On 2020-07-28 14:21:33, user Liam Brierley wrote:

Please note a brief lay summary of this work is available as part of the RCVS Knowledge series.

On 2021-09-25 16:30:29, user TeeJay2000 wrote:

I left a comment reflecting on the reputation hit that the Ottawa Heart Institute will take on this, but my comment was removed. Thank you medRxiv for encouraging discussion. I have now written to the Ottawa Heart Foundation, to indicate my withdrawal of support, until the Institute makes a formal statement how this paper made it even to a 'preprint', given the colossal size of the error.

On 2021-09-22 03:14:20, user Norsksoul wrote:

It is a preprint article but they basically identified all vaccine recipients in Ottawa during the June 1 through July 31 study period. <br /> This was the denominator of the study group. <br /> Anyone from this study group that was admitted with Acute Myocarditis or Pericarditis within 1 month of a Moderna or Pfizer vaccine became the numerator. <br /> So 32 cases occurred in 32,379 vaccine recipients which comes out to a 1/1000 incidence. This study should be done in the 12-18 year old age range and the incidence would likely be even worse.<br /> But wait,....it gets even worse. <br /> That 1/1000 incidence is in a group of 32,000 men AND women. <br /> But out of 32 cases of myocarditis, 29 occurred in men. <br /> That’s 90%! <br /> They unfortunately don’t give the data on male/ female percentages in the study group denominator but if we assume a 50/50 split, then the male incidence is actually 29/16,189 or 1 in 558 males vaccinated. <br /> 1/558<br /> 1/558<br /> 1/558<br /> Let that sink in for a minute. <br /> This is reckless medical malpractice at its worst.

On 2020-06-09 20:00:05, user Anne Smith wrote:

I'm blood group O. That should require two of the same allele. The article reports an association between ARDS and a specific SNP, but all of the discussion in the paper is about the relationship between major blood group and chance of ARDS. At 23andme, for rs657152, on a ABO gene, my genotype is A/C. My first question is how is that even possible, since if this SNP determines blood group and I'm type O I should have either two A's or two C's. Second, I and many others would really think you to present the odds of specific variants on rs657142 and ARDS! For two A's, two C's, and one of each.

On 2020-06-10 11:57:58, user Arturo Tozzi cns wrote:

Note that a gene tentatively proposed for COVID-19 severity is the gene SUSD1, which is located very close to the ABO zone on the chromosome 9 (precisely: 9q31.3-q33.1)<br /> see: https://vixra.org/abs/2004....

On 2022-01-09 17:21:03, user Kevin McKernan wrote:

You should test if the primers light up on SARs-CoV-2.<br /> With the high break through rate, People want to differentiate vax from C19. Would also be interested to know if SARs-CoV-2 also ends up in the lipid fraction. I suspect not. The heavy methylation of these RNAs changes their lipophilicity.

Longer testing window would help answer some questions.

Thank you for putting the primers public.

On 2020-04-21 22:42:10, user Dan Johnson wrote:

Great work, quite an undertaking. This is a very minor question: why "Pair-wise p-values were calculated between isolates using the t-test" rather than use Tukeys or one of the many post-hoc tests designed to get around the Type I error problems of multiple t-tests. Is that what you mean by "adjusted p"?

On 2020-04-07 06:12:40, user Marm Kilpatrick wrote:

This paper has been published: https://www.eurosurveillanc...<br /> I'm not sure why this preprint doesn't direct readers to the published paper.

On 2020-05-03 09:08:28, user Daniel Corcos wrote:

These calculations rely on wrong estimates.<br /> 1) The delay between infection (does it include incubation time?) and death is based on a preprint from data on the Diamond Princess epidemic. There were 7 deaths at that time but the current number is 14 (1). The case fatality rate in South Korea was 1.6%, but now it is 2.32% (2). Delayed deaths should be taken into account.<br /> 2) A zero generation time is unrealistic, as the virus must multiply before spreading, and estimates of the generation time have been calculated to be between 4 and 5 days (3,4) .<br /> Changing these parameters should alter the conclusions.

1) https://en.wikipedia.org/wi...<br /> 2) https://en.wikipedia.org/wi...<br /> 3) https://www.sciencedirect.c...<br /> 4) https://www.medrxiv.org/con...

On 2021-08-29 17:23:02, user Paula wrote:

I have question about the injection experiment for SARS-CoV-2 that is ongoing. What if the experiment includes a placebo group in addition to those who have received an actual shot? Wouldn't that understate the the adverse incidence rate too? The last EUA by the CDC for the Pfizer shots made an oblique reference to a placebo group and may indicate that there actually is a double blind study going on right now and it has been ongoing since the inception of the mass experiment.

On 2021-08-31 14:37:06, user Christopher G DeMaria wrote:

It would seem to me, that more people would be instructed to get a pulse oximeter. If their O2 stats remain below 92%, it might be time to get some additional medical help. Unfortunately, it is very plausible to have Covid pneumonia, be starved of oxygen, and not even be aware of it.

On 2021-08-28 18:17:03, user Squid Pro Crow wrote:

Despite the fact that I have no formal medical training, I think that I now have the real life experience to knowledgeably comment on this. My wife and I both had our second doses of the Phizer just under 5 months ago. Also my daughter and son-in-law had the Pfizer shots about 3-1/2 or 4 months ago. At the end of a 3 day stay of 2 grandkids i began to get a cough and slight fever, and lost my sense of smell and taste. So I got tested and it was positive, My wife has a cough and body aches and will be tested today. My daughter and son-in-law (in their low 40's) are also experiencing mild symptoms and will be tested today. The kids, of course had very minor symptoms for about a day, and are completely fine. So, assuming that the adults test positive, it seems evident that the delta strain does indeed spread rapidly and easily, and the vaccine(s) may not be as effective against it. HOWEVER, I feel that at my age, with asthma and possibly COPD history, I would be much worse off had I decided against the vaccine, as my symptoms are very mild now, except for the chest congestion that I have (which is already better) that I also get from just about every cold.

My main concern is that there is not enough focus on theraputics, and major health providers like Kaiser just expect even their at-risk patients like me to just sit at home and wait to see if their lips turn blue and they can't breathe, and make it to an E.R. for a company that is usually proactive about health care, this is just stupid. An apparently, this is the norm. There are some treatments that are effective if taken early, but our government and the health system that follows their dictates are afraid to prescribe safe drugs off-label that are semi-proven to be very helpful, like ivermectin, which I managed to get from a nearby Dr. It seems to be helping clear it up even faster--my sense of smell is even starting to come back.

On 2021-08-29 13:49:31, user Faithkills wrote:

There is no conclusion that either is "beneficial". The conclusion is that infection acquired immunity is much stronger than vaccine induced which is entirely warranted. It would be shocking were it otherwise.

They aren't saying the vaccine nor the disease are "beneficial". Clearly neither are.

However were one not at risk, it is logical to conclude that for personal and collective interest its preferable to have virus aquired immunity. If young healthy people took it upon them to expose themselves, their much greater resistane would protect the old and at risk far better than getting vaccinated. Real herd immunity could then occur more rapidly and reliably.

If you are at risk & still think you are safer around a vaccinated person than a recovered person, you are liable to win a darwin award.

On 2021-09-10 11:41:51, user maury779 wrote:

It is very difficult to tell what variant infected those who were sick prior to February 2021. We know that the Pfizer vaccine was made early in 2020, not using the delta variant. There is now questioning that the efficacy of the Johnson & Johnson vaccine may have been lower because the delta variant was already present during the trials. Furthermore a Kentucky recent study still shows that natural immunity did not protect as well as the previously sick who were then vaccinated. We need to sort out all those studies.

On 2021-08-27 14:34:28, user Jonathan Bennett wrote:

Does this mean I should be allowed to travel anywhere, given I have prior infection, and people who are merely vaccinated should be subject to tight restrictions?

On 2021-08-30 22:11:02, user Emmanouil Magiorkinis wrote:

This study is a retrospective observational study trying to answer whether natural COVID-19 infection provides better immunity than vaccination. The problem with such retrospective studies in infectious diseases is that they cannot eliminate the differences between the social networks among the two arms of the study. Social networks are important in the course of spread of infectious diseases. People infected by COVID-19 may have contracted from their social surrounding vice-versa, and in that case those people have an extra immunity firewall which could explain the results. Moreover, natural immunity in this viral disease may be connected with long-term effects such as long COVID, which by default does not leave as an option to let those people contract the virus, because natural immunity may be better.

On 2021-09-14 18:50:40, user STI Team wrote:

Why, in THIS case, SHOULD natural immunity post infection, and it's various attendant antibodies attenuated by the components of the virus, be inferior to the narrow targeting of spike proteins in the vaccines? Add to that, some folks who had SARS 1 in 2003, who are still showing robust immunity.

It's an argument that could be made... but proving a sudden complete cessation of the tacit principles of virology, in the case of SARS CoV2, in an atmosphere of honest scholarship, would be a very very high hill to climb.

Is the Israel study really the first time it has occurred to anyone, to consider the importance of the natural immunity in those who have recovered? Of course not... but you would not know that based on the discussion from medicine/media regarding this topic. WHY IS THAT?

WHY, INDEED! “Something is rotten in the state of Denmark” ("Hamlet." Act I, Scene 4)

On 2021-08-27 19:58:34, user Kryptos wrote:

Good research study. So is it necessary to risk vaccinating a billion children who have no underlying conditions, considering the risks of blood clots, vascular damage, etc.? Wouldn't it be better to let them acquire natural immunity?

On 2021-01-27 10:12:04, user Elias Hasle wrote:

some countries being hardly hit while others to date are almost unaffected

Readers will tend to read this as "barely hit", the opposite of the intended meaning. "hit hardly" would be less ambiguous.

On 2020-04-19 06:51:19, user DFreddy wrote:

reference 2 -> link not correct

Report 12 - The global impact of COVID-19 and strategies for mitigation and suppression [Internet]. Imperial College London. [accessed 2020 Apr 7];Available from: http://www.imperial.ac.uk/m... epidemiology/mrc-global-infectious-disease-analysis/covid-19/report-12-global-impact-covid-19/

On 2020-04-21 13:43:22, user Hell of a Fella wrote:

German study also reported a 50fold increase in prevalence over official numbers. <br /> https://www.nature.com/arti...

On 2020-04-02 18:30:40, user Kannapiran P wrote:

Sure, not all studies are comprehensive or definitive as a single cause or effect to be responsible for a disease resistance or susceptibility.

Also, i do not see (testing feasibility) to be different or infavour of the economic status of <br /> country. All countries have kept out the option of checking everyone, <br /> symptomatic patients are tested or interactions with confirmed patients <br /> are tested - all these have been cited to non-availability of voluminous<br /> testing and treatment facility, be it third world nation or developing <br /> or developed nation.<br /> i do not see the correlation between economic or infrastructure facility only (as we call in genetics to be environmental factors) but the genetic moderation a population shows <br /> towards a disease spread. This is truly spelled by targets vulnerable in various steps in pathogenesis. I see this paper to have effected the impact of any changes owing to generated resistance in such populations(BCG vaccinated). I think if the use of retroviral drug cocktails is working the impact of resistance developed cited in this paper is also <br /> feasible.

On 2020-07-23 10:39:18, user Jeff Morris wrote:

I would love for these “dark matter” arguments to be true. I understand the mathematics, but is there any direct evidence that significant proportions of the population have immunity from exposure to previous coronaviruses or is it just hopeful speculation? Our antibody studies at Penn suggest our patient population has coronavirus antibody prevalence of about 1% — it is not even clear if these IgG titers indicate immunity — but even if they did the 1% would be negligible and big enough to produce this proposed “dark matter” effect.

On 2020-12-29 00:33:03, user Olga Matveeva wrote:

Several recent preprints support some of this manuscript findings.<br /> 1. Authors from Sweden and China in a study entitled “Pulmonary stromal expansion and intra-alveolar coagulation are primary causes of Covid-19 death” demonstrated that “The virus was replicating in the pneumocytes and macrophages but not in bronchial epithelium, endothelial, pericytes or stromal cells. doi: https://doi.org/10.1101/202...<br /> 2. Researchers in China concluded that “Collectively, these results demonstrate that SARS-CoV-2 directly neutralizes human spleens and LNs through infecting tissue-resident CD169+ macrophages.” They published a preprint entitled “The Novel Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Directly Decimates Human Spleens and Lymph Node” doi: https://doi.org/10.1101/202...<br /> 3. Researchers in France demonstrated “that SARS-CoV-2 efficiently infects monocytes and macrophages without any cytopathic effect.” Their findings are reported in the preprint entitled “Monocytes and macrophages, targets of SARS-CoV-2: the clue for Covid-19 immunoparalysis” doi: https://doi.org/10.1101/202...<br /> 4. Researchers in Brasil investigated SARS-CoV-2 infection of PBMCs and found that in vitro infection of whole PBMCs from healthy donors was productive of virus progeny. They also found that “SARS-CoV-2 was frequently detected in monocytes and B lymphocytes from COVID-19 patients, and less frequently in CD4+T lymphocytes” The preprint is entitled “Infection of human lymphomononuclear cells by SARS-CoV-2”. <br /> doi: https://doi.org/10.1101/202...

On 2020-05-12 11:12:30, user Guest wrote:

I’m an advocate for ignoring cases & case fatality rates at this stage. Why? Because of variance in testing & “at risk” populations.

I only watch deaths & “excess deaths.” I don’t see a benefit from arguing CFR at this point if excess deaths are much higher in most countries than would be expected from any other cause. The severity of disease can be seen on the multiple excess death numbers by various countries (See Ecuador’s excess deaths.)

From memory: U.S. Flu season tests ~10,000,000, uses ~1,000,000 for statistical analysis, Flu season starts at the 10% positive rate & only ~6,000 confirmed seasonal deaths. There are ~8750 daily deaths from all causes & as the rate goes higher during December, January & February they try to calculate how many could be due to influenza. Ie. From all these #s they produce statistical modeling that shows ~35,000,000 infected & ~36,000 deaths.

ADDENDUM<br /> CFR numerator & denominator. <br /> To be clear, CFR should be based on all deaths out of all cases from a disease. The Flu example above shows it is not 6,000 / 1,000,000 but 36,000 / 35,000,000. The former is CFR of 0.6%, the latter near 0.1%. The numerator & denominator on the former are low based on lack of testing (especially both asymptomatic & deaths at home, nursing homes, hospice, etc.) The latter deaths are calculated out of analysis of all excess deaths. If we use the former numerator, with the latter denominator it greatly lowers the CFR. CFR of Flu is not 6,000 / 35,000,000.

Law of large numbers. <br /> Taking one example of infection rates doesn’t show the variance in the country nor the globe. Most believe both the case counts & deaths are a multiple higher than are posted.

Current estimates are CFR ~0.5-0.9 which is 4-9X more deadly than the Flu. This really doesn’t need to be true to assess the dangers of an infection; CFR is but one variable. If, CFR is 0.1 but reproduction numbers are large & the # of cases & deaths are drastically larger than Flu, it would still support “social distancing” measures.

On 2020-03-22 16:23:48, user Sinai Immunol Review Project wrote:

Main findings: Colonic enterocytes primarily express ACE2. Cellular pathways associated with ACE2 expression include innate immune signaling, HLA up regulation, energy metabolism and apoptotic signaling.

Analysis: This is a study of colonic biopsies taken from 17 children with and without IBD and analyzed using scRNAseq to look at ACE2 expression and identify gene families correlated with ACE2 expression. The authors find ACE2 expression to be primarily in colonocytes. It is not clear why both healthy and IBD patients were combined for the analysis. Biopsies were all of children so extrapolation to adults is limited. The majority of genes found to be negatively correlated with ACE2 expression include immunoglobulin genes (IGs). IG expression will almost certainly be low in colonocytes irrespective of ACE2 expression.

