, each patient had received a different course of therapy prior to receiving the tPA infusion. One patient received hydroxychloroquine and ceftriaxone prior to tPA infusion, two patients required external ventilator support, and another patient received concurrent convalescent plasma therapy as part of a clinical trial. Each patient received an infusion of tPA at 2 mg/hour but for variable durations of time. One patient received an initial 50 mg infusion of tPA over two hours. 3/4 patients were also given norepinephrine to manage persistent, hypotensive shock. Of note, each patient was at a different stage of the disease; One patient showed cardiac abnormalities and no clots in transit on an echocardiogram, prior to tPA infusion.

      Significance<br /> The study describes emphasizes the importance of coagulopathies in COVID-19 and describes clinical outcomes for four severe, COVID-19 patients, who received tPA infusions to manage poor gas exchange. While the sample size is very limited and mixed benefits were observed, thrombolysis seems to warrant further investigation as a therapeutic for COVID-19-associated pneumonia that is characterized by D-dimer elevation and dead-space ventilation. All four patients had normal platelet levels, which may suggest that extrinsic triggers of the coagulation cascade are involved.

      The authors suspect that endothelial dysfunction and injury contribute to the formation of pulmonary microthrombi, and these impair gas exchange. Pulmonary thrombus formation has also been reported by other groups; post-mortem analyses of 38 COVID-19 patients' lungs showed diffuse alveolar disease and platelet-fibrin thrombi (Carsana et al., 2020). Inflammatory infiltrates were macrophages in the alveolar lumen and lymphocytes in the interstitial space (Carsana et al., 2020). Endothelial damage in COVID-19 patients has also been directly described, noting the presence of viral elements in the endothelium and inflammatory infiltrates within the intima (Varga et al., 2020). One hypothesis may be that the combination of circulating inflammatory monocytes (previously described to be enriched among PBMCs derived from COVID-19 patients) that express tissue factor, damaged endothelium, and complement elements that are also chemotactic for inflammatory cells may contribute to the overall pro-coagulative state described in COVID-19 patients.

      References<br /> Carsana, L., Sonzogni, A., Nasr, A., Rossi, R.S., Pellegrinelli, A., Zerbi, P., Rech, R., Colombo, R., Antinori, S., Corbellino, M., et al. (2020) Pulmonary post-mortem findings in a large series of COVID-19 cases from Northern Itality. medRxiv. 2020.04.19.20054262.

      Varga, Z., Flammer, A.J., Steiger, P., Haberecker, M., Andermatt, R., Zinkernagal, A.S., Mehra, M.R., Schuepbach, R.A., Ruschitzka, F., Moch, H. (2020) Endothelial cell infection and endotheliitis in COVID-19. Lancet. 10.1016/S0140-6736(20)30937-5.

      The study described in this review was conducted by physicians of the Divisions of Pulmonary, Critical Care, and Sleep Medicine, Cardiology, Nephrology, Surgery, and Neurosurgery and Neurology at the Icahn School of Medicine at Mount Sinai.

      Reviewed by Matthew D. Park as part of a project by students, postdocs, and faculty at the Immunology Institute of the Icahn School of Medicine, Mount Sinai.

    1. On 2021-02-04 09:44:55, user Sepp271 wrote:

      Taking into account the 7-day incidence of that region (Munich) and the number of tests taken, about 1 or 2 positive cases would have been expected when similar testing would have been done in general population. Taking dark number of incidence into concern this figure goes up to roughly 2 or 3.

      Therefor within this study one can not state that the observed number of positive cases of 2 found in primary schools, kindergartens and nurseriesis is significantly different from the infection numbers in the general population.

      It would have helped if the authors had made a strict comparison of both groups including statements about the confidence interval.

    1. On 2021-10-02 06:16:24, user Not Ready to Panic Dog wrote:

      Since low Vitamin D levels are associated with increased incidence of cancer, heart disease, diabetes, and various auto-immune, neurological and inflammatory disorders, how did you account for the patients’ comorbidity influence on disease progression? https://pubmed.ncbi.nlm.nih...