On 2020-06-21 13:18:56, user OxImmuno Literature Initiative wrote:

https://www.immunology.ox.a...

On 2020-06-23 15:56:38, user SeaDNA Project wrote:

This study was partially funded by the UK Natural Environment Research Council grant NE/N005759/1, 'SeaDNA'.

On 2024-09-27 11:24:18, user Ruth Berger wrote:

Important, high quality research. There is a potential alternative explanation for the pattern observed which should be checked: Viola arvensis is a pretty rare wildflower where I live, and in any peri-urban environment must be much, much rarer than garden cultivar violas that usually don't offer any pollinator food at all. Could it be that pollinators avoid them because of their experience with garden variety violas that taught them viola-like flowers are useless? From observation, I have seen various wild and cultivated viola species not getting any pollinator visits at all despite pollinators busily visiting other flowers in the immediate vicinity.

On 2020-09-03 07:49:01, user David Curtis wrote:

This paper, which shows there is an excess of rare, functional coding variants in synaptic genes in schizophrenia seems relevant: https://www.ncbi.nlm.nih.go...

On 2024-07-12 23:46:41, user Alex wrote:

I hate myself for doing this, but apparently this is the only way to point this out: why doesn’t this benchmark include singleCellHaystack? Haystack was published in Nat Commun in 2020, has >75 citations now, is easy to install and run. An updated was published last year In Scientific Rep. Still, a part of this field that has apparently decided that it is completely fine to ignore this method.

On 2020-05-29 12:51:56, user Matteo Brilli wrote:

Supplementary Table 2 contains accessions to full genomes not orfs used. Why is ORF3b not present (well, not annotated) in the reference genome?

On 2017-11-21 13:23:43, user Peyton Lab wrote:

We reviewed this paper in journal club recently and took some notes. I hope these comments are useful.

1. Need to define “TIC” in the paper somewhere (first appearance is in Figure 1). TD is referenced but it is near the end - would be useful to have this closer to figure 1 (or in the figure legend).

2. For Figure 1 - which cancer types tend to follow the HA model vs. the Stochastic model.

3. It appears that Figure 2 (left) is a model applied to Figure 1 (left). Then Figure 2 (right) is a new model. It would be helpful to also compare what existing models are currently used applied to Figure 1 (right).

4. We had trouble understanding how the equation in figure 2 results in a double biphasic relationship in the bottom of that figure. What cell population is used as an origin point (boundary condition): a cell state of 0 or 1? It was not clear to us, using either as a starting point, how a double biphasic curve would be achieved. Additional equations shown would be useful.

5. Figure 3 is not referenced in the paper.

6. For Figure 3 - a control would be useful, looking at CD133-negative cells as well. One might expect these to be all Sox2 low, regardless of medium. However, if you were to find variation in Sox2 expression in CD133-low cells, then that would be another argument against the existing models.

7. Additionally in figure 3, after 3 days of culture, are these cells still CD133-high? If so, that would help validate your model further (against the HA the model).

On 2018-08-08 16:33:12, user Brian Tsui wrote:

Feel free to check out my blog at to understand the why I created the project: https://brianyiktaktsui.wor...

MUCH SUGGESTIONS ARE NEEDED. :)

On 2018-07-10 19:34:18, user Vivek Iyer wrote:

Author list has now been extended by one: new list is Vivek Iyer, Katharina Boroviak, Mark Thomas, Brendan Doe, Laura Riva,<br /> Edward Ryder, David J. Adams. We have now been published by PLOS Genetics: http://journals.plos.org/pl...

On 2016-09-13 08:53:31, user Guillaume Rousselet wrote:

Very useful coverage and great illustrations. <br /> You might be interested in the R and Matlab code for robust outlier detection techniques in these toolboxes for instance:<br /> http://dornsife.usc.edu/lab...<br /> http://journal.frontiersin.... <br /> https://wis.kuleuven.be/sta...

On 2021-11-04 18:06:08, user Donald R. Forsdyke wrote:

Macroevolution versus Microevolution

Presumably this paper (1) has been released in preprint form to obtain feedback before formal publication. Coauthored by a consortium of current leaders in the field of population genetics, it states that "the ability to fit the parameters of one's preferred model to data does not alone represent proof of biological reality." They hope fellow practitioners, having been alerted by this "simple truism," will avoid various pitfalls. Apart from concerns on synonymous site neutrality (2), calls to reconsider evolutionary fundamentals (3, 4) are not mentioned.

The historical authority of William Provine is referred to (5). He described the early 20th century dispute between geneticist William Bateson and the "Biometricians" (Pearson and Weldon). While disputing Mendelism, the latter made outstanding contributions to statistics. However, Provine concluded The Origins of Theoretical Population Genetics diffidently: "With the gap between theoretical models and available observational data so large, population genetics began and continues with a theoretical structure containing obvious internal consistencies."

Despite these 1971 words and "the wealth of data" now available, that gap remains. To bridge, the authors appeal to "interdisciplinarity … in order to connect genotype to phenotype" (1). This should remind us that in the 1920s Bateson foresaw (3) "that before any solution is attained, our knowledge of unorganized matter must first be increased." So sadly, regarding his topic, genetics: "For a long time we may have to halt." It was only following great progress in molecular biology, that in the 1970s WWII bomber pilot Richard Grantham, at the Université de Lyon, was able to ask the very question the authors pose (1): "Whether, and if so how, accurate evolutionary inferences can be extracted from DNA sequences sampled from a population?" In the authors' words (1), Grantham was able to use "molecular variation and divergence data to infer evolutionary processes." What Grantham called his "genome hypothesis" (6) was later related to the earlier ideas of Darwin's research associate, George Romanes, and Bateson (3).

While readily adopting Bateson's coinages – homozygote, heterozygote, allelomorph, epistasis, homeotic, meristic – the modern-day biometricians (1) have overlooked the most fundamental of his ideas, the "residue" (3), as they did Grantham's "genome hypothesis" and Romanes' "collective variation." Likewise, to make the mathematics easier, they embraced the neutral ideas of Kimura (2), instead of the "homostability" ideas of his compatriot, Akiyoshi Wada (7), who pressed unsuccessfully for a Japanese "genome project," which would have anticipated by many years that of the USA (8).

The works of Grantham, Romanes and Bateson, together with those of various Russian evolutionists and Richard Goldschmidt, focus on the fundamental distinction between inter-species "macroevolution" and intra-species "microevolution" (7). This crystallized historically in 1990 in the lectures and writings of the Russian specialist, Mark Adams. He stressed that the understanding of macroevolution would demand "a radically new interpretation of the history of Darwinism, population genetics and the evolutionary synthesis." For "if intra- and inter-specific variation differ not in kind, but only in degree, then it is possible, by extension, to envision selection as the creator of a new species. But if varieties are fundamentally different from species – if the fundamental character of intraspecific and interspecific variation is essentially different – then the effect of selection on a population cannot explain evolution." Initially published in French in 1990, Adams' work is now available in English (4).

1. Johri et al. (2021) Statistical inference in population genomics. bioRxiv: doi.org/10.1101/2021.10.27.... Nov 2.
2. Kern AD, Hahn MW (2018) The neutral theory in light of natural selection. Mol Biol Evol 35:1366–1371.
3. Cock AG, Forsdyke DR (2008) Treasure Your Exceptions. The Science and Life of William Bateson. Springer, New York.
4. Adams MB (2021) Little evolution, big evolution. Rethinking the evolution of population genetics. In: Delisle RG (ed), Natural Selection. Revisiting its Explanatory Role in Evolutionary Biology. Springer Nature, Switzerland, pp. 195-230.
5. Provine WB (1971) The Origins of Theoretical Population Genetics. University of Chicago Press.
6. Grantham R, Perrin P, Mouchiroud D (1986) Patterns in codon usage of different kinds of species. Oxford Surveys in Evolutionary Biology 3:4 8-81.
7. Forsdyke DR (2016) Evolutionary Bioinformatics, 3rd edn. Springer, New York.
8. Cyranoski D (2009) Reading, writing and nanofabrication. Nature 460:171-2.

On 2022-03-29 10:48:01, user Daniel Baldauf wrote:

Nice work! I wonder how your distinction of local/global relates to high-level object-based auditory attention. For example, Marinato & Baldauf (2019, Sci.Rep.) used mixed environmental 'sound-scene', and showed that top-down object-based attention has a strong effect on the parsing of the acoustic stream. DeVries et al. (2021, JN) then also recorded MEG during such a task, showing that it is particularly the alpha band in a inf.fronto-temporal network that mediates these functions of object-based attention, and that allows for successful trial-wise decoding of the locus of attention. <br /> Best wishes!

On 2023-07-11 18:40:20, user argonaut wrote:

"This suggests that the people at both sites

genetically related individuals varied in the places where they resided over their lifetimes" ... "some evidence that families sourced food<br /> from different landscape contexts, either through variation in direct consumption or<br /> through variation in consumption of animals eating these plants."

Have you taken into account the slash-and-burn farming strategy of LBK and the constant displacement that it takes?

On 2017-12-28 14:58:49, user Matteo Carandini wrote:

Never mind, I got it! Black curve is lick rate, green curve is body movement, and all trials are rewarded (there is no match vs. nonmatch -- all trials are rewarded). Got it.

On 2024-03-11 04:02:22, user Passionate Scientist wrote:

Yup, nice validation of previous study by verma et al. Nat Immunology 2019. Seems like these cells phenotype is very important determinant of checkpoint inhibitors efficacy.

On 2019-04-01 08:17:18, user Arne Schwelm wrote:

Any chance you can add the reference list?

On 2020-05-13 21:11:47, user agoraks wrote:

In our experience Abbot ID Now is very operator dependent.

On 2019-11-11 05:58:46, user Saurabh Gayali wrote:

Doesn't sound like a complete paper but a tutorial. Would be great if you dockerize all tools in a single container and provide step by step guide. That should build a comprehensive tool and increase face value to this article.

On 2018-05-06 21:02:01, user BenjaminSchwessinger wrote:

This is of course is an important contribution to plant pathology as it reports the molecular cloning of three! plant resistance genes toward wheat stripe (yellow) rust, which is an important wheat pathogen globally.

Overall I really enjoyed the paper and it was easy to read. In the following are some suggestions and questions. These are just listed in order of appearance.

* The order of introducing the three resistance genes changes throughout the text. It would be great to be consistent all the way Yr5, Yr7, YrSP or such.<br /> * I don't think the usage of acronym PST helps the readability of non-experts and some journal don't allow these acronyms. I would suggest to stick to P. striiformis f.sp. tritici throughout the text. Of course this approach is also more scientific.<br /> * Line 31. Of course Yr5 is likely to have 'remained' effective against Pst only because it hasn't been used much in modern breeding varieties. Maybe worth pointing out.<br /> * please ensure that all acronyms are introduced on the first mention<br /> * So are there wheat varieties that are resistant to AvrYr5 AND AvrYrSP?

* I am a bit at loss with the BED integrated domain part at times. This maybe just me. Here are my thoughts.

The BED domains of Yr5/YrSP are identical and only have one amino acid difference to Yr7. (Here it would be great to see in future if this single amino acid change matters). This conservation of the BED would suggest to me that is more likely to be involved in signaling than effector recognition. Or former being at least an equal hypothesis at the moment. Clearly these domains are important and quasi identical yet all the three Yr proteins recognize different Avrs (at least that what we think based on phenotypes).