Importance: This study performs a retrospective analysis of ACE2 expression using an RNAseq dataset from intestinal biopsies of children with and without IBD. The implications for the CoV-19 epidemic are modest, but do provide support that ACE2 expression is specific to colonocytes in the intestines. The ontological pathway analysis provides some limited insights into gene expression associated with ACE2.

On 2020-06-05 10:23:43, user Alberto M. Borobia wrote:

This manuscript has been published in "Journal of Clinical Medicine" https://www.mdpi.com/2077-0...

Borobia, A.M.; Carcas, A.J.; Arnalich, F.; Álvarez-Sala, R.; Monserrat-Villatoro, J.; Quintana, M.; Figueira, J.C.; Torres Santos-Olmo, R.M.; García-Rodríguez, J.; Martín-Vega, A.; Buño, A.; Ramírez, E.; Martínez-Alés, G.; García-Arenzana, N.; Núñez, M.C.; Martí-de-Gracia, M.; Moreno Ramos, F.; Reinoso-Barbero, F.; Martin-Quiros, A.; Rivera Núñez, A.; Mingorance, J.; Carpio Segura, C.J.; Prieto Arribas, D.; Rey Cuevas, E.; Prados Sánchez, C.; Rios, J.J.; Hernán, M.A.; Frías, J.; Arribas, J.R.; on behalf of the COVID@HULP Working Group. A Cohort of Patients with COVID-19 in a Major Teaching Hospital in Europe. J. Clin. Med. 2020, 9, 1733.

On 2020-08-24 19:16:22, user joaopicoito wrote:

Now published in Social Psychiatry and Psychiatric Epidemiology doi: 10.1007/s00127-020-01903-y

On 2021-10-28 16:04:42, user Erin wrote:

My main concern is there were only 5 subjects. I LOVE new methods of treatment, especially when it isn't a drug, and this looks to be really promising. However, when you look at the limited number of subjects to the possible treatment group it is a bit of a let-down. I hope they are able to perform this treatment on more people; men, women, and different backgrounds!

On 2020-05-13 03:11:18, user deirdre alexandra platt wrote:

Useful information thankyou. A query: on average, how long is the "longterm" exposure to airpollution? Are you looking at the previous 2 years, or 5 years, or more? How long can we endure breathing polluted air before we become vulnerable to a virus like COVID-19?

On 2023-08-14 20:23:31, user Peter Lange wrote:

This paper reports the prevalence of common symptoms in the population after covid-19. There is no control group. The paper is therefore of no utility and no conclusions can be drawn. The authors should make some effort to derive useful comparisons before attempting to publish.

On 2020-05-07 21:23:21, user Dan T.A. Eisenberg wrote:

How much spit did you collect and how much of it + the PBS was needed for sufficient sample for extraction. Thanks!

On 2019-10-10 12:11:25, user GuyguyKabundi Tshima wrote:

EPIDEMIOLOGICAL SITUATION

EVOLUTION OF THE EPIDEMIC IN THE PROVINCES OF NORTH KIVU AND ITURI AT OCTOBER 06, 2019<br /> Monday, October 07, 2019<br /> Since the beginning of the epidemic, the cumulative number of cases is 3,205, of which 3,091 are confirmed and 114 are probable. In total, there were 2,142 deaths (2028 confirmed and 114 probable) and 1006 people healed.<br /> 363 suspected cases under investigation;<br /> 1 new confirmed case at CTE in North Kivu at Oicha;<br /> No new confirmed deaths<br /> 2 people healed from Butembo CTE;<br /> No health workers are among the newly confirmed cases. The cumulative number of confirmed / probable cases among health workers is 161 (5% of all confirmed / probable cases), including 41 deaths.

NEWS

7 people healed from Ebola Virus Disease released Monday at Komanda CTE<br /> - A total of 7 people cured of Ebola Virus Disease were released on Monday October 7th at the Ebola Treatment Center (ETC) in Komanda. ;<br /> - This is 4 people from Mambasa and 3 cases from Komanda Health Zone to whom discharge certificates were given by the director of this Ebola Treatment Center<br /> - This certificate of discharge bears as inscription: "On the date of issue of this document the bearer of this certificate does not present any risk of contaminating other people, because his test was negative for the Ebola virus disease. He / she is thus DECLARE GUERI (E) . His current state of health is not a danger to the community. That is why he / she can return to his household and his professional environment to continue the daily activities. The family, the community and the authorities are asked to welcome him to promote his social integration ".

VACCINATION

• Continuation of vaccination around the confirmed case of 04 October 2019 in the Tenambo Health Area in Oicha, North Kivu;
• Continuation of the vaccination of newly recruited front-line staff at the General Reference Hospitals of Katwa and Kyondo in North Kivu;
• Since vaccination began on 8 August 2018, 234,693 people have been vaccinated;
• The only vaccine to be used in this outbreak is the rVSV-ZEBOV vaccine, manufactured by the pharmaceutical group Merck, following approval by the Ethics Committee in its decision of 20 May 2018.

MONITORING AT ENTRY POINTS

• Since the beginning of the epidemic, the total number of travelers checked (temperature increase) at the sanitary control points is 103,167,809 ;
• To date, a total of 111 entry points (PoE) and sanitary control points (PoCs) have been set up in the provinces of North Kivu and Ituri to protect the country's major cities and prevent the spread of the epidemic in neighboring countries.

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

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

On 2025-02-12 19:59:20, user Aron Troen wrote:

Review Part II

Methodological shortcomings<br /> Study population and period: The population demographics used as the denominator of per capita caloric requirement rely on census data from 2017 and UN OCHA reports on movement and displacement of the population between Gaza governates during the war. The study states that no adjustments were made for out-migration or excess deaths. However, approximately 150,000 people left the Gaza strip from the beginning of the war until the Rafah crossing was closed in May. When added to casualties and a natural death rate of ~5500 people per year, this means that the population denominator used to calculate the food supply in Kcal per person-day (Figure 4) was overestimated by ~ 200,000 people, which would result in the underestimation of the food supply by approximately 10%.

The authors acknowledge the limitation that “There remains considerable uncertainty about our population denominators in the north, and even moderate error in these would have affected our Kcal per capita estimates. Gaza’s population has probably decreased due to high mortality and out-migration…”. Nevertheless, they shrug off this limitation by asserting that “…we expect this to have only marginally affected our estimates.” without explaining why.

Data on truck deliveries

The comparison between UN and Israeli shipping data is superficial and inadequate for supporting the decision to dismiss and exclude the data from the analysis. The authors fail to discuss the literature, of which they surely must be aware, which addresses the high-profile controversy over the number of trucks supplying aid to Gaza and the discrepancies between the UN and COGAT data, and which notes the under-reporting of private sector food shipments by the UN (see for example, Rosen, Bruce and Nitzan, Dorit, Humanitarian Food Aid for Gaza: Making Sense of Recent Data (June 02, 2024). Available at http://dx.doi.org/10.2139/ssrn.4851635) "http://dx.doi.org/10.2139/ssrn.4851635)") .

Although the authors note the "large discrepancy between UN and Israeli government data" on the entrance of goods into Gaza, they erroneously assert that UNRWA monitored the composition of “ALL trucks” crossing into Gaza, despite the partial coverage of non-UN food consignments, and despite disclaimers published by UNRWA and recorded by the authors, that the data from May-August are incomplete. The authors make little effort to help the reader understand the reason for the discrepancy nor to explain how they reached the conclusion that UNRWA's dataset "appeared highly complete and well-curated, but may be biased by systematic under- or over-reporting unknown to us". Instead of making a serious effort to include COGAT data to improve the accuracy of their simulation, they perform a perfunctory comparison of the UN and COGAT data and justify the summary dismissal of the Israeli registry, using the categorical listing of truck weight registered by COGAT as “evidence of digit heaping or crude approximation”. This is a peculiar choice, given the importance of the COGAT dataset, which is included in the June IPC report and in a working paper that the authors cite that analyzes the caloric content of food supplied to Gaza, including private sector shipments that are missing from the UN data (now published at https://ijhpr.biomedcentral.com/articles/10.1186/s13584-025-00668-6) "https://ijhpr.biomedcentral.com/articles/10.1186/s13584-025-00668-6)") . An alternative choice might have been to simulate the weight and contents of the COGAT data like the authors did for incomplete WFP data, or to perform a sensitivity analysis and compare how caloric supply estimates might differ based on the data and assumptions used.