I am not following the argument made on 159ff. If I understand correctly, the phylogeny on the protein level separates the NLR-BEDs from the non-NLR BEDs. This suggest a conserved function in these proteins compared to others. For me again this would be more consistent with signaling as the downstream targets of signaling maybe also conserved and different from non-NLR BEDs. Yet here the argument is made (line 160ff) that 'This separation is consistent with the hypothesis that integrated domains might have evolved to strengthen the interaction with the effector after integration'. This argument would suggest that the effectors these proteins recognize is conserved? Was already present when the integration invent happened? Or do different BEDs evolve to bind different effectors that target the same or different non-NLR BED? Why are all the three BED domains all identical than? Maybe I am missing something here.

On 2019-04-16 14:35:28, user hugo wrote:

Hello, the github link is not working. Maybe is this repo? https://www.biorxiv.org/con...

Best regards.

On 2022-06-16 02:27:16, user Sciency wrote:

This is a fascinating article to read, and I look forward to learning more. I'm going to take it step by step, commenting on clarity as I read through the paper.

• I stumbled a few times in the abstract. " A deeper sampling of individual ants from two colonies that included all available castes (pupae, larvae, workers, female and male alates), from both before and after adaptation to controlled laboratory conditions, revealed that ant microbiomes from each colony, caste, and rearing condition were typically conserved within but not between each sampling category." <br /> What does "deeper" mean, is it that you sampled ants from each caste? (the way it's phrased now, it sounds somewhat detached, like stating that the colony had castes without stating that you sampled them) <br /> So colony number, caste, and wild vs lab are sampling categories? What would it mean "within, but not between each sampling category"?

• What kind of sequencing did you do? I'd like to see at a glance which -omics you are doing, right at the start of the paper, because I sometimes look for papers that use a particular method.

• You use "Tenericute" in the abstract, and "Mollicute" in the Importance section. For the readers unfamiliar with the two, it might be good to disambiguate.

• The Importance section is somewhat long and repeats a lot of the abstract. What made you want to do this study? That no one has studied this ant's microbiomes? That the findings might extrapolate to other ants? That you could say something about individuality and colonial organization or evolution from the members' microbiomes?

• "Honey bee queens, workers, and drones also each have unique gut microbiomes, where worker microbiomes are more diverse than those of queens and drones, possibly due to worker foraging (9)." "Unique" has the connotation of being individual, rather than a group characteristic. Would "discrete" be a better term? And I'm a bit confused by "more diverse". Diverse how? Is the meaning that the range of species within the microbial community is somehow wider on the taxonomic tree? Or something else?

• But now reading that honey bees have a core microbiome that is found in all colonies and castes. But were we not talking about "more diverse"?

• "However, strains [...]" just need to be a bit clearer on what are these strains of.

• What makes these microbiomes "low-diversity"?

• " the samples collected from each colony were not differentiated from each other" is unclear. Do you mean that the team collected ants of caste X from all 25 colonies into a single blended sample? Try to rephrase " Whether the 19 common bacteria found in Texas T. septentrionalis and form a conserved microbiome that is found in other geographic regions or castes is also unknown." is unclear to me. Maybe try to break it up into shorter sentences.

• "major driver" might suggest causality. I think you mean that the differences in the presence or absence of those symbionts are producing the statistical effect of seeing differences between microbiomes, is that correct?

• "Colony JKH000270 lab-maintained ants were sampled after a year and 4 months (some male alates were sampled earlier) and Colony JKH000307 lab-maintained ants were sampled after 4 months."<br /> I'm wondering if the time factor would be important in microbiome adaptation. If it is, can the two colonies really be compared to each other? Would you mind adding a couple of sentences to explain the procedure?

• I'm not sure I understand how pupae and ant guts and whole ants will act as confirmatory datasets. Would you mind elaborating?

• "Reads that were not classified as belonging to the kingdom Bacteria (i.e., those identified as Archaea or Eukaryote) using the SILVA database v128 (43, 44) were removed." I understand that including viruses, other fungi, diatoms, etc. would change the scope of the project. I'd be interested in learning about that part of the microbiome, and hope you write the next paper on it.

On 2024-10-20 08:57:36, user Alireza Mani wrote:

This paper is now published at https://doi.org/10.1016/j.bbrc.2024.150854

On 2021-09-07 17:14:38, user Rohit Ruhal wrote:

Need screening of chemical library

On 2025-11-22 19:01:53, user Brandon wrote:

Hello! I am lead author for this manuscript, which has been published and has yet not been linked to this preprint. This is likely due to a title change during the review process. The link to the final manuscript is at the Journal of Neural Engineering, https://iopscience.iop.org/article/10.1088/1741-2552/ae1bdb

Cheers!<br /> Brandon

On 2017-03-27 16:16:18, user AdamMarblestone wrote:

-"Spatially Compact Neural Clusters in the Dorsal Striatum Encode Locomotion Relevant Information" https://www.ncbi.nlm.nih.go...<br /> -"A multi-modal parcellation of human cerebral cortex" http://www.nature.com/natur...

On 2020-01-07 00:25:42, user Guofeng Meng wrote:

Comments are appreciated to improve this manuscript.

On 2018-12-03 19:55:25, user Jon Moulton wrote:

In the Lai et al. 2018 preprint from Didier Stainier's group, <br /> Morpholino knockdown of vegfaa showed no stress gene response. This <br /> demonstrates that the stress gene upgregulation seen with knockdown of <br /> egfl7 and some other transcripts is not a response to the Morpholino <br /> backbone but a response to the loss of the target's expression. The <br /> Robu et al. 2007 p53 paper showed that if Morpholino knockdown of a <br /> transcript caused a p53 response, knockdown of that target with a <br /> different oligo type (in their case a modified PNA) caused a similar p53<br /> response, again a response caused by the loss of particular proteins.

These studies reveal more about biological responses to a knockdown <br /> and the contrast of knockdowns and knockouts. Especially combined with <br /> observations of a mutant, the loss of target function in a wild-type <br /> organism (an uncompensated background) can reveal more information about<br /> the target protein's function and the cellular response to its loss.

As demonstrated by the stress response to the Standard Control oligo <br /> at elevated doses, keeping the dose of a Morpholino as low as <br /> practicable improves the oligo specificity, decreasing the probability <br /> of stress responses.

I work at Gene Tools, which manufactures Morpholinos.

On 2019-08-22 15:16:23, user William James wrote:

Very nice follow-up work by Cantor and Lenardo on T cell biology in physiological medium. NB, HPLM as used here is not identical with the 2017 original, as it has been supplemented with Uridine (3 µM), ?-ketoglutarate (5 µM), acetylcarnitine (5 µM) and malate (5 µM). This is no bad thing in principle, as they are physiologically and biochemically justified, but I wonder whether we should start to version-number media formulations, to avoid confusion. This could be, for example, HPLM v1.2

On 2020-11-09 11:21:13, user David Curtis wrote:

Khera et al exaggerated the value of PRS for CHD. The important familial hypercholesterolaemia variants confer a much higher risk than the top decile of PRS.

https://onlinelibrary.wiley...

On 2015-10-05 14:16:27, user gedankenstuecke wrote:

Nice to see this. We recently also did work on this, looking into how important it is to evaluate assemblies based on simulated data to get an expectation value prior to doing large scale sequencing. See http://dx.doi.org/10.1111/1...

On 2019-05-10 06:28:34, user Milind Watve wrote:

Our manuscript was rejected by a leading journal with comments by three reviewers. We expressed our desire that in the spirit of transparency of the review process, the reviewers’ comments and our responses should be allowed to be posted and made public. Two of the two reviewers and the journal editors agreed to the request and therefore we are posting their comments and our responses to them here. Although the journal editors consented to post them, on the advice of Biorxiv admin, we are keeping the journal, editors as well as reviewers anonymous. <br /> Rejection is a part of the game and we respect the editors’ decision. However, the reasons for rejection should be transparent so that readers can make their own judgment about the fairness of the editorial process. Transparency would make the review process more responsible and we express our full support to it. <br /> We thank the editors and all the three reviewers for their inputs. We would have been happier if reviewer 1 also agreed to post his comments.<br /> Milind

Reviewer #1:<br /> Did not respond to the request for consent to post the comments.

Reviewer #2:

The authors provide a systematic literature study on the question: “does insulin signaling decide glucose levels in the fasting steady state?”. The answer is a clear no. Although the overview looks solid - I am not an expert in all the literature on glucose homeostasis, so I cannot decide on that, really – the conceptual aspects of this study are rather weak. This may very well reflect the general weakness in conceptual thinking in biomedical sciences, but certainly the control engineers that build feedback control system for artificial pancreas applications will find the answer trivial. The authors use biologically fuzzy terminology, such as “drivers” and navigators”, CSS and TSS, and later r and K strategies, where terminology of control theory would be most appropriate. Not a single reference to control theory, where an integral feedback principle could explain much, if not all of the observations, it seems.

Response: The reviewer appropriately captures the state of control theory and models by the words “much, if not all”. All the models of glucose homeostasis today explain only a small part of the demonstrated features of glucose homeostasis and of diabetes. The “much” is a very small fraction of reality and most models stop at explaining only some of the features. Not being able to explain a certain empirical finding does not immediately invalidate a model. However, a direct contradiction with empirical findings certainly raises questions about the model. The model suggested by the reviewer below is an excellent example of it.

For illustration: if the CSS model that the authors use in the supplements is slightly modified by:

dGlc/dt = (Gt+L) – K1 Glc – Ins_sens K2 ins<br /> dIns/dt = K3 Glc - d

(so insulin removal is independent of the insulin level), then at steady state of this coupled system (where dGlc/dt = dIns/dt = 0):<br /> Glc_s = d/K3<br /> Ins_s = {(Gt+L) – K1/K3 d }/(Ins_sens K2)

Thus, Glc at steady state is independent of insulin sensitivity, or glucose production or consumption. It is also said to be perfectly adapted to these parameters. So if Ins_sens is lower, Ins_s will be higher but glc_s remains the same: a perfect basis for the HOMA index!<br /> Only the experiments with reduced removal of Ins (parameter d) would be expected to have lower glucose, but of course this is a very very simple model of glucose homeostasis. Also poor synthesis of insulin by impaired beta cells would lower K3 and this may explain higher fasting glucose levels.

Response: This is an interesting model and a perfect example of how in order to explain one empirical finding the model contradicts many others. Certainly the model accounts for hyperinsulinemia in response to insulin resistance without a change in glucose level. However, it does not explain the results of insulin degrading enzyme knockouts, which would decrease d and is thereby expected to increase glucose, but that does not happen in experiments. Further we simulated using this model to see whether the FG-FI correlation in the steady state would be different than during post glucose load dynamics. Even in this model the regression correlation parameters remain the same and only the range shifts upwards. Thus the model suggested by the reviewer does not account for the experimental and epidemiological results that we cite in this manuscript. <br /> The focus of our manuscript is to look at convergence of many sets of experiments and therefore suggesting a model that satisfies one but not others is not an appropriate solution. <br /> The other problem with the model suggested by the reviewer is that it makes an assumption of constant degradation rate of insulin independent of its standing concentration. Most biochemical decays are known to follow negative exponential. If you want to make an assumption deviant with the general pattern, you need a justification and validation for the assumption. In the case of insulin there is published literature on the half-life of insulin.So the baseline assumption should be that insulin degradation follows half-life dynamics and if you want to make any other assumption, you need convincing justification for it.<br /> So I am a bit puzzled. What is the point of this paper? Does anyone take CSS seriously, really? Again, I do not know all the literature but I am sure there are good models out there that can and do explain T2D and glucose homeostasis very well. <br /> Response: The whole point is that in existing there isn’t a model that does so. Believing that there are good models out there is not sufficient for the reviewer. If there is any kindly point it out specifically. <br /> Should ….(Journal name)…. fix a failure in the education of doctors? And if ….(journal name)… decide they want to do that, please teach them the right vocabulary and conceptual frame work, and properly cite the control theory literature!<br /> Response: We would be glad if control theory has a model that is compatible with all the empirical results pointed out in our manuscript. It is not enough for the reviewer to say that there are. Kindly point out specifically if there really are. As far as we know there aren’t any. But this manuscript is not an intended review of models, it rather lays out the set of experimental results and epidemiological patterns that any model of glucose homeostasis needs to explain. This set has been put together for the first time and that is the main contribution of the paper. Our central argument is that glucose homeostasis needs to take into account all these results TOGETHER. You cannot look at partial picture again and say there are models that are compatible with the partial picture. <br /> To the best of our knowledge, none of the existing models would explain all of them together. We are suggesting here that this is because the set of foundational assumptions of these models is not correct. We are suggesting what change might be needed in it. Building models with the new set of assumptions would certainly deserve a separate publication. Our manuscript is not intended to give the answer, we are defining the question in a broader perspective that has not been taken so far.