Instead, the study implies that the discrepancy has to do more with weight of aid reported rather than the number of trucks. However, significant gaps are also evident in the number of trucks reported. For example, in February, UNRWA reported 1,857 trucks carrying food while COGAT's figure is 15% higher (2,117). In January the gap is equally large, with COGAT's number of trucks 13% higher than UNRWA's (3,364 and 2,990 respectively). According to COGAT, between January and May 2024, "as a result of the UN’s partial counting… there are 3,406 trucks missing from their Kerem Shalom data and 2,198 trucks missing from their Nitzana/Rafah data." ( https://govextra.gov.il/media/dtmhzmtn/discrepancies-in-un-aid-to-gaza-data-2.pdf) "https://govextra.gov.il/media/dtmhzmtn/discrepancies-in-un-aid-to-gaza-data-2.pdf)") . Furthermore, the period analyzed covers several unexplained changes in UNRWA's dashboard ( https://honestreporting.com/how-unrwa-covers-up-its-faulty-gaza-food-data/) "https://honestreporting.com/how-unrwa-covers-up-its-faulty-gaza-food-data/)") , apparently following data-driven criticism about its methodology and lack of transparency on social media ( https://x.com/AviBittMD/status/1780052840930578499) "https://x.com/AviBittMD/status/1780052840930578499)") . According to a FEWS NET report, "on September 8… UNRWA’s dashboard was updated with additional supply data for August, as well as for previous months, including commercial truck entries as reported to UNRWA." UNRWA has not disclosed where the new data on commercial trucks came from or how far back the data update had gone.

The subsequent calculation of caloric availability includes a mix of registered and simulated data, in which the simulation parameters extremely underestimate the caloric supply. The model derives the simulated distribution of estimated Kcal per truck as described in the methods and shown in supplementary figure A1: “We reconstructed the number of these trucks over time based on published information and data shared by WFP . As no data on content were available, we simulated their caloric equivalent by repeatedly sampling from the empirical distribution of calories per truck obtained from the UNRWA dataset.“ There are several problems with this approach. First, it is unclear which specific truck data “shared by WFP” were used for this simulation, and whether they are publicly available. This should be clearly indicated in the uploaded github data files. Moreover, the WFP records the contents of their shipments. Why were their contents omitted in this case? Presenting summary tables in the article would help the orient the reader to the source data for the truck counts used, distinguishing between simulated or assumed and actual contents. An implicit assumption underlying the simulation of WFP contents according to estimated distribution of calories by UNRWA trucks, is that the contents of UNRWA and WFP shipments are the same. This needs to be documented or the assumption should be made explicit. Given that the study appears to significantly underestimate the weight of the UNRWA pallets, the procedure used would be expected to propagate biased estimates lower than the actual weights to the WFP data as well.

The most critical problem in the model is with the ASSUMED weights that the authors assign to the consignments. They assume mean pallet weights to be 637.5 kg per pallet, with a minimum to maximum weight of 510-765 kg per pallet (gaza_food_data.xlsx, general tab), based on citations 23, 30 and 31. Citation 23 does not provide supporting data and refers to IPC reports in general. Citations 30 and 31 are standard operating procedures for the Egyptian Red Crescent (ERC) from October and November 2023, which REQUIRE an 18% higher palletization weight of 750 kg. However, even this value is considerably lower than UN aid REQUIREMENTS that specify pallet weights for wheat flour (1125-1200 kg/pallet), sugar (1200 kg/pallet), chickpeas 1200 kg/pallet), red lentils (1200 kg/pallet), rice (1200 kg/pallet), SF oil (910-1213 kg/pallet) or milk (655 kg/pallet) (UNRWA Special Shipping Instructions for Shipments by Sea Air and Land – April 2024 - page 6; https://unrwa.org/sites/default/files/emergency_gaza_2023-_rfq-pskh-42-24-the_provision_of_man_trucks_for_gfo-tender_doc.pdf) "https://unrwa.org/sites/default/files/emergency_gaza_2023-_rfq-pskh-42-24-the_provision_of_man_trucks_for_gfo-tender_doc.pdf)") . Examination of “dataset 20240911_Commodities Received.xlsx” reveals that consignments attributed to ERC alone or with other agencies (including UNRWA) account for only 90,009 of the total of 531,175 food line items (17%) and 8085 of the total of 22,833 mixed line items (35%). Therefore, even if the mean value of 637.5 kg/pallet were correct for the ERC-associated consignments, the weights assigned to the foods supplied are unreasonably low, giving an extreme underestimation of the calories supplied.

This unreasonably low distribution of the estimated Kcal per truck can be seen in the simulated truck weights. The histogram in Appendix figure A1 shows a distribution that is heavily skewed to the left with the vast majority of trucks carrying less than 50 Million Kcal and perhaps a third carrying less than 25 Million Kcal. The simulated lower end of the distribution, which begins with 600 trucks carrying zero Kcal/truck, is highly unlikely to be accurate. Even if one takes the assumed mean weight per truck assigned by the researchers as 14,500 kg, multiplying by the calorie content of wheat flour (3,640 Kcal/kg) would give a mean calorie content per truck of 52.8 M Kcal. Even if a lower calorie food calorie density of circa 3200 Kcal/kg were used, based on visual inspection of Figure 3A (Kcal/kg food consignments between Oct 21 2023 – May 4 2024), the assumed mean caloric content of the food trucks should be 46.4 Million Kcal. These values, are hard to reconcile with the histogram, even if the assumed and simulated truck weights in the model are true. Thus, the validity of the model assumptions and their potential for propagating error and uncertainty in the results should be carefully revisited.

Data on other food sources

Estimates of the available existing food supply before the war combine the household stocks of humanitarian food aid, data provided to the researchers by UNRWA giving the exact stocks in UNRWA warehouses and the range of minimum-maximum capacity of WFP warehouses before the war; estimates of existing private stores, and of agriculture and livestock production, discounted for gradual depletion and destruction during the war’s early months. The model does not account for potential Hamas stockpiles ( https://www.nytimes.com/2023/10/27/world/middleeast/palestine-gazans-hamas-food.html) "https://www.nytimes.com/2023/10/27/world/middleeast/palestine-gazans-hamas-food.html)") .

The spreadsheet “gaza_food_data.xlsx” tab “warehouses” lists total UNRWA and WFP warehouse capacity before the war as a range with a minimum to maximum capacity of 7,900-21,479 MT or 28.7 – 78.1 billion Kcal, whereas presumably, the “exact” contents of the food in UNRWA warehouses are those data listing a total of 38.3 billion Kcal of food in tab “unrwa_stocks”. No further information is provided to ascertain that the data given to the researchers by UNRWA and WFP is complete and accurate.

Existing private stores/Caloric balance and consumption: The text describes the assumptions used in estimating the existing stores and their depletion during the war. The text defines model parameters (eg. I0, I0,m, etc.) but does not spell out the full model equation. Doing so would help the readers better understand the explicit logic of the simulation. <br /> The model discounts agriculture and livestock production using estimates of the rate and extent of damage to agricultural infrastructure citing UNOSTAT remote sensing data published by FAO (references 11, 40-42). The validity of estimates derived from image analysis depends heavily on the control conditions selected for a reference and on the quality of validation and calibration in the field. The percent damage arrived at by automated image analysis algorithms, depends on the selected reference conditions, whose rationale and validity are not given. Field validation is impossible in a war zone which is why the cited reports carry important disclaimers such as: ”This assessment has been conducted based on available satellite imagery, ancillary data and remote sensing analysis for the period 7 October - 31 December 2023 without field validation. Land cover data from 2021 was used as baseline data due to limited availability for data collection in the area of interest and time constraints related to the nature of the report.“ ( https://openknowledge.fao.org/server/api/core/bitstreams/f2ad2f59-0c29-472e-978b-54cef347c642/content) "https://openknowledge.fao.org/server/api/core/bitstreams/f2ad2f59-0c29-472e-978b-54cef347c642/content)") . The limitations of these estimates used in the model should be acknowledged.

Estimating Baseline and Recommended per-capita caloric intake

The per-capita caloric intake for emergency-affected populations is given by the WHO guide and is stratified by age and sex. Given the age and sex distribution of the population of Gaza (gaza_food_data.xlsx, tab prop_age_sex), the mean daily per capita calorie requirement for the population is 2,065 Kcal/person-day. This threshold shown in yellow in Figure 4, is the appropriate criterion for evaluating the adequacy of the food supplied by the humanitarian food cluster. <br /> However, the researchers go beyond this consensus humanitarian requirement, and derive a much higher Gaza-specific estimate “I0“ for the population intake at baseline. The baseline value of I0 appears to be just under 2,800 Kcal per person-day according to figure 4 (blue line value on October 7th, 2023). The paper does not give the baseline value “I0“ explicitly. However, it is nearly identical to the weighted average caloric intake (2,837 Kcal/person-day) observed in a population of obese older Gazan adults (mean age 57, weighted mean BMI 31.4) with a high prevalence of noncommunicable diseases, in a survey conducted during the COVID pandemic between March and July 2020, which was used to impute the daily intake of the overall population. The weighted intake and BMI may be calculated based on the data provided in the gaza_food_data.xlsx spreadsheet, tab prop_age_sex. The estimated pre-war intake, is roughly 33% higher than the humanitarian requirement, or “recommended daily intake”. The model derives the weekly available per person food supply, by subtracting this pre-war intake estimate, from the estimated weekly available daily per-capita food supply (from the sum of private stores and warehouses, agriculture and delivered food-aid, discounted for reported consumption and damage). The model makes the questionable assumption that the emergency-affected population would continue to consume the same amount of food that it did during the war, as it did before the war. Even before examining the validity of the method used to derive “I0“, this assumption forces the model to deplete the available food supply significantly more rapidly (about 33% sooner) than if the recommended humanitarian food requirement were used to simulate the adequacy of the available food supply.

The logic behind the method of imputation to the whole population is not clearly explained (“we sampled random values from each age-sex stratum distribution…” Appendix A, Figure A2). <br /> Supplementary figure A2, entitled “Baseline adult caloric intake” shows simulated untransformed and log-transformed, age and sex specific distributions of energy intake, from Abu Hamad et al., J Hum Hypertens 2023. That reference describes a health survey conducted in Gaza between March and July 2020 among adults aged 40 and older, and using the semi-quantitative Food Frequency Questionnaire for Palestinian Populations which was developed by Hamdan et al., in a population of Palestinian women in Hebron, and published in Public Health Nutrition 17(11) in 2013. While such survey tools may be useful for epidemiological studies, they are intended to classify populations into categories of relative nutritional intake, rather than for deriving valid absolute individual nutrient intakes. In the case of the specific instrument used, Hamdan et al. write that studies like theirs “can be considered a calibration and correlation rather than a validation procedure”. The correlation that they obtained in that study between three repeat 24 hr food recall questionnaires and the semi quantitative FFQ was 0.601 and was not statistically significant (in other words the FFQ gives a similar but poorly concordant result to the reference standard). Moreover, it is doubtful, if the high average food intake of an obese, older and unhealthy population (which was obtained during a health crisis that increased sedentary behavior due to social distancing and isolation), provides a sound basis for imputing routine intakes for a population that is predominantly younger (82% of Gaza’s population are below age 40 – see gaza_food_data.xlsx, tab prop_age_sex), healthier, and not affected by a pandemic. It would be helpful if the researchers clarified these limitations and presented the age-and sex stratified per-person daily caloric derived intake and compared it with the consensus humanitarian requirements.

On 2024-02-26 17:16:20, user Ciarán McInerney wrote:

Please, justify why<br /> you use a 1:4 ratio for matching. Is this representative of the true<br /> prevalence? Perturbing the true prevalence is only valid for control<br /> experimental studies, not for observational studies. Note that some of the<br /> statistics you use to evaluate your predictive model are affected by the<br /> prevalence of the outcome, so arbitrarily fixing it invalidates their<br /> interpretation as real-world evaluations (specifically, NPV and PPV, which are<br /> the canonical statistics for evaluating prediction).

On 2021-10-03 07:19:18, user Ruth Berger wrote:

That age and male sex are major risk factors is well known; mortality associations with pandemic wave should not be reported without factoring in varying levels of underdiagnosis (to my knowledge, it was larger in the first wave than the second) and age-specific vaccination rates.

On 2021-03-16 18:23:19, user Zhe Zheng wrote:

It's a really interesting finding! Will be nice to know what kinds of NPI have been implemented in Lyon. This will enable a comparison between different countries.

On 2021-01-23 14:32:57, user Michael J. McFadden wrote:

You state, "there is reason to believe that there are unknown confounds that may be spuriously suggesting a protective effect of smoking."

Can you expand a bit on what that reason is? I'm guessing you mean there is evidence pointing to such?

Also: I have seen seemingly strong arguments made for Carbon Monoxide blood/cell levels as forming the base of this resistance. Do you have any thoughts on that?

:?<br /> Michael McFadden

On 2024-10-19 10:44:10, user CDSL JHSPH wrote:

Thank you for sharing this insightful work on optimizing antibiotic treatment duration using model-based approaches. Your adaptation of MCP-Mod for duration-ranging trials is an innovative application, and the findings offer promising implications for future clinical trials and antibiotic stewardship. I particularly appreciate how the study addresses the limitations of traditional qualitative methods, and the simulations clearly demonstrate the strengths of MCP-Mod in identifying the shortest effective treatment duration.

While the study is well-executed, validating MCP-Mod with real-world clinical data would further strengthen the conclusions. Pilot trials with actual patient outcomes could provide practical evidence of its utility beyond the simulated environment. It would also be helpful to include a discussion of how your findings could be generalized across different infections or antibiotic classes, as this would enhance the broader applicability of your work. Additionally, incorporating visual comparisons of qualitative models, such as plotting duration-response curves for linear splines and other methods, would give readers a clearer view of how MCP-Mod stands out.

Lastly, a deeper discussion of the assumptions and potential challenges in real-world adoption would offer practical guidance for future researchers or clinicians who may want to apply this methodology in their own trials. Overall, your study is a significant step forward in optimizing antibiotic treatment duration, and I look forward to seeing how these methods evolve in future research and clinical applications.

On 2021-03-11 21:24:49, user disqus_foVd2sEK3I wrote:

Thank you for this important work. I was hoping to take a closer look at the model, only to find out that it was not included. It would be useful to people like me to include the new model's equations for reproducibility.

On 2022-05-02 23:00:41, user Brian Mowrey wrote:

The authors evince no apparent regard for the importance of the interval between PCR+ and serum sample (PDV), especially given the small number of presumed infections among the mRNA-1273-vaccinated in the main analysis, simply remarking

Anti-N seropositivity at the PDV was similar when stratified by median days from illness

Not for the vaccinated, it wasn't (50% vs 32% when stratified, Table 1). Did the authors merely lump both groups together to get around investigating why N-seropositivity was 18% lower in the -5-53 day vaccinated set?

In fact, the same stratification should have been expected to produce different N-positivity rates in the placebo group, had infections been evenly spaced in the -5-53 day interval. Since the authors find only 74% Day 29 N-positivity for placebo participants who are PCR+ on Day 1, and 60% on Day 57 for placebo who are PCR+ on Day 29, it's clear the placebo group isn't defying standard expectations about seroconversion not being instantaneous (bearing in mind a higher false positive rate on Day 1/29 due to screening, obviously) - until the main analysis, when suddenly there is no apparent penalty for near-PDV infections. So maybe there were almost no near-PDV infections in the placebo group (as in, infections skewed toward February due to seasonal patterns) while in the vaccine group the opposite was true (infections skewed to March due to the waning of infection efficacy)?

Thus, both the values for the placebo group and Covid-vaccine group suggest uneven time between PCR+ and PDV. The authors make no comment on this problem and *do not present* a plot of to-PDV-intervals for either group, even though they obviously had full access to that data. This is a glaring oversight at best.

It's not the only one. Have the authors never heard of false positives / base rate fallacy? I doubt it. So why isn't this taken into account, when comparing a group with a frequent outcome to a rare outcome? Among 14.5k participants in both arms, a mere 25 false PCR+ in both groups would be enough to render the main results way off the mark (Placebo: (100% x (1-((.066x648 - 25)/(648-25)) = 97.1% mRNA-1273: 100% x (1-((.593x52 - 25)/(52-25)) = 77.8%). Yet no consideration of the problem is made. The word "false" is not even in the text.

On 2020-10-06 15:42:18, user T_Rogers wrote:

How do we know that NR was the effective factor?? Perhaps it was the other ingredients in the mixture. Also, only 71 patients with mean age of 35 and limited to no co-morbidities. IOW, exactly the profile that would be expected to recover. So, good result, but inconclusive as to efficacy of NR.

On 2020-05-05 22:35:44, user Alan Bell wrote:

"If worse respiratory health and aggravated symptoms in polluted areas are the main channels of action, higher COVID-19 case hospitalization rates should also be expected in these locations."

Not sure that follows:<br /> If two locations each have 150 cases, some of which may remain asymptomatic and undetected.<br /> Location A with low pollution detects 80 cases, 4 of which are hospitalized.<br /> Location B with higher pm2.5 levels detects 100 cases, 5 of which are hospitalized.<br /> Location A and location B have the same hospitalization rate of 5% of detected cases but due to aggravated symptoms location B has a higher detection rate.

The analysis in 6.2.5 is interesting but I am not sure it is conclusive. Maybe random antibody sampling across the whole population will eventually reveal more information.

On 2020-08-19 17:59:56, user petsRawesome1 . wrote:

"Of the 43 patients randomized to ConvP 6 (14%) had died while 11 of the 43 (26%) <br /> control patients had died."

That sounds like the study showed promise on the key metric, mortality, it just did not have enough data when it was stopped. It would be good to be very clear about the reasons for discontinuing the study, as the New York Times of Aug 19, 2020 is quoting this paper as "Last month, one such trial in the Netherlands was stopped when researchers realized that patients given plasma showed no difference in mortality"

On 2020-07-14 17:07:42, user Avnish Singh wrote:

i need email address of Lan-Juan Li. I am from www.meraupbihar.xyz

On 2020-04-13 17:55:08, user Izz Thatso wrote:

The country benefiting the most from its BCG vaccination program, South Africa, probably has too few deaths in the defined 30 day period for it to be of statistical significance.

Why do my comments need to be approved?

On 2020-04-14 15:07:32, user Senad Hasanagic wrote:

Is there any difference between Eastern and western parts of Germany?

On 2020-05-12 17:11:33, user Sui Huang wrote:

Hi Anne (et al) - nice, heroic work! 2 quick questions as this work inspires similar approaches for scaling up community testing...:<br /> (1) Have you examined longer transports, more than the 5hrs @RT, and on ice, or even frozen for a longer time?<br /> (2) Have you tried to skip the RNA isolation step and do qPCR directly in the saliva as others have done?<br /> Thank you

On 2020-05-27 00:52:35, user Jayanti Prasad wrote:

Please post your comments, suggestions & feeback.

On 2020-07-21 15:54:39, user OxImmuno Literature Initiative wrote:

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

On 2022-06-03 06:08:07, user Toby Mansell wrote:

This paper has now been published at eLife. You can view the published version of this paper at: https://doi.org/10.7554/eLi...

On 2025-01-16 06:08:40, user xPeer wrote:

Courtesy review from xPeerd.com

Summary:<br /> The manuscript titled "Typhinder: Rapid, low-cost colorimetric detection of Salmonella Typhi bacteriophages for environmental surveillance" presents a novel colorimetric assay designed to detect Salmonella Typhi (S. Typhi) bacteriophages in environmental water samples. This study primarily focuses on areas with poor sanitation infrastructure, including regions in Brazil, Côte d’Ivoire, Nepal, and Niger, demonstrating high sensitivity and specificity of the assay. The work indicates potential applications in public health surveillance, particularly in resource-limited settings, by providing a cost-efficient method (approximately $2.40 per sample) that does not require sophisticated equipment.

Potential Major Revisions:

1. Validation and Methodological Robustness:<br /> One key concern is the validation of the colorimetric assay only against the double agar overlay method. More comprehensive testing against additional molecular techniques like PCR/qPCR, which are considered gold standards for pathogen detection, is essential to determine the assay's accuracy and reliability under diverse environmental conditions. This gap was acknowledged in the discussion section.

2. Sample Diversity and Detection Limit:<br /> The study demonstrates that the detection limit is 28 PFU/mL, which although sensitive, may need further optimization to ensure applicability in environments with even lower pathogen concentrations. Additionally, the research did not provide adequate comparative data from different environmental contexts, such as varying water sources with potential inhibitors like antibiotics, which could affect assay reliability.

3. Comprehensive Data Analysis:<br /> The study's reliance on environmental surveillance data lacks integration with epidemiological data and molecular-based assessments of typhoid burden. Correlating phage detection with rates of clinical typhoid fever incidents would offer stronger evidence of the assay's utility in public health management. Future studies should aim to establish these correlations more explicitly.

Potential Minor Revisions:

Typographic and Grammatical Errors:<br /> 1. Page 2, Line 1: "particularly in low-resource settings with inadequate sanitation." - Repetition of the phrase "particularly in low-resource settings", consider rephrasing for clarity.<br /> 2. Page 6, Line 3: "require precise data on where typhoid is most prevalent, yet current surveillance methods are expensive and limited in scope..." - The sentence structure could be improved for readability.<br /> 3. Page 9, Line 5: "Antimicrobial resistance among S. Typhi strains poses serious challenges to effective treatment and may lead to higher mortality..." - Consider rephrasing for clarity.

Formatting Issues:<br /> The figures and tables should be better integrated into the text for improved readability. For example, citing Table 1 and Figure 2 explicitly within the corresponding discussion for context will aid readers' understanding.

AI Content Analysis:<br /> - Estimated AI-generated content: Given the extensive detail and specific nature of the subject, it is estimated that the manuscript has less than 5% AI-generated content.<br /> - Highlighted AI-detected sections: The introductory summary and some instances of repetitive phrasing suggest possible AI involvement.<br /> - Epistemic impact: Minimal as the core research contributions and data seem original and substantive.

Recommendations:

1. Enhanced Validation:<br /> Incorporate a broader range of validation techniques, particularly molecular methods like qPCR, to establish the assay's robustness across different environmental samples and contexts.

2. Addressing Limitations:<br /> Include detection methods for concurrent fecal contamination to provide contextual data, enhancing the reliability of typhoid phage detection results as environmental indicators.

3. Future Studies:<br /> Focus future research on correlating phage presence with clinical incidence of typhoid fever, and explore structural analysis of phage-host interactions. This will substantiate the assay's efficacy in public health interventions and policy-making.

Overall, the manuscript provides a promising tool for typhoid fever surveillance in low-resource settings, with significant public health implications. Addressing the detailed critiques will strengthen the manuscript and its potential impact.

On 2021-08-01 00:45:40, user Michael Kowalik wrote:

18 deaths in the study group and 16 in the control group, so (at best) zero lives saved from Covid.

On 2021-08-11 23:42:57, user circleofmamas wrote:

Where did you get this? "All we get from these figures is that the relative risk of death is 12.5% higher for the vaccinated vs the unvaccinated: (18-16)/16."

Are you looking at overall deaths?

On 2021-08-18 16:08:24, user circleofmamas wrote:

I don't know why you three are dismissing this, because there is already a signal for increased risk of myocarditis and pericarditis after mRNA vaccination. The elevated risk was observed for both females and males, male ages spanning 12-49 were at an increased risk of heart inflammation. We should be paying attention, not dismissing potential signals.

On 2021-08-16 20:41:22, user John wrote:

He's talking about a self-reporting system called VAERS that records if someone died after taking the vaccine.

Anti-vaxxers get confused by it, because VAERS does NOT show that the death was related to the vaccine.

For example, if someone gets vaccinated then dies of cancer, it can appear in VAERS. But that does not mean that the vaccine caused the cancer or caused them to die from cancer etc.

On 2023-05-28 06:54:51, user Stuart Gilmour wrote:

Dear authors, I really want to believe this study (I am vulnerable to Ramsay Hunt Syndrome and have got this vaccine, and I would love to believe it also reduces my risk of dementia!) but I think you have massively under-estimated the effectiveness of the vaccine, which is a real missed opportunity. I want to explain why and I hope you'll take my comments into account. I think there are three sources of error in your study which I list in order of severity: 1) failure to take into account period at risk, 2) the change in slope term and 3) confounding due to education/wealth in sub-analyses.

[Obviously, the comments that follow assume I have correctly understood your methods, so please forgive me if I have missed something your explanation]

1) is the reason I think the study under-estimates the effect. I wondered why it is that you found a vaccine efficacy (after adjusting for take-up of the vaccine) against shingles of 41%, while the 2005 NEJM study you reference finds it to be 55%, and I think this is because you have not properly accounted for follow-up time. Judging by how you report probabilities, you seem to have calculated the proportion of people over seven years who got shingles (Fig 2) or dementia (FIg 3). This is also clear from your equation (1), which is a linear probability model. But since shingles incidence, dementia incidence and death risk increase by age and your primary study cohort is 80 years old, follow-up time is a very important variable. Judging from your figure 2, the youngest people were 78 and the oldest 82 in this study. It's very likely therefore that the youngest people had to be followed for considerably longer before a diagnosis of shingles/dementia, and were also less likely to die of other causes. A person who dies of other causes before getting shingles/dementia should not be considered in the calculation, since we didn't find out whether they got it - they should be censored. Then, if we calculate incidence densities, we will find the youngest people (with the lowest proportion of cases) have a considerably longer follow-up time to diagnosis, and were less likely to drop out of follow-up early due to death. If you properly account for this in the model, I think you'll find that the rate in younger people is much lower than in older people and the discontinuity is greater.

I don't have UK data to hand, but I do have a life table by single year of age for the USA, which implies that there would probably be about 40% more follow-up time in the 78 year olds than the 82 year olds over the entire 7 years of the study, simply because of drop out due to death from all causes - a 78 year old american has a 4% chance of dying in one year, while an 82 year old has a 5.7% chance. Those differences add up over 7 years of follow-up!

This study is a classic survival study, and your decision not to use the follow-up time means that you have over-estimated the incidence density in young people and under-estimated it in older people. This also explains why your sex-stratified analysis finds no effect in men. How could the vaccine not work in men but work in women? Because at this age (~80 years old) men are dying much faster than women, with death rates increasing more rapidly over the study period, which attenuates the effectiveness more in men than in women.

If you use an incidence density (Poisson regression) or survival approach, it's easy to reproduce the approach described in equation (1) but you'll be properly accounting for follow-up time, avoiding the known problems associated with a linear probability model, and properly able to compare your results with those of the previous shingles vaccine studies.

[I'm sorry all my comments here hinge on my interpretation from your methods that you have assumed a 7 year follow-up for everyone, and simply calculated the proportion of events as the number who got shingles/dementia divided by the number at risk at the start of the 7 years. If I'm wrong about this, please ignore everything I wrote!]

For problem 2), the change of slope term, it seems obvious to me that the slope after week 0 in figure 3A is poorly fitted. If there was no change of slope term in this model, the change in level would be smaller and your study would show no effect. Was the beta3 term in your model for figure 3 statistically significant? I think it wasn't - there is no visible change of slope in the data shown there. Given how borderline your estimate of the change in level (Beta1) is, I think the conclusion of this analysis depends heavily on whether you choose to include the non-significant change of slope. Of course, this isn't very important because a) we should always report studies of this kind separately by sex and b) once you properly adjust for follow-up time the effect of the vaccine will be so huge that we'll immediately have a statistically significant effect with or without the change of slope term.

For problem 3), you estimate the CACE based on the assumption that there is "no other difference in characteristics that affects the probability of our outcomes occurring", and date of birth eligibility threshold "is a valid instrumental variable to identify the causal effect of receipt of the zoster vaccine on our outcomes". I'm not sure why you would believe this. People who receive any voluntary preventive health care in the UK are much more likely to be wealthy, to be better educated, and to be from certain occupations and backgrounds, and I would suggest it's highly likely that these factors are strongly associated with reduced risk of dementia. The method here is nice, but the assumption is completely unreasonable in the NHS context, and it's likely that these confounding factors would lead to a reduction in the CACE estimate. Again, if you properly account for follow-up time I doubt this will matter because the raw impact of the vaccine eligibility itself will be so much larger than your estimate that you will find a much bigger impact without needing to do any calculation of CACE (but anyway a simple caveat about this, or a calculation separately in each wealth stratum, might solve the issue).

I can't see any way that the lack of proper calculation of follow-up time would reduce the effectiveness of the intervention you have tested, so I'm going to continue to believe that this vaccine prevents dementia, but I worry that you have massively under-estimated the size of the effect and I guess there is a tiny chance the impact of this mis-calculation could go the other way.

I guess you could argue it doesn't matter if you've under-estimated the effect but I would say it does. I'm sure you're aware that in the UK the chickenpox vaccine is not part of the routine childhood immunization schedule. If your study finds a huge effect of shingles on dementia risk, this is a strong argument for preventing it at childhood, through inclusion of the vaccine in the routine schedule. But currently your study finds no benefit for men, a 20% overall reduction in relative risk, and about a 40% reduction in relative risk for women. I think if you properly account for follow-up time the effects will be much larger and consistent across men and women. Even a cursory consideration of such large numbers would surely be sufficient to tip even the UK's relatively anti-vaccination institutions into recommending both a) routine chickenpox vaccination of children b) routine shingles vaccination of adults and c) earlier implementation of adult vax. Currently for example in Japan the vaccination for shingles is recommended at age 50 but not covered under insurance, costs about 40,000 yen (350 pounds) and is not widely taken. If it has a huge impact on dementia risk the policy implications are enormous. So please don't undersell your work by using this linear probability model!!!

Thank you!<br /> Stuart Gilmour<br /> Professor, Biostatistics and Bioinformatics<br /> St. Luke's International University<br /> Tokyo<br /> Japan

On 2024-05-08 05:54:42, user Valerie Yang wrote:

https://pubmed.ncbi.nlm.nih...

This article has been published on November 9, 2023

On 2021-12-03 04:03:08, user Srinivasa Kakkilaya wrote:

This is completely misleading. There is no proof about the so called re-infections as being due to omicron variant, yet the authors seem to blame and speculate further that omicron is causing and can cause re-infections.

On 2020-07-21 05:50:56, user Josh Lerman wrote:

Super interesting. I also noticed this cycle just by eye browsing the CA data. If it is an artifact of data collection, that should maybe be fixed. Thanks for the analysis.

On 2021-06-27 00:03:19, user Laurence Weinberg wrote:

Please also refer recent JAMA paper below:

https://jamanetwork.com/jou...

On 2020-05-12 07:59:56, user Erik Hansson wrote:

Thank you for your work, it is a valuable addition to consider that that people have different social activity levels, but I am concerned that your approach miss two important aspects which will underestimate the herd immunity threshold and make it less valid as an indicator of the risk of new severe epidemic flares:

1. Social distancing recommendations from Swedish authorities likely have different effects on levels of social activity between social strata. R is probably more flexible downwards in more affluent social classes leading to different seroprevalence in different strata when the global disease-induced herd immunity threshold is reached.

2. Post-social distancing (i.e. after achieving disease-induced herd immunity threshold) social interaction will happen primarily within social strata (i.e. within seroprevalence strata).

Lower social classes will be less able to achieve a low level of social activity due to household crowding, dependence on public transportation and inability to work from home due to having manual work (https://gupea.ub.gu.se/hand... "https://gupea.ub.gu.se/handle/2077/64124)"). This may lead to higher disease transmission in lower than higher social classes. Add to this the situation in elderly care in which the absence of PPEs has probably led to quite intense transmission both to and from workers, who are strongly concentrated to lower social classes in Stockholm. Disaggregated outcome data is scarce but there seems to be empirical evidence for such a social gradient in covid-19 infections both in hospitalized cases and very limited seroprevalence studies (contact Björn Olsen in Uppsala for more details or read Expressen article from last week - their study found 0% seroprevalence at Östermalm (~Kensington and Chelsea) in the end of April, n=?). Information from other major cities tell a similar story of a social gradient.

Under normal circumstances persons from lower social classes may not necessarily have higher levels of social activity than persons from the more affluent classes. I am concerned it may rather be the opposite as people from higher social classes may more likely engage in several activities less accessible to persons from lower classes, activities that are greatly reduced in a semi-quarantine setting such as cultural and sports events, parties, eating and drinking out, office work and meetings, conferences, university education, etc, but that are expected to be possible to do in a society having reached herd immunity.

Furthermore, due to segregation by class and ethnicity such post-social-distancing activities will likely primarily be done together with other persons likewise having been able to limit their activities during the first phase of the epidemic. There are thus conditions that allow rapid disease transmission in more affluent social strata if these go back to business as usual. It may even be argued that estimating a herd immunity threshold as an average percentage within a strongly segregated population (and segregation is probably aggravated during social distancing) is not especially meaningful. If there are large enough pools of connected susceptible individuals there is still a possibility of epidemics that overwhelm the healthcare system.

Another concern, which is not directly related to the present manuscript but to a media appearance in relation to it is the use of uncertain modeled estimates to make predictions of when Stockholm will reach the disease-induced herd immunity threshold, in June 2020 - less than weeks from now. This model estimated 26% had been infected by May 1. A critique of this model estimated 5-10% (https://twitter.com/AdamJKu... "https://twitter.com/AdamJKucharski/status/1254084771535376391)"), and the only (to my knowledge) somewhat representative seroprevalence study found 7.5% (Björn Olsen) at that time. Two separate methods that concur so well seems more credible than one modeled estimate, and the difference is large.

The combination of estimating an artificially low herd immunity threshold and using potentially exaggerated cumulative infected proportion risk declaring “all-clear” in Stockholm much prematurely.

Erik Hansson<br /> MD, MSc Epidemiology

On 2024-07-10 21:16:54, user Nicolas Hulscher wrote:

This study inadequately addresses the role of vaccination by failing to account for the documented adverse events associated with mRNA vaccines. According to Polykretis et al ( https://doi.org/10.1111/sji.13242 ), there is substantial evidence of serious adverse events following mRNA vaccination, including myocarditis, which is known to increase the risk of SCD. Their analysis highlights that the incidence of myocarditis post-vaccination is significantly higher than post-SARS-CoV-2 infection, especially in young males. Additionally, their findings show a concerning rise in athlete deaths due to cardiovascular complications since the rollout of mRNA vaccines, far exceeding pre-pandemic averages. Thus, Binkhorst and Goldstein's conclusion that there is no evidence of a link between mRNA COVID-19 vaccination and SCD in athletes is unsubstantiated and overlooks critical data suggesting otherwise .

On 2020-05-20 00:57:13, user David Philpott wrote:

For the discussion: If you wish to make a comparison with influenza, please give a citation for this "a (0.1%, 0.2% in a bad year)". I have not found a reference for fatality risk for influenza using serologies that is in the 0.1-0.2% range. Typically, those numbers are for doicmented symptomatic cases which is not what is being addressed in this manuscript. Rather, the available evidence is much lower for influenza, perhaps in the range of 0.01%. See here for example: https://www.ncbi.nlm.nih.go...

On 2020-10-24 19:58:34, user Per Sjögren-Gulve wrote:

Why not use multiple logistic regression and examine age plus additional predictive variables (continuous awa categorical) together + interaction terms? Studies can be numbered/ID:d and included as one predictive variable in a common dataset. In that way, differences in distribution of the other predictive variables between the studies can be considered or - if there are no such differences - rejected and datasets pooled.

On 2021-07-21 20:13:20, user Dean Karlen wrote:

The first claim in the title (vaccines dampen diversity) is not substantiated.

Linear entropy is a measure of how evenly spread are the lineages. As stated, if for one month all sequences are the same lineage (no diversity), then the measure is zero… on the other hand if the sequences are evenly distributed amongst all 1296 lineages, the value is about 7. When a VOC appears, it is labelled a VOC precisely because a large number of cases are seen of that lineage! So, of course, when VOCs begin to dominate the linear entropy decreases.

Figure 1 shows that starting in January 2021, just as alpha becomes dominant, linear entropy begins to decline as expected. January 2021 happens to be the time that mass vaccination gets underway - so it is not clear how much (or if any) of the reduced diversity is due to vaccination.

The second claim in the title (regarding breakthrough infections) is questionable. The sample size is small and no statistical test is presented that supports the claim of greater diversity in the unvaccinated compared to vaccinated.

On 2021-12-01 11:23:59, user Drago Varsas wrote:

Common sense will do. "No matter the causes of death ( like being killed by a truck driving over you ) if covid ( a test run at 35-45 cycles never meant to be used for diagnostics ) it counts as covid" The test does not exclude other corona viruses or influenza and most people had serious chronic illnesses and died from them after a very questionable covid positive test. People will other illnesses were also falsely labeled as covid and not treated for their actual illness and often consequently died. It's a crime against humanity and no hair splitting talk will change this.

On 2021-10-11 18:40:32, user Andrew T Levin wrote:

Comment #1: Research in Context

1. Diamond Princess Cruise Ship. The manuscript makes no reference to any epidemiological analysis of this episode, which informed seminal assessments of the age-specific infection fatality rate (IFR) of COVID-19.[1-4] Nonetheless, that evidence is particularly relevant, because the cruise ship’s passengers included 1231 individuals ages 70+ who were not merely “community-dwelling” but healthy enough to embark on a multi-week grand tour of southeast Asia. Following extensive RT-PCR testing, 335 passengers ages 70+ were confirmed to have been infected with SARS-Cov-2, and 13 of those passengers died from COVID-19 – an IFR of about 4%. Moreover, the strong link to age is underscored by the even higher IFR of 8% for passengers ages 80+. Given the size of that sample (which meets the 1000+ threshold used here), this evidence should certainly be incorporated into this meta-analysis.

2. Comprehensive Tracing Programs. The manuscript makes no reference to countries that succeeded in containing the first wave of the pandemic in spring 2020 through systematic tracing and testing of all contacts of infected individuals.[5] Such evidence is particularly relevant here, because the virus was contained within the “community-dwelling” populations of those locations and never spread to any elderly care facilities. For example, in the case of New Zealand, there were 256 infections and 19 deaths among adults ages 70+ -- an IFR of about 7%.

3. Hospitalized Patients. The manuscript cites a single study (published in July 2020) that examined the association between comorbidities and mortality risk of COVID-19.[6] However, that study was not able to distinguish whether comorbidities were linked to greater prevalence (the probability of getting infected) or to a higher IFR (the risk of mortality conditional on infection). Unfortunately, the manuscript makes no reference to any subsequent studies on this issue. In particular, a large-scale study of U.K. BioBank participants found that measures of frailty were indeed associated with higher mortality rates in the overall panel but not linked to mortality within the subset of hospitalized COVID-19 patients.[7] In effect, the prevalence of COVID-19 was markedly higher among residents of U.K. nursing homes compared to individuals of similar age living in the community, but the IFR was not significantly different. Those findings directly contradict a key assertion made at the start of this manuscript.

4. Prior Meta-Analysis of Community-Dwelling Populations. The introduction of this manuscript neglects to mention that an existing meta-analysis study (published in Nature