Specific comments:<br /> 1. “The belief that this product (HOMA) reflects insulin resistance is necessarily based on the assumption that insulin signalling alone quantitatively determines glucose level in a fasting steady state.”<br /> I really do not get this. See the above simple model: many parameters determine the steady state levels, but if Ins_sens is lower (or L is higher by less insulin inhibition), steady state insulin is higher at the same glucose concentration, so HOMA makes perfect sense to me. Obviously, there can be other ways to change HOMA, but it is simple and effective in the clinic.<br /> Response: HOMA does make sense w.r.t the above model but as pointed out earlier this model has multiple flaws and unless we have a model that is compatible with all experimental and epidemiological results it is difficult to claim that HOMA makes sense.

1. “There is a subtle circularity in the working definition of insulin resistance. Insulin resistance is blamed for the failure of normal or elevated levels of insulin to regulate glucose…. However, clinically insulin resistance is measured by the inability of insulin to regulate glucose. Such a measure cannot be used to test the hypothesis that insulin resistance leads to the failure of insulin to regulate glucose.”<br /> Sorry but the circularity is so subtle that I miss it. If the argument is that insulin regulation is impaired in insulin resistance (what’s in the name), people should measure the action of insulin, right? What is wrong here?<br /> Response: To explain the circularity in different words-<br /> (i) Insulin is unable to regulate glucose because the body has insulin resistance<br /> (ii) Insulin resistance is measured as the inability of insulin to regulate glucose<br /> (iii) Put (i) and (ii) together, it reads “insulin is unable to regulate glucose because of the inability of insulin to regulate glucose”<br /> Isn’t this circular enough or is more clarification needed?

2. line 437: suddenly, “hysteresis” appears out of nowhere. What is this? Please explain properly if relevant, do you really think these poor doctors know what that is?<br /> Response: We agree and will revise the text here to explain the context without the word “hysteresis”.<br /> In brief, the comments by this reviewer are thought provoking and we learnt a lot while addressing them, but they leave us with a little bit of doubt about the soundness of his/her ideas about control theory. <br /> --

Reviewer #3:

This is a very interesting question, and a novel approach to addressing it. I have focussed primarily on the systematic review aspects.<br /> 1. The meta-analysis technique used is essentially "vote counting", and this is not recommended (see https://handbook-5-1.cochra... "https://handbook-5-1.cochrane.org/chapter_9/9_4_11_use_of_vote_counting_for_meta_analysis.htm)") for reasons given in the reference.<br /> Response: Many many thanks to the reviewer for pointing this out. We read the link carefully to find that our analysis is very sound by these guidelines. It does not recommend vote counting in significant versus non-significant types of outcomes. But it clearly says, <br /> “To undertake vote counting properly the number of studies showing harm should be compared with the number showing benefit, regardless of the statistical significance or size of their results. A sign test can be used to assess the significance of evidence for the existence of an effect in either direction”<br /> This is precisely what we have done. So this comment validates our analysis and increases our confidence. Thanks once again. <br /> 2. I could find no mention of a PROSPERO registration - this is important<br /> Response: We agree and will improve during revision.<br /> 3. There is no attempt, as far as I can see, to address the possibility of publication bias<br /> Response: Publication biases are discussed already in the main text line 125-129, but we will elaborate more and also include in supplemental table 3.<br /> 4. The analysis is not reported in a way consistent with the PRISMA guidelines (although these relate to reviews of human data, they have lessons for animal reviews<br /> Response: We made our best attempts to follow PRISMA guidelines for animal experiment reviews as well. It would have been more useful if any inconsistency was specifically pointed out by the reviewer.<br /> 5. There is, as far as I can see, no assessment of risks of bias in the contributing animal studies<br /> Response: We agree and would be glad to improve on. <br /> 6. In my view, it is not enough to say that data will be made available on acceptance - part of peer review should be to ensure that it is made available in a form which is complete, comprehensible and useable, so it needs to be avaialble (even if only through a private link) at this stage.<br /> Response: That is certainly possible and will be done for the revised version.

Regarding the animal experiments these should be reported according to the ARRIVE guidelines, and as far as I can see (I may have missed it, or you may have done it but not reported it) these were non randomised unblinded experiments without an a priori sample size calculation.<br /> Response: We see the importance of reporting these details for the primary experiments that we performed, but for the review and meta-analysis section we do not have control over what the authors did.<br /> In a nutshell, comments by all the three reviewers are a convincing reinforcement that our central argument is sound and strong. We agree with many of the refinement suggestions and look forward to publish a revised version soon.

On 2021-10-14 23:54:29, user Heinz V Bergen wrote:

You need to fix both abstract and PDF: You state W. somnifera (ATRI-CoV-E2) and further down we find W. somnifera (ATRI-COV-E5), which is it?

On 2021-06-01 21:20:00, user Daniel Osorio wrote:

Dear Samuel,

Thank you very much for your interest in our work, as well as for your thorough review and comments. We will try our best to solve your questions below:

1. We agree with your point of view about the effect of a gene knockout on cellular homeostasis. In fact, in metabolic models where this kind of analysis is usually done using optimization approaches, the propagation effect of the gene knockout across several parts of the metabolism is evident. Nevertheless, Santolini and Barabasi (DOI: 10.1073/pnas.1720589115) compared the performance of the topology to predict the perturbation patterns caused by a gene knockout against the result of the optimization methods. They found a good overlap in the prediction, and based on those findings; we decided to use the topological approach. We did not try multi-knockout experiments since, as you mention single-cell RNA-seq characterization for those experiments is not yet available. However, we did try a double knockout and found a good overlap with the findings reported by the paper describing the dataset (Figure 3, Panel C). We yet do not have any other evidence to support or reject your hypothesis about the decrease of the precision of the predictions with an increasing number of knocked-out genes at the same time.

2. Since the regulatory link between two genes is assessed using principal components regression (PCR), the result after evaluating all the associations is a fully connected asymmetric weighted network. You may consider PCR as a partial correlation analysis (pcorr). When C1 and C2 are the same, pcorr(A,B|C1) equals pcorr(B,A|C2). However, in PCR, C1 is not equal to C2—C1 is all genes excluding B, while C2 is all genes excluding A. This asymmetry makes the result of PCR an asymmetric matrix. To further set the directionality of the resultant matrix, first, we removed the weaker link between two genes, and then, we filtered the links below the 95th percentile to reduce the false-positive rate. We provided evidence supporting that the true directionality of the regulation is favored by the regression method with a larger weight value (Figure S1, Panel C) when the directionality is tested using the transcription factors and their target genes reported by the ENCODE database. We also provided evidence of the accuracy of principal components regression to detect the association between genes in single-cell RNA-seq without imputation compared with other methods during the benchmark of scTenifoldNet (Figure 2, Panel A in DOI:10.1016/j.patter.2020.100139).

3. We are afraid we have to disagree with you on this. We did our best to perform an unbiased comparison of the gene knockout phenotypic effect reported by the authors of the datasets and the results provided by scTenifoldKnk. In fact, the characterizations made for the authors are not only based on data-driven approaches using the generated single-cell RNA-seq datasets but also include other experimental techniques. We used several gene-set databases to evaluate the extent of the overlap that we can predict, as it is recognized that a single gene or pathway often cannot fully explain a particular cellular state. Instead, biological processes are better characterized by gene regulatory networks, whose structures are altered as the phenotype changes (DOI: 10.1038/s41540-018-0052-5). We thought about reporting the result of scTenifoldNet and compare them with the results predicted by scTenifoldKnk. Still, since we are the developers of both methods, we decided to better compare our findings with the results reported by the original authors of the datasets. Please note that the original authors did not report all the changes or perturbed gene sets found after the gene knockout. We believe your experimental design is correct and feasible. However, also have in mind that not all the regulatory changes induced by a gene are detectable as changes in the expression level since a given gene may be under the regulation of more of one gene at the same time.

4. To evaluate the stability and robustness of the results provided by scTenifoldKnk, we used two approaches. First, we compared the results obtained by running independently scTenifoldKnk over two biological replicates in the Mecp2 example (Figure S2, Panel D) and found that the overlapping results agree with what is known about the Rett Syndrome pathology that is caused by the malfunctioning of the Mecp2 gene. Second, we resampled the cells used as input for scTenifoldKnk 10 times and compared the rank of the perturbed genes in the predicted in-silico knockout for each one of them. Since single-cell RNA-seq allows to uncover hidden subpopulations of cells with specialized phenotypes, and for that reason, by random subsampling the cells, we expect each of the constructed gene regulatory networks to be unique and provide a unique result. Because the approach is the same and the expected biological effect is the same (randomly sampling cells), we only performed the analysis over the Trem2 dataset. We expect the results to be similar in the other datasets.

Please let us know if you have any other questions or concerns,

Best wishes,

Daniel and James

On 2021-08-24 15:53:56, user Pedro Mendes wrote:

Nice work. Table 1 should indicate that COPASI (of which I am one of the authors) is <br /> capable of both fixed-interval output and actual time step output (which<br /> is obtained by selecting the option "automatic" in our time course <br /> settings).

On 2021-11-29 22:42:36, user Martin Rouse wrote:

Flower et al. PNAS 2021 showed that deletion of the 'ARK' of ORF8 is involved in dimerization. Wouldn't deletion of this sequence affect ORF8 dimerization? If the 'ARK' sequence is really acting as a 'histone mimic' and getting acetylated, wouldn't mutation of the 'K' to anything abolish its function? If acetylation of the 'K' is actually important for SARS-CoV-2 biology, generating a K-to-anything substitution should work to generate a damaged ORF8 protein. Perhaps the phenotypes observed have nothing to do with a H3K9-like sequence, and rather deletion of this sequence simply abolishes the function of ORF8 dimers...

On 2018-04-30 19:31:27, user Tristan Carland wrote:

Great tool!

On 2024-06-07 12:51:36, user Duchenne François wrote:

This work has now been peer-reviewed and published here:<br /> https://doi.org/10.1098/rsp...

On 2020-01-26 06:35:13, user Wes Wong wrote:

Is the methods supplement available anywhere? I can't find it

On 2020-01-25 02:59:28, user AJ wrote:

Where did you see an "ascertain" rate? Your assumption?

On 2021-05-28 05:42:20, user Karl Elshoff wrote:

Because it supports 17 of the best studies we already have that, "None of the studies established a conclusive relationship between mask/respirator use and protection against infection." 1

1. bin-Reza F et al. The use of mask and respirators to prevent transmission of influenza: A systematic review of the scientific evidence. Resp Viruses 2012; 6(4):257-67

I got this from an article written by Dr. Blaylock titled, 'Blaylock: Face Masks Pose Serious Risks to Healthy', posted by Russell Blaylock, MD 11, 2020. I recommend you read what Dr. Blaylock wrote in its entirety.

On 2021-06-02 15:17:27, user Miglet32 wrote:

Something that could be accounted for by other measures such as distancing or increased handwashing protocols.

On 2020-04-19 10:09:36, user Ratnasingham Edward Shanthakum wrote:

I wonder if the humidity in very cold air facilitate the virus to be dried and suspended and taken further apart. Also would there be any electrical charge on the surface attract it to gas molecules.

On 2020-03-28 16:52:02, user Ben Auxier wrote:

I have sent the following questions to the authors by email:

=============================================================

Hello Dr. Santarpia,

I just finished reading your preprint, and I was wondering if you could clarify the following:

1. Most of the samples had RNA copy numbers of 0.1-0.5 /uL. If I am <br /> performing the back caclulations properly, this means the ct value was <br /> between 37 and 41. What was the ct value of the negative controls, or <br /> did the never reach detection threshold?

2. I cannot find any information regarding negative control samples. I see<br /> that you used no template controls, but I do not see for example a swab<br /> of the inside of a sterile container inside the hospital room to <br /> control for contamination during sampling itself and subsequent sample <br /> processing.

3. I do not know if there is an error in the calculations for your table <br /> (labelled as Figure 2), but almost all of your values have SD that <br /> overlap zero. Additionally I notice that your Figure 1A axis cuts off at<br /> zero, which fails to show the SD values overlapping zero. While I agree<br /> there will not be negative copies of virus in your sample, I think <br /> these SD values show something important about your measurement accuracy<br /> and precision.

I have posted these as a comment on the MedRxiv article itself if you would rather respond there.

-Thanks for your time

Ben

On 2021-08-27 15:48:14, user Kim wrote:

What about those who have never been vaccinated nor had any vaccines?

On 2021-08-26 14:57:06, user JK wrote:

Is anyone really surprised by this?

On 2021-09-15 17:18:14, user Chewbacca wrote:

The point of this study is to assess the natural immunity vs vaccine induced immunity. This study doesn't draw any conclusions on whether getting covid is safer than getting a vaccine. It simply evaluates the protection of people who already had covid.

So generalizability is not relevant because the study focuses on post-infection protection and people who died from covid aren't part of this subgroup. The only conclusion here is that the recovered population is statistically less at risk than the fully vaccinated population.

Selection bias is very far-fetched but might have some minor significance, it's hard to tell.

Information bias can just as well go the other way around. What makes you think that vaccinated people are more likely to get tested? Vaccines have been promoted as a miracle solution, so I'd think fully vaccinated people are less likely to go get tested because they think they're safe.

For cofounding, the fact that the population is younger in the previously infected group actually biases the study in favor of the vaccinated. Younger people go out and socialize more and they go to work every day. They are thus more at risk of contracting and transmitting the virus than elders who stay safely at home.

On 2020-04-16 06:06:53, user Hellbound Reaper wrote:

SARS-CoV-2 is the virus name..COVID-19 is the disease name..you can't catch a disease. :P

On 2023-08-13 10:24:43, user Cath Miller wrote:

Why are the "never vaccinated" grouped with those with 1 or 2 doses?

Is there a likelihood that people who reacted badly to 1st or 2nd jab didn't take a third?

On 2021-12-01 22:50:20, user Depp Jones wrote:

"This article is a preprint and has not been peer-reviewed "<br /> 32 times you can find in this article the wording "assume", 10 times assumption and 4 times "we set" and what else ever. Really, you think that will pass a peer-review? And if so apparently only possible in the pharmaceutical or medical industry.

On 2021-12-03 00:07:08, user Nils S wrote:

If it was true that unvaccinated were responsible for 90% of the infections, the incidences in Denmark would not be possible. Denmark’s share of unvaccinated in the population is roughly 1/3 lower compared to Germany. Following the results of the paper, the growth-rate in the exponential function for spreading the virus would be much smaller in DK, which would theoretically lead to much lower infections in DK compared to DE – if the analysis was correct.<br /> However, the incidences (7-day incidence per 100.000) in DE and DK started October 20th at the same level, close to 90. On November 28th Germany peaked with 482 while DK reached 505 and continued to increase. The Danish numbers do not fit to the results of this paper. The incidences should be much smaller due to higher vaccination rates in DK. Thus, the share of unvaccinated does not explain the growth of infections. It rather looks like, vaccinated and unvaccinated spread the virus similarly.<br /> I strongly recommend to test your hypothesis with data from other countries. And furthermore, I have strong concerns with respect to the RKI data used as input for your analysis. Due to the German regulations vaccinated do not test at all – except for a few exemptions. The entire data is heavily biased. UK for example provided much more reliable data. <br /> Best regards<br /> By the way, have a look at this Nature articel: <br /> https://www.nature.com/arti...

On 2021-11-30 07:55:24, user Hartwig Zehentner wrote:

What a tremendous model to prove ones view of life. Models are great, if they do, what they are supposed to do. I have a completely different idea, about the situation: If you force unvaccinated people to do tests for daily procedures or as entry ticket for work. Even if they are asymptomatic. And on the other side estimate even symptomatic (sneezing, cough, etc.) vaccinated people as "negatively tested".. (Example: I had two patients lately with confirmed COVID 19 despite being "fully vaccinated"; if they hadn´t had severe symptoms needing to go to the hospital, they both could have shown their "Vaccinepassport" for a tour through discotheques all night, where unvaccinated people are restricted from entering). And maybe many vaccinated but infected people have only mild symptoms, they surely don´t get tested, because of the green pass...<br /> So i´m very sure you can´t compare the groups of vaccinated and unvaccinated in regard of amount of tetsting. And with the background of vaccinated people with breakthrough infections being at least as infectious as unvaccinated people, for me this blaming of unvaccinated people is only propaganda, reminding me of germays worst times.<br /> Dr. med Hartwig Zehentner DESA EDIC

On 2021-08-25 10:47:17, user ibamvidivici wrote:

In Figure 1 c is a infectiouness profile startet ca. 10 days before symptoms onset. But Figure 3 shows, that the meassurement startet 4 days before symptoms onset. How is that possible?

The infectiousness profile is not the real infectivity, it is the viral load of the tested person, estimated from the Ct-Value. For real infectivity the viral load had to be transfered to another people. After symptoms onset this happens with cough and sneeze. I doubt, that this happens before symptoms onset, because the only possibility would be by breathing. But Aerosol size of breathing droplets ist smaller than 1 micron and is vaporized in less than 1 ms, so before it settles onto a desk or towards other people. It's not proofed, that the virus is still intact after vaporisation process of the aerosol droplet.

(only relatives could become infected from asymptomatic by kissing or shared cutlery.)

On 2025-02-22 17:25:17, user Shawn M wrote:

The study's questionnaire has significant design flaws. The main issue is how the questions are worded - they repeatedly ask about 'health conditions that you have had as a result of vaccine injury.' This phrasing assumes vaccines caused these health problems before even asking the question. It's like asking 'When did you stop stealing?' instead of 'Have you ever stolen anything?'<br /> This problematic wording can influence how people respond in two ways. First, it might lead people to automatically connect their health issues to vaccines without considering other possible causes. Second, by focusing only on vaccine-related problems, the questionnaire misses important information about people's overall health that could explain their symptoms.<br /> These issues make it difficult to trust the study's findings because we can't tell if the health problems reported were actually caused by vaccines or if they happened for other reasons that weren't explored.

On 2020-04-25 16:58:34, user Mike wrote:

As a reminder, medrxiv.org displays this on their opening page:

Caution: Preprints are preliminary reports of work that have not been certified by peer review. They should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.

(I added the emphasis)

On 2020-04-24 05:20:43, user Olexiy Buyanskyy wrote:

Where is the zinc? Zelenko pointed that zinc is required!

On 2020-08-11 14:22:58, user David Curtis wrote:

Hi.

Just to say that you might want to consider citing this paper, which also analysed exome sequence data from ADSP:

https://onlinelibrary.wiley...

Regards

• Dave Curtis

On 2021-07-26 17:44:49, user Fortu Nisko wrote:

From expected results.

We intend to present the evidence in three distinct packages: study description, methodological quality assessment and data extracted. We intend on summarising the evidence and drawing conclusions as to the quality of the evidence.

Fair enough. A ruthlessly non-politiccal assessment of the quality of scientific evidence will be the most significant portion of the research. Perhaps you might add to the discussion portion of your research paper the impact of basing policy on evidennce that does not meet the standard for policy-grade evidence. This is a very important discussion. Policy-makers seem to be ignorant of the standards necessary to draw conclusions that then can translate into sound policies for public health. They seem to have fallen into the trap of self-perpetuating policies that become untethered from an assessment of the quality of evidence.

Wishing you luck and good fortune in your pursuit of a worthy goal re quality of evidence.

On 2021-11-09 13:00:01, user ingokeck wrote:

Dear Authors, two Questions:

(1) You state: "Partly vaccinated was defined as having received the first dose of a <br /> 2-dose schedule with a time since vaccination of at least 14 days." So you counted freshly vaccinated persons as not-vaccinated? IMHO this is a bad idea, because in the first 14 days after the 1. dose it is well known that the immune system is impacted by the vaccination and a high risk of testing positive for Covid19 exists. If you count these cases as not-vaccinated, this will skew your results towards higher vaccine effect.

(2) Thanks for plotting the case counts in figure 1. Did you check if there is some temporal imbalance in the cases? It seems the second part of your data interval has a substantial lower infection risk and may have higher vaccination numbers, i.e. you may have data that skews towards vaccinated in the lower risk time, also accounting for part of the measured vaccination effect. Could you please have a look at this as well? Thanks.

On 2021-08-03 11:19:56, user TBV wrote:

1. Since we don’t know characteristics of patients in each arm, these results could simply reflect the vulnerable, with weaker immune systems, being more likely to be vaccinated.
2. The authors throw out roughly 1/3 of the observations in an already small study because viral loads are low. Isn’t it possible these were mostly vaccinated people? So, by cutting off patients to only those with a fairly high viral load we generate the result that vaccinated people in the study have a high load

On 2020-02-10 07:08:01, user ScientistCN wrote:

Only 1099 patients were analyzed, how can we get the significant figure to 0.01%?

On 2020-04-19 19:15:06, user Michael A. Kohn, MD, MPP wrote:

From the 3439 people who showed up for testing, they were able to obtain 3330 valid specimens on which to perform the Premier Biotech serology test. Of these, 50 were positive. That’s 50/3330 = 1.5% . They tried to adjust for the fact that the people who actually showed up were not representative of the county population’s sex, race, and zip code distribution. But the main potential source of error is the accuracy of the test. At a low sero-prevalence like this, a small proportion of false positives can result in a large overestimate. They ran the Premier Biotech test on 30 serum specimens drawn prior to the pandemic and it was negative on all 30. If the error rate on truly uninfected individuals is 0.5%, and the test properly identifies 91.8% of previously infected individuals, then the true sero-prevalence is 1.1%. As the authors say, “Additional validation of the assays used could improve our estimates and those of ongoing serosurveys.” Having reviewed the test accuracy studies of this and other lateral flow immunoassays (http://covid-19-assay.net/ ), I believe we will end up with a true sero-prevalence of about 1% in Santa Clara County. But the authors made a reasonable estimate and did a great job of collecting this data and reporting their results and assumptions.

On 2020-04-24 06:11:48, user JM V wrote:

Oh, and for people who compare this to the flu, here is some lowballing of the disease:<br /> ~5 times higher expected infection fatality rate<br /> ~5 times higher expected infection rate w/o control measures<br /> Multiply those out for me please.<br /> I think that is a comparison.

On 2020-04-18 16:15:43, user spacecat56 wrote:

In reading the draft report of the study (pre-print, dated April 11, 2020) my predominant thought was, in choosing to respond to the invitation to the study participants are overwhelmingly likely to have self-selected based on their recent prior experience of symptoms that they suspect may have been due to COVID-19 infection.

The draft acknowledges the possibility of this bias but tosses it off as "hard to ascertain". But the draft also says that data on prior symptoms were collected; data which are entirely omitted both from the published analysis and from the published tables.

Because the analysis ignores this factor and because of the potential for this bias to totally dominate the analysis, in my opinion after reading the study draft, we still know effectively nothing at all about the prevalence of infection in the studied population. Accordingly I would expect to vigorously object to any attempt to incorporate the reported results into public policy and planning.

I would urge the study team to bend their efforts to addressing this deficiency. At a minimum, I suggest, the report should include the withheld prior-symptoms data. Preferably, some efforts should be made to deal with the difficulty of estimating the bias. Perhaps it would be helpful to subdivide the sample data based on yes/no prior symptoms and analyze each subset?

On 2020-04-24 05:00:13, user tom wrote:

And it works out to an IFR for NYC of at least 0.56% (counting diagnosed covid deaths only), 0.85% (including undiagnosed probable covid deaths), to 1.07% (all excess deaths). One more piece of evidence indicating that Stanford's test kits, methods, and/or analyses here led them to an IFR range (claimed 0.12-0.2% if not lower) that's about 5x too low

On 2020-04-22 16:02:39, user Texas Longhorns wrote:

The research paper does not indicate how many of those that participated had already been tested for Covid and what those test results were.

If they over sampled people that had already tested positive and recovered of course you will get a higher rate of positive antibodies. That would not be indicative of the general population.

There is also the problem of false positives because the test can trigger for the common cold that is also a coronavirus.

I don't think this research passes muster as any reliable indication of antibodies in the general population and should absolutely not be used as a basis to reopen businesses and large public gatherings.

Having antibodies to one strain of the virus may not give you any immunity to the more than 8 strains of Covid we know are out there.

Even if the test results are accurate at 2% that is nothing and you need at least 60% solid immunity to consider any large population to have herd immunity protection.

On 2020-09-07 15:19:58, user VirginiaBoy1969 wrote:

This appears to look at weekly data, not annual, so you may be reading it wrongly. This is a methodological study, not a risk of infection study.

On 2020-09-14 14:10:20, user AlwaysThinking wrote:

You might find this insightful.

https://www.youtube.com/wat...

On 2021-01-22 12:24:37, user Jeany wrote:

See the final version published by Analytical Chemistry (ACS) (https://doi.org/10.1021/acs.analchem.0c04497) "Covid-19 Automated Diagnosis and Risk Assessment through Metabolomics and Machine Learning"

On 2020-05-17 07:28:24, user Robert Ray wrote:

Would this suggest that plasma transplants from recovered patients might supply the responsive T-cells that are missing in severe disease patients?

On 2020-03-27 17:09:13, user Peterson Biodiversity Lab wrote:

So one prediction from the models presented by Miguel Bastos Araújo and Babak Naimi was that of low or no local transmission of COVID-19 in humid tropical countries. They stated, "Much of the tropics have low levels of climate suitability for spread of SARS-CoV-2 Coronavirus owing to their high temperatures and precipitation... human populations will likely be spared from outbreaks arising from local transmissions..." Two weeks or so of further data say that that prediction is not robust--rather, it is proving quite wrong. See attached image... source: https://coronavirus.jhu.edu... https://uploads.disquscdn.c...

On 2020-08-31 13:17:21, user Kamran Kadkhoda wrote:

Great work! This suggests the specificity of the Euroimmun assay is around 33%!

On 2025-10-07 13:23:10, 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:

The study examines the effect of cigarette taxes on smoking behaviors (participation, cessation, and intensity) and whether these effects differ by polygenic indices and timing of exposure to cigarette taxes.

The authors find that cigarette tax exposure during adolescence is a determinant of lifetime smoking status (cigarette tax is a deterrent of smoking participation), and the effect of cigarette taxes during adolescence is significantly higher for individuals with a higher genetic predisposition for smoking. The authors also find that ordinary least squares models underestimate the detrimental effects of smoking on chronic disease.

Future studies can explore other genetic ancestries and within-family GWAS.

Highlights the importance of youth-targeted tobacco taxes, taking into account the risk of initiating smoking.

On 2020-04-30 00:28:52, user conceitedlawyers wrote:

The paper is very important and interesting. Almost all commentators didn't seem to read the full paper. The author points out that the dosage considered in the in vitro studies is unlikely to be high enough. However, unlike 9/10 commentators, the author of the paper does NOT say that Ivermectin should be ruled out. The author suggests alternatives (e.g combination therapy using a mix of antivirals). I respectfully concur with the author and dissent from the views expressed in almost all comments above.

On 2021-04-13 23:06:29, user disqus_pagO5NCOKq wrote:

From the abstract above: "After the second vaccination, 31.3 % of the elderly had no detectable neutralizing antibodies"... does that mean the vaccination offers NO benefit to 1/3 of the "elderly"?

On 2020-11-03 05:06:24, user KOTTAISAMY K wrote:

Hoe to download the dataset from Taiwan's National Health Insurance Research Database (NHIRD).kindly share the dataset link or share the dataset. its very useful for My Research works. Otherwise share the dataset to mail. THANK YOU..

On 2021-08-23 04:17:45, user Anon wrote:

How about selection bias? Vaccinated people take covid more seriously which would lead to less infections.Not to mention this study was before Delta took over so the results are irrelevant

On 2021-07-23 21:59:46, user Vuong Trieu wrote:

This paper is now published at

https://www.openaccessjourn...

On 2021-02-09 15:09:34, user Gemma Quinn wrote:

i would say Colchicine should be considered and to avoid steroids for chronic situations

On 2022-01-13 18:25:17, user Mackenzie Lee wrote:

I think it's somewhat difficult to make solid claims re incident rates, etc, due to self-reported/-selected data collection via a Facebook site dedicated to survivors of COVID. The rapid data turnaround is nice of course, but a follow up with random sampling will be needed to substantiate claims.

On 2020-03-25 15:31:18, user Sinai Immunol Review Project wrote:

These authors compared the ABO blood group of 2,173 patients with RT-PCR-confirmed COVID-19 from hospitals in Wuhan and Shenzhen with the ABO blood group distribution in unaffected people in the same cities from previous studies (2015 and 2010 for Wuhan and Shenzhen, respectively). They found that people with blood group A are statistically over-represented in the number of those infected and who succumb to death while those with blood group O are statistically underrepresented with no influence of age or sex.

This study compares patients with COVID-19 to the general population but relies on data published 5 and 10 years ago for the control. The mechanisms that the authors propose may underlie the differences they observed require further study.

Risk stratification based on blood group may be beneficial for patients and also healthcare workers in infection control. Additionally, investigating the mechanism behind these findings could lead to better developing prophylactic and therapeutic targets for COVID-19.

On 2022-03-01 12:10:22, user Eric Fauman wrote:

There's a reason we use 5e-8 for detection of significant GWAS hits and that's because below that you're swamped with associations that are likely not real. You shouldn't do pathway enrichment on genes identified from SNPs at 1e-6; the results are likely meaningless.

On 2020-08-23 18:52:01, user Michael Shodell wrote:

Greatly enjoyed this paper as the logic, assumptions, and<br /> analyses are very-well described and readily followed. This also enables good critical assessments<br /> of the range of confidence to place in the numbers at which the author<br /> arrives. HOWEVER – by ignoring<br /> everything other than the sedentary in-flight, non-perambulating and generally<br /> observant passengers (see excerpts below), the author may have missed the<br /> greatest risk areas of flying.

For instance, when disembarking at the destination, the more<br /> crowded the plane (eg. a plane with middle seats occupied), the riskier this<br /> part of travel. I can tell you from recent<br /> experience that for up to 10 minutes passengers crowd the aisles awaiting disembarking<br /> and, being the flight conclusion, often with masks at half-staff or barely<br /> covering the face at all.

Probably similar consideration for use of toilets and aisle<br /> movement during the flight.

106 We focus on a particular passenger who is traveling<br /> alone, and assume that the primary

107 infection risk for this passenger arises from other<br /> passengers in the same row. We further

108 assume that additional risk arises from passengers in<br /> the row ahead and row behind. For two

109 reasons, we treat the risk posed by other passengers as<br /> negligible …

… we treat the risks associated with boarding

119 the aircraft, leaving the aircraft, visiting the<br /> lavatory, and touching surfaces in

120 the passenger cabin, as second-order effects.

On 2021-02-22 23:08:15, user Meg Beller wrote:

HOW do I get a neutralizing antibody titer test post Pfizer vaccine to see how my immune suppressed body responded?

On 2020-04-03 16:05:53, user John B wrote:

This is correct. In Hubei, daily deaths were not Gaussian. They were not symmetric about the peak date, rather there was a slower decay compared to attack (longer tail). There are better sigmoidal functions than erf. This study could probably be significantly improved. Also, holding some parameters from a Hubei fit constant in application to other geographies could address the issue with under-predictions from early-stage data, as is the case essentially everywhere in the United States. See https://twitter.com/JohnBur... for a peak at a similar-in-spirit but petters better-done estimates for several European countries.

On 2020-04-03 17:03:45, user just maybe wrote:

There are SIR models available that are more thorough.<br /> E.g. (Researchgate)

Batista, Milan. (2020). Forecasting of final COVID-19 epidemic size (20/04/03).

On 2020-04-01 16:28:50, user Erik Likness wrote:

Also, looking for the modeling methodology... Thank you.

On 2020-05-19 16:26:56, user Wizard of Oz wrote:

This study claims to compare the risk of dying of COVID19 to the risk of driving a car. It does so by assuming the former can be measured by the number of deaths that occured in a given timeframe divided by the population size in M. That is an utterly misleading metric. First of all, the data for COVID19 is incomplete (the pandemic is not over yet). Also the study does not take into account that up to 80% of the population can get infected if the virus is left unchecked, and that this has secondary effects causing many more die for lack of treatment. In conclussion the validity of this study's conclusions is highly doubtful.

On 2020-03-14 06:52:08, user Muhammad Yousuf wrote:

Hypokalemia is caused by SARS-CoV-2 virus due to its affinity for the Angiotensin Converting Enzyme (ACE) receptor that is present in the lungs, heart, blood vessels and the gastrointestinal tract of humans. It has been suggested from animal experiments that medications inhibiting this receptor (called ACEI or ARBs) could be a potential management strategy(1-2). Because ACEI and ARBs are medications mainly use for high blood pressure and would lower the BP, it is recommended that these medications should at least be used in patients with COVID-19 who are already suffering from hypertension or whose BP is not lower than 100 mm Hg systolic.

It would also be interesting to know the recovery and death rate of COVID-19 patients with hypertension or heart failure who were already using an ACEI or ARB medications compared with those who were not on suchmedications.

Abbreviations: ACEI= Angiotensin Converting Enzyme Inhibitors, ARBs= Angiotensin Receptor Inhibitors, BP= Blood pressure

References<br /> 1. Gurwitz D. Angiotensin receptor blockers as tentative SARS-CoV-2 therapeutics. Drug Dev Res. 2020 Mar 4. doi: 10.1002/ddr.21656. [Epub ahead of print]<br /> 2. Dimitrov, D. S. The secret life of ACE2 as a receptor for the SARS virus. Cell, 2003; 115(6), 652–653.

On 2021-10-14 19:09:32, user Dave Green wrote:

This trial was listed as one of the clinical trials being done on Ivermectin on the FDA website. The media has portrayed the use of this drug as completely ridiculous and idiotic. Yet, there are several clinical trials underway to prove or disprove its effectiveness. If its use is so "idiotic" then why would highly educated people bother to study it???

This is an example of one (of several) that shows it is effective and has promise. From what I have read this "effectiveness" increases if it is given early on. Where I live in Canada, if you have symptoms you are just told to self isolate until they become so bad you have to be hospitalized. You are not provided Vitamin D, Zinc, Ivermectin or any other form of treatment that could help prevent you from being hospitalized. Everyone criticizes any study that comes out and nothing can be proven or believed. It is so frustrating.

Thank-you to the people who performed this study. Your efforts to help the world are appreciated, if not by everyone, at least by me.

On 2021-08-28 16:42:57, user MS Simon wrote:

"Ivermectin for Prevention and Treatment of COVID-19 Infection: A Systematic Review, Meta-analysis, and Trial Sequential Analysis to Inform Clinical Guidelines"

Does the American Journal of Therapeutics routinely publish pseudoscience and quackery, or just sometimes?

Most importantly, what would motivate a doctor or a researcher to go to the trouble of creating and publishing a flawed or biased study for a generic drug that costs 1/3 of a cent per dose to manufacture?

On 2021-10-05 22:04:39, user Brooke wrote:

It would be useful to know what sorts of samples were used for PCR testing — were they nasal swabs or saliva?

On 2023-11-04 15:16:53, user Clive Bates wrote:

Two problems here.

First is scalability. This doesn't sound like an intervention that would engage many veterans, nor does it seem likely to be affordable or practical at the scale necessary to achieve a turnaround in the aggregate burdens arising from smoking.

Tobacco-related deaths exceed those resulting from homicides, suicides, motor vehicle accidence, alcohol consumption, illicit substance use, and acquired immunodeficiency syndrome (AIDS), combined.

Almost all of that excess mortality is attributable to smoking not nicotine. Tobacco harm reduction approaches may deliver more and sooner - e.g. encouraging migration to smoke-free alternative forms of nicotine use such as vaping.

Second, it is quite possible that veterans with forms of PTSD are benefiting in some way from the functional and therapeutic properties of nicotine. Again, an approach to smoking cessation that does not demand nicotine cessation may achieve nearly all the health benefits of quitting smoking without demanding withdrawal from nicotine use.

The trial could at least consider an additional arm to assess the utility of encouraging vaping for smoking cessation. It might achieve more for less.

On 2021-01-21 09:05:28, user Dominik wrote:

The conclusion drawn here is simply wrong: "suggesting that current SARS-CoV-2 vaccines will protect against the 20B/501Y.V1 strain" when in fact they didn't check for all 17 epitope changes of mentioned strain but only N501Y which was never thought to be immune evasive. The same erroneous conclusion was drawn in the paper of Uni Texas which also only tested against N501Y but not all mutations.

On 2025-04-02 10:00:44, user Md Shahed Morshed wrote:

The published version can be found here: https://doi.org/10.3329/jacedb.v3i2.78642

On 2020-09-18 20:29:54, user David C. Norris, MD wrote:

This paper is fundamentally misconceived:

Biostatistically

This paper apparently arises out of the biostatistical perspective which presently dominates the design and analysis of dose-finding trials in oncology. Yet even by purely statistical standards, it suffers serious shortcomings. Most notably, it looks for an interaction (viz., dose-response) without first demonstrating or ensuring the existence of a main effect. Reference #153 in this paper (Hazim et al. 2020) reported a 5% median response rate in a systematic review of recent dose-finding trials. Would the authors venture to estimate what fraction of their 93 ‘analysis series’ employed a drug with a substantial therapeutic effect? Some indication might be found in what fraction of the treatments unequivocally demonstrated a therapeutic effect in subsequent phase 2 or 3 trials. Adashek et al. (2019) document a secular trend in overall response rate (ORR) observed in phase 1 trials which is “now almost 20%, or even higher (~42%) when a genomic biomarker is used for patient selection.”

Also arguably well within the purview of biostatistics would have been a decision-theoretic framing of phase 1 cancer trials. These trials may be understood as the earliest clinical steps in a learn-as-you-go (adaptive) drug-development process (Palmer 2002; Berry 2004). On such an understanding, aiming to treat early-phase participants at maximum tolerated doses (MTDs) in no way “dictates that an assumption is made … that higher doses are always more efficacious” (p. 4; italics in original). The authors’ use of “dictates” suggests they see something of logical necessity in this, and their further insertion of the logical quantifier “always” only exacerbates their overreach in formulating this central tenet of their study. Even the distinction between a logical assumption and a statistical prior gets lost in the shuffle. To remedy all this, the authors might consider attempting to state formally their understanding of the individual phase 1 trial participant’s decision-problem, complete with its essential uncertainties and some plausible utilities. (Within the community of investigators whom they address in the final paragraph of their Discussion, there is, I believe, broad agreement on the doctrine that these trials have therapeutic intent (Weber et al. 2016; Burris 2019). The authors would do well to take this patient-centered view as their starting point, as opposed to the dose-centered and unitary goal they proclaim at the end of their current Discussion.)

Furthermore, statistics is nothing if not a discipline for “mastering variation” (Senn 2016), and a paper that sets out to question the strict monotonicity of dose-efficacy ought also enquire as to the presence of inter-individual heterogeneity in dose-response. Note that such heterogeneity would tend to attenuate the maximum slope of a convex dose-response in aggregate.

Finally, the absence-of-evidence fallacy is widely appreciated among professional statisticians, yet seems to have been indulged liberally here without any safeguards such as are usually provided by power calculations.

Pharmacologically

Within statistics, there is a doctrine that statistical analysts should always engage ‘subject-matter experts’. But one sees in this paper no sign that any pharmacological concepts—let alone expertise—have been brought to bear on what would seem to be a pharmacological question. At a minimum, in any serious challenge to the ‘MTD heuristic’—as I have called it—one expects to find distinctions between on-target and off-target toxicities. In an analysis that invokes dose-response plateaus (whether these are conceived as approximate or absolute in this paper remains unclear), we ought to find discussion of receptor occupancy and saturation as underlying realistic mechanisms.

To some extent, a neglect of subject-matter knowledge may be embedded in the very form of the present analysis, which tries to deal with its question in aggregate (through statistical techniques such as standardization) rather than in its particulars.

Clinically

In the final paragraph of their Discussion, the authors proffer advice to clinical investigators. In light of the limitations—statistical, logical, subject-matter—catalogued above, this is premature and should be omitted. Any given phase 1 clinical investigator will be considering a candidate drug in its particulars, conditional on a great deal of preclinical data and perhaps even nontrivial PKPD and systems-pharmacology modeling. The authors acknowledge as much (p. 16), seeming to appreciate that they have conducted an unconditional analysis of highly conditioned decision-making. To investigators thus intimately engaged with pharmacologic particulars, the null conclusions from a marginal analysis such as this one can contribute little useful guidance. If it were proposed to submit this work for peer review in substantially its present form, only a statistical audience should be addressed—and then solely with a cautionary note that the finding of a dose-response interaction will not leap out at a statistician from a convenience sample of phase 1 studies in which a therapeutic main effect remains dubious and unexamined. The main lesson of this work is that statisticians ought to investigate questions of pharmacology in their particulars, and with recourse to subject-matter concepts and expertise.

References

Adashek, Jacob J., Patricia M. LoRusso, David S. Hong, and Razelle Kurzrock. 2019. “Phase I Trials as Valid Therapeutic Options for Patients with Cancer.” Nature Reviews Clinical Oncology, September. https://doi.org/10.1038/s41....

Berry, Donald A. 2004. “Bayesian Statistics and the Efficiency and Ethics of Clinical Trials.” Statistical Science 19 (1): 175–87. https://doi.org/10.1214/088....

Burris, Howard A. 2019. “Correcting the ASCO Position on Phase I Clinical Trials in Cancer.” Nature Reviews Clinical Oncology, December. https://doi.org/10.1038/s41....

Hazim, Antonious, Gordon Mills, Vinay Prasad, Alyson Haslam, and Emerson Y. Chen. 2020. “Relationship Between Response and Dose in Published, Contemporary Phase I Oncology Trials.” Journal of the National Comprehensive Cancer Network 18 (4): 428–33. https://doi.org/10.6004/jnc....

Palmer, C. R. 2002. “Ethics, Data-Dependent Designs, and the Strategy of Clinical Trials: Time to Start Learning-as-We-Go?” Statistical Methods in Medical Research 11 (5): 381–402. https://doi.org/10.1191/096....

Senn, Stephen. 2016. “Mastering Variation: Variance Components and Personalised Medicine.” Statistics in Medicine 35 (7): 966–77. https://doi.org/10.1002/sim....

Weber, Jeffrey S., Laura A. Levit, Peter C. Adamson, Suanna S. Bruinooge, Howard A. Burris, Michael A. Carducci, Adam P. Dicker, et al. 2016. “Reaffirming and Clarifying the American Society of Clinical Oncology’s Policy Statement on the Critical Role of Phase I Trials in Cancer Research and Treatment.” Journal of Clinical Oncology 35 (2): 139–40. https://doi.org/10.1200/JCO....

On 2020-04-18 21:04:02, user Katri Jalava wrote:

Manuscript does not include references for the methodology used. Furthermore, the mathematics behind the model is not being presented. More rigorous, referenced comments why you choose to use a model that is not widely used in infectious disease outbreak modelling would be useful, and preferable present the results in parallel with a standard SEIR model. You could also discuss IHME model.

For the parameters:<br /> • I am not sure how you calculated the deaths. If you used Wuhan data, all case fatality numbers need to be revised as China updated its numbers. This is also obvious from your figure 6. There are 61 deaths as per 18 April in HUS (and it does not include all 48 deaths outside hospitals), and ~ this number should be reached only around 1 May. There is something else wrong than just the Chinese number, I think. If I understand correctly from the text that you may have calculated the deaths from HUS data, it goes badly wrong, I am afraid. There is literature how to correct the ongoing outbreak death numbers to get accurate estimates, or use Chinese numbers. Calculating the mortality rates is one of the most challenging things. Even though new cases would stop today, number of deaths would increase for the next 3-4 weeks from the current case load. Simply dividing the number of deaths by total number of cases may only be used post-outbreak. <br /> • Individual characteristics should include underlying illness if possible, not only age. This is probably available from TTR, and there is surely some sort of enhanced surveillance done.<br /> • Would it be possible to estimate the success of movement restrictions based on overall mobile phone data?<br /> • Excretion is by disease severity/age, https://doi.org/10.1016/S14.... This is likely (one of) the reason(s) why the outbreaks are so explosive in the elderly people’s homes. But as the illness is often mild(er) during the first week (when cases infect onward), and it was also noted in the mentioned publication that there was not a difference between severe and mild cases in the initial/peak excretion (but # observations small), this may not need to be taken into account, but would need to be mentioned. Excretion (or lack of it) may need to be taken into account with children.<br /> • Cases should be ideally categorized to travel, community acquired and mass gathering participants as well as household contacts. These all have different time spent in the community before testing and isolation. Help line has probably an algorithm for the cases which could be used.<br /> • Massong 2008 is pretty outdated reference for a contact matrix, there are more recent ones. Note that especially school aged children from abroad may not be valid as there are no boarding schools in Finland.<br /> • Relative infectiousness period is quite short, it is up to a week, please refer to<br /> https://doi.org/10.1038/s41...<br /> • P(infection|virus contact), test more values, this is out of a hat(?) Needs a distribution (gamma) around it.<br /> • P(symptomatic|infection) 50 %, Ferguson’s figure includes mild cases, ie. it is not really asymptomatic, but “non-GP-seeking”.<br /> • P(death|severe, not hospitalized) 20 % seems too low. Please have a look on the elderly home data.<br /> • The assumption that test positive would lead to 0 % transmission is likely quite false. Most of the mild cases remain at their homes (they should ideally be isolated to a hcf, but this is unlikely happening). There are publications describing household cases where this may be estimated. This could and should be assessed ideally from HUS case data.

A positive thing is that your study clearly shows (what was also known from international data) that suppression works well, mitigation is of less use. This is also logically evident when there is not major community transmission ongoing.

On 2021-07-12 13:43:17, user metalhead wrote:

Hi Tobi,<br /> but isn‘t it a correct consequence that one reconsideres his own risk in terms of such findings ? I am not talking about inducing fear but discussing these things

On 2021-12-20 23:18:35, user Nico wrote:

One more comment - it seems the survey was originally designed to look at impacts of covid itself on menstrual cycles - has that analysis been done? It would be useful to mention in this paper as well. If not already done - that seems like a good control: how do the effects of vaccination on menstrual cycles compare to covid itself? People get so focused on effects of vaccination, forgetting that in many cases effects of covid are far worse. Thanks. (Going to go and search now to see what I can find!)

On 2021-07-03 05:01:14, user Covid wrote:

Is this pre-print going to be published on real peer reviewed journals?

On 2023-09-04 21:32:53, user Joilson Xavier wrote:

This study is now published in Nature Communications doi:10.1038/s41467-023-40099-y

On 2020-04-21 19:31:05, user Pierre Balaz wrote:

Just to get all the details : which was the posology of then medications used ? (HCQ and AZT) ?

On 2020-04-22 12:15:25, user Thomas Aquinas wrote:

Yet in a poll of 6000 doctors worldwide who have actually, personally treated CV patients (unlike Birx and Fauci), doctors rated HCQ and Zithromax as the top #1 and #2 drugs in efficacy. (37% HCQ #1, 32% Azithromycin #1).

The effective protocol is already well-established. Drug therapy should begin at the onset of breathing difficulties, not after patients have been placed on ventilators, from which only 20-50% of patients currently survive. Doctors Zelenko and Didier have had success rates of well over 90% when following the standard protocol.

On 2020-04-22 15:03:20, user Eric Hall wrote:

But not a prospective study with a randomized control group. How do we know the HCQ groups weren't just sicker and it was used more like maximum medical therapy. Correlation doesn't equal causation.

On 2020-07-11 04:42:51, user Tom Jarman wrote:

the authors reached the conclusion that masks do not have a significant difference in person-to-person transmission for influenza-like illnesses, yet they still recommend use of masks. What am I missing here?

On 2025-08-07 18:34:27, user Sabir Awad Mustafa wrote:

Peer Review for Preprint (medRxiv)<br /> Title of the Preprint:<br /> Distribution, epidemiology, and antimicrobial resistance pattern of gram-negative bacteria isolated from blood: a retrospective study in a tertiary care hospital, Dhaka, Bangladesh<br /> Preprint Server: medRxiv<br /> Posted: June 26, 2025<br /> DOI: https://doi.org/10.1101/2025.06.25.25330327 <br /> Reviewer: Dr. Sabir Awad Mustafa Mohammedzein, Consultant in Medical Microbiology and Infectious Diseases<br /> The preprint delivers important information about gram-negative bacterial epidemiology and resistance patterns, which cause major healthcare-associated infections globally. The research comes at a crucial time because it addresses the growing problem of multidrug-resistant organisms (MDROs). The authors have gathered extensive data, which they present in an organized manner. The research confirms worldwide worries about antimicrobial resistance (AMR), particularly for Klebsiella pneumoniae, Acinetobacter baumannii, and Pseudomonas aeruginosa<br /> Strengths:<br /> The study presents essential resistant microorganisms together with geographically specific findings.<br /> The study uses well-arranged tables and figures to improve comprehension.<br /> The research properly focuses on MDR organisms together with ESBL production and carbapenem resistance because these issues are both critical and current.<br /> The discussion demonstrates an understanding of worldwide public health consequences. Suggestions for Improvement:<br /> Methodology Clarification<br /> The study needs additional information about how researchers chose their isolates and the specific period they included. Was it retrospective or prospective?<br /> Antibiotic Usage Data<br /> The inclusion of hospital antibiotic usage data expressed in DDD/1000 patient-days would improve the accuracy of resistance pattern correlations.<br /> Infection Control Factors<br /> The study would gain value by examining whether screening and isolation practices formed part of the surveillance system.<br /> Resistance Mechanisms<br /> The study would gain substantial strength through the inclusion of molecular data, which includes bla gene detection and MBL identification.<br /> Statistical Analysis<br /> The study needs more details about the statistical methods used for comparing resistance trends and determining their statistical significance.<br /> Minor Comments:<br /> The first occurrence of each abbreviation, including ESBL, MDR, and CRAB, must be written out in full.<br /> The conclusion needs a better distinction between research results and proposed recommendations.<br /> Overall Impression:<br /> The research provides significant value to the field of AMR and gram-negative pathogens in healthcare environments. The research findings confirm worldwide resistance patterns that emphasize the need for immediate antibiotic stewardship programs and resistance control measures.<br /> The authors should revise their work based on these clarifications to enhance both the study's impact and clarity.<br /> Reviewer’s Professional Statement:<br /> My role as a medical microbiology consultant and hospital laboratory and blood bank director helps me understand the essential importance of surveillance research. My experience as a director of multiple antimicrobial stewardship and infection control improvement projects has shown me that this preprint holds great potential to shape clinical practice after peer review and validation.

On 2021-07-26 09:07:20, user Jörg Hennemann wrote:

Dear authors, I do not get the point: In your raw data (table 1) the percentage of people dying from Corona Delta is 0.7%. All other variations cause 0.9% deaths for infected people. So, how can the risk to die from Delta be higher than for other variants? Where can we see how the "adjustment for age, sex, comorbidities, health unit, and temporal trend of the raw data works? Here in Germany people go wild because of this study, but I can not comprehend it. Thank you very much!

On 2020-09-03 20:23:26, user Mahdi Rezaei wrote:

Please see the demo results of this research in our 2-minute video clip below: <br /> https://youtu.be/FwCP2ySDshE<br /> I hope you like it. Your valuable comments/advice will be highly appreciated.

On 2020-05-02 06:26:29, user Jasmin Zessner wrote:

How come the authors only looked into countries most affected by SARS -COV-2 while ignoring the ones where lockdown was effective (Austria, Germany) and extrapolate that “lockdown is not effective in western Europe”

On 2021-07-06 06:43:05, user Fat wrote:

Someone please correct me if I'm mistaken, but this study relates only to T and B cell antibody reactions to the spike protein. It says nothing about all the other antibody proteins that the disease might have induced to differentiate and create variants, no?

On 2021-09-11 14:15:42, user TheBonesm wrote:

Exactly this. The CDC's page for VAERS states "It is not possible to use VAERS data to calculate how often an adverse event occurs in a population," however that is exactly what the authors have done. I am sure this will be caught in peer review.

On 2022-01-12 11:43:46, user kdrl nakle wrote:

There is nothing in this paper worth beyond what is already expected. The numerical predictions will likely be erroneous. I have no idea why would anybody want to write the stuff like this that wil be outdated in two weeks time.

On 2020-12-28 18:07:37, 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.

(Revised, not yet copy-edited)

https://doi.org/10.2196/22617

On 2020-04-06 11:57:49, user Sinai Immunol Review Project wrote:

Main findings<br /> It has been previously reported that COVID-19 patients exhibit severe lymphocytopenia, but the mechanism through which this depletion occurs has not been described. In order to characterize the cause and process of lymphocyte depletion in COVID-19 patients, the authors performed gross anatomical and in situ immune-histochemical analyses of spleens and lymph nodes (hilar and subscapular) obtained from post-mortem autopsies of 6 patients with confirmed positive viremia and 3 healthy controls (deceased due to vehicle accidents).

Primary gross observations noted significant splenic and LN atrophy, hemorrhaging, and necrosis with congestion of interstitial blood vessels and large accumulation of mononuclear cells and massive lymphocyte death. They found that CD68+ CD169+ cells in the spleens, hilar and subscapular LN, and capillaries of these secondary lymphoid organs expressed the ACE2 receptor and stain positive for the SARS-CoV-2 nucleoprotein (NP) antigen, while CD3+ T cells and B220+ B cells lacked both the ACE2 receptor and SARS-CoV-2 NP antigen. ACE2+ NP+ CD169+ macrophages were positioned in the splenic marginal zone (MZ) and in the marginal sinuses of LN, which suggests that these macrophages were positioned to encounter invading pathogens first and may contribute to virus dissemination.

Since SARS-CoV-2 does not directly infect lymphocytes, the authors hypothesized that the NP+ CD169+ macrophages are responsible for persistent activation of lymphocytes via Fas::FasL interactions that would mediate activation-induced cell death (AICD). Indeed, the expression of Fas was significantly higher in virus-infected tissue than that of healthy controls, and TUNEL staining showed significant lymphocytic apoptosis. Since pro-inflammatory cytokines like IL-6 and TNF-? can also engage cellular apoptosis and necrosis, the authors interrogated the cytokine expression of the secondary lymphoid organs from COVID-19 patients; IL-6, not TNF-?, was elevated in virus-infected splenic and lymph node tissues, compared to those of healthy controls, and immunofluorescent staining showed that IL-6 is primarily produced by the infected macrophages. In vitro infection of THP1 cells with SARS-CoV-2 spike protein resulted in selectively increased Il6 expression, as opposed to Il1b and Tnfa transcription. Collectively, the authors concluded that a combination of Fas up-regulation and IL-6 production by NP+ CD169+ macrophages induce AICD in lymphocytes in secondary lymphoid organs, resulting in lymphocytopenia.

In summary, this study reports that CD169+ macrophages in the splenic MZ, subscapular LN, and the lining capillaries of the secondary lymphoid tissues express ACE2 and are susceptible to SARS-CoV-2 infection. The findings point to the potential role of these macrophages in viral dissemination, immunopathology of these secondary lymphoid organs, hyperinflammation and lymphopenia.

Limitations<br /> Technical<br /> A notable technical limitation is the small number of samples (n=6); moreover, the analysis of these samples using multiplexed immunohistochemistry and immunofluorescence do not necessarily provide the depth of unbiased interrogation needed to better identify the cell types involved.

Biological<br /> The available literature and ongoing unpublished studies, including single-cell experiments of spleen and LN from organ donors, do not indicate that ACE2 is expressed by macrophages; however, it remains possible that ACE2 expression may be triggered by type I IFN in COVID-19 patients. Importantly, the SARS-CoV-2 NP staining of the macrophages does not necessarily reflect direct infection of these macrophages; instead, positive staining only indicates that these macrophages carry SARS-CoV-2 NP as antigen cargo, which may have been phagocytosed. Direct viral culture of macrophages isolated from the secondary lymphoid organs with SARS-CoV-2 is required to confirm the potential for direct infection of macrophages by SARS-CoV-2. Additionally, it is important to note that the low to negligible viremia reported in COVID-19 patients to-date does not favor a dissemination route via the blood, as suggested by this study, which would be necessary to explain the presence of virally infected cells in the spleen.

Relevance<br /> Excess inflammation in response to SARS-CoV-2 infection is characterized by cytokine storm in many COVID-19 patients. The contribution of this pathology to the overall fatality rate due to COVID-19, not even necessarily directly due to SARS-CoV-2 infection, is significant. A better understanding of the full effect and source of some of these major cytokines, like IL-6, as well as the deficient immune responses, like lymphocytopenia, is urgently needed. In this study, the authors report severe tissue damage in spleens and lymph nodes of COVID-19 patients and identify the role that CD169+ macrophages may play in the hyperinflammation and lymphocytopenia that are both characteristic of the disease. It may, therefore, be important to note the effects that IL-6 inhibitors like Tocilizumab and Sarilumab may specifically have on splenic and LN function. It is important to note that similar observations of severe splenic and LN necrosis and inflammation in patients infected with SARS-CoV-1 further support the potential importance and relevance of this study.

On 2020-09-21 08:43:03, user ?????? ??????????? wrote:

The simplicity of the model, together with its generalization, are the<br /> advantages over complex models. Do you know that W. O. Kermack and A. G. McKendrick model can be reduced to the Verhulst equation?

On 2020-10-28 16:35:38, user Edsard wrote:

I think we have a chicken and egg issue here. Your pollen theory is pretty good but also the reason why scientist always say: Correlation is not causation. Your pollen is the result of the weather (temperature and humidity, which has explained seasonality of the flu for 10 years already). Here is our paper. https://www.medrxiv.org/con...

On 2020-04-28 16:33:17, user Katri Jalava wrote:

Interesting paper, and fascinating model. I was a bit curious of your contact percentages. How do you come up with the numbers? E.g. for CS adult-adult would be reduced only by 20 % by closing the public events. I could argue that it is at least 60 %, especially if you have a look on SF1 in 10.1371/journal.pcbi.1005697. Also, if you have both CS and HO in place, you get 80 % + 20 % =100 % reduction for child-child contact(?).

Getting any data on impact of the closure measures from publications is hard. I think they have tried this in the UK from the case load data. Do you think you could do a telephone survey among Germans? Or if an app company would make a data collection tool where everyone could register their daily contacts during the outbreak, that would be cool. Good luck and thank you.

On 2021-03-29 19:49:55, user killshot wrote:

This paper needs major review. Statins do not "improve endothelial function". If anything they are anti-inflammatory. Also there is very little discussion of randomization. If the group is not randomized minimally with vitamin D levels, the whole study is meaningless.

On 2020-04-22 21:11:52, user Amy Weicker wrote:

Was zinc administered along with the CQ in this study? Not seeing it mentioned.

On 2021-02-14 21:31:21, user Dr. Stefan Pilz wrote:

This manuscript has just been published by the European Journal of Clinical Investigation:<br /> https://onlinelibrary.wiley...<br /> Many thanks for the interest in this publication!<br /> Best wishes,<br /> Stefan

On 2020-07-15 06:52:23, user Philipp Berens wrote:

The paper makes strong claims about the decline of antibody levels and neutralizing antibody titer. While no specific recurrence is made to the trendlines shown in Fig. 1 and Fig. 2A, these seem to underline the message promoted in the media, that antibody levels/titers go down over time and therefore there may not be immunity for a prolonged period of time.

For a paper making such far reaching statements, the statistical part is extremely thin. The trendlines are loess fits obtained with R using a span parameter of 1.5. This produces a fit which is quite obviously off. I extracted the data from the figure and recreated the plot using other parameter settings (see here for twitter post). For span parameters <1, the fit looks much more reasonable and I am sure also formal model comparison would confirm that. In particular, these fits do not predict declining antibody/titer levels after a certain period, albeit with high uncertainty.

On 2023-10-25 00:51:38, user Samina Sultana wrote:

Wonderful research structure! It's very admirable how much detail was provided to illustrate the process by which the literature to support the study purpose was selected. Guiding the readers through step by step process in which eligibility of each paper was determined through meticulous and fully blinded process not only instills trust from the audience but it also validates the credential of the information that has been analyzed and dissected to be included in this paper. I understand that the actual transition practices were vaguely described, but were there any information provided that would help synthesize the outcome of these 10 selected transition strategies already in practice? It would be a useful piece of information to support the purpose of this paper, which is to establish what already exists as a basis for future work on relative effectiveness. Examining the efficacy of these initial topology to highlight the importance of work done in this paper.

On 2021-06-01 00:21:43, user Pat Frank wrote:

"We show here that the mRNA from anti-COVID BNT162b2 (Pfizer) and <br /> mRNA-1273 (Moderna) vaccines is not detected in human breast milk <br /> samples collected 4-48 hours post-vaccine"

Nice, but irrelevant. The worry has been that the mRNA encoded spike protein gets into breast milk (and other tissues).

There does not appear to be any concern that the mRNA itself appears in breast milk. So the paper of Golan, et al., is irrelevant to the concern.

On 2021-07-24 06:42:21, user itellu3times wrote:

Need to compare with background - what is the vaccination rate for Houston, during the period of the study? This may completely dominate the purported findings.

On 2021-09-25 19:07:25, user Peter Dimitrov wrote:

Time frame : 60 days following vaccination; Location: who presented at a single academic institution/Ottawa Heart Centre. Patients were identified by admission and discharge records of the Ottawa Heart Centre. Sample size: 32 patients, 29 of which were male! Median time between vaccination dose and pain symptoms was 1.5 days. Startling findings by themselves, ought to raise curious questions not outright dismissal/withdrawal.

However, obviously the incidence rate based on total MRna vaccines in Ottawa area is waay off. Am curious to know disaggregated sex/age etc data of Myo/peri cases recorded in the exact same time period in all the other hospital treatment centres in Ottawa area?

On 2021-09-22 21:03:39, user Ovi Wan-Kenobi wrote:

where did you get the 1/1000 rate? I can't see it anywhere in the study mentioned.

On 2020-11-28 13:16:44, user Angie wrote:

The description of the amount of vitamin D used doesn't account for the mistake made in calculating vitamin D needs, nor is that mistake discussed in the article. In addition, making active forms of VitD from what is ingested is not an instant magic process. A body under attack may lack the energy to carry it out. Maybe it's just giving something by a pill is ineffective right now. What if you did transdermal? That would avoid the stomach/gut which is a place we know the virus attacks. Also vitamin D doesn't act alone. A person in ICU may not get a lot of vitK and may even be on anti-K blood thinners if they are a stroke risk. How many patients were on Lovenox vs something that thins blood via the vitamin K route? A daily exposure to a UV lamp may be more efficient for providing Vitamin D.

Anyway, the point is, I am not convinced that this test was properly done with reference to vitamin D. It takes weeks to normalize vitamin D in tissues where it is needed. Just testing the blood level after you gave a bolus pill is lying to yourself. It's like adding dye to water and saying, look, the sand at the bottom of the river turned all blue, we can assume it goes deep. What's the vitamin D status of hepatocytes after the one pill you gave? How much enzyme activity was there in the kidney to activate the D you gave?

Giving someone a vitamin is not like giving them a drug. The vitamin has to go to the tissues and do its work. You're thinking far too simplistically. VitaD affects thousands of reactions in the body and is not actively excreted as if it were an invader. That's nothing like a drug. Vitamins aren't drugs, that goes double for the fat soluble ones.

On 2020-12-15 10:58:41, user NK wrote:

Re: article pre-published at https://www.medrxiv.org/con...

There are several methodological problems in this study.

1. Findings that suggest increased ORs among primary school teachers, child care workers and secondary education teachers are not properly presented and discussed

The summary states: "Teachers had no or only moderately increased odds of COVID-19". This finding is mentioned several places in the text of the article. Teachers are repeatedly referred to as having a low risk, even when the results for teachers show a significant increase in admissions and borderline significant increase in infection rates. Quotes: «First, our findings give no reason to believe that teachers are at higher risk of infection», and in the conclusion: “Teachers had no increased risk to only a moderate increased risk of COVID-19”. We wonder why the authors find it important to repeatedly mention this<br /> result for teachers when the result for the last period does not exclude a substantial increased risk for teachers, whereas occupational groups with lower risk than teachers are not mentioned in the summary.

The part of “Supplementary table 1” does not provide a basis for such a conclusion that teachers are a low risk group.

The OR (95% CI) for 1) primary school teachers 2), child care workers and 3) secondary education teachers were 1.142 (0.99-1.32), 1.145 (1.02-1.29) and 1.095 (0.82-1.47) respectively. The upper confidence limits does not exclude 29 % to 47 % increased ORs, which represent substantial increases.

Concerning the results on the risk of admission, it is stated: «None of the included occupations had any particularly increased risk of severe COVID-19, indicated by hospitalization, when compared with all infected in their working age (Figure 3, S-table 2), apart from dentists, who had 7 ( 2-18) times increased odds ratio, and pre-school teachers, child care workers and taxi, bus and tram drivers who had 1-2 times increased odds ratio”.

This finding is not discussed or mentioned in the summary, even if the findings were statistically significant for pre-school teachers as well as for child care workers.

1. The study periods include periods when the schools were closed and include no period with high infection rate among children and youths.

It is not to be expected that teachers have higher infection rates than the average working population in periods when school are closed and when the infection rates are low in the age groups 0 - 9 and 10 -19 years. This problem is not discussed in the paper. Schools were closed from 12 March to 27 April. For a majority of the schools, holiday started from Friday 19 June.

The first study period lasted from February 27 to July 17. Thus, schools were closed for over 70 days of the first study period of 139 days. The infection rates in children at school age in the first study period were rather low (3.6 per 100 000 children per week between in the age group 10 -19 in week 19, 1.1 per 100 0000 children per week in week 25). In the last study period, the infection rates varied between 7 to 17 per 100 000 per week in the age group 10 - 19. Even if these rates are much lower than later weeks that were no studied (after week 42), the results from this second part of the study suggest an increased risk for teachers.

Thus, the infection rates among children started to increase from week 43, after the end of the study period. By not including this period, the study design excludes the possibility to detect if these high rates among pupils could be related to increase infection rates among teachers.

It is a problem that the results from this pre-published study has been quoted in the media and referred to as if teachers have no excess risk, or even possibly a reduced risk at the time that several municipalities were to decide what type of restrictions at schools should be introduced to reduce the risk of transmission among school children, see https://www.barnehage.no/korona/ny-forskning-nei-barnehagelaerere-har-ikke-okt-risiko-for -smitte/211143

On 2020-05-03 07:57:39, user Soumi Ray wrote:

http://www.businessworld.in...

On 2020-09-05 15:23:44, user Kwon Seokjoon wrote:

Then, why not even FDA UEA trial for the Saliva test until now (9/04/20020) ???

On 2021-07-10 22:08:06, user Mazzs wrote:

Australia is currently showing good data on precise outcomes for delta variant cases.

On 2021-11-06 02:10:25, user David wrote:

I disagree with your pessimistic analysis. There is evidence that Vaccination after Infection produces Hybrid immunity, along lasting immunity that protects against variants. That will help Iran, now 42% fully vaccinated and rapidly increasing. Additionally, there is positive new from Pfizer, they announced the result of Phase 2/3 trials of Paxlovid, it is 89% effective at preventing hospitalisation in SARS-CoV-2 positive vulnerable cases. There will likely be other antivirals soon. We are close to the end of this pandemic.

Callaway, E. 2021. COVID super-immunity: one of the pandemic’s great puzzles. Nature, 598, 393–394, https://doi.org/10.1038/d41...

On 2022-01-11 17:30:23, user Franciska Ruessink wrote:

Read the study in the link, and on current regulations :https://en.coronasmitte.dk/....<br /> A large part of the unvaxxed oppose to tests as well.

On 2020-08-04 19:47:00, user Kamran Kadkhoda wrote:

The specificity of 85% doesn't support your conclusion...

On 2020-10-16 12:42:55, user Christopher Leffler wrote:

This paper demonstrating the effectiveness of masks was just accepted to the first and only peer-reviewed journal we sent it to--it's pubmed-indexed, both print and online.

On 2020-07-18 17:50:20, user Matthew Almario wrote:

Who is the manufacturer of the UV light?

On 2020-04-28 09:20:22, user Carlos Gaspar Reis wrote:

What hydrogen peroxide concentration was used, 3%?

On 2022-01-28 20:28:06, user Charlie Jones wrote:

Does the background of the person affect their suicidal ideation (ie socioeconomic status, family situation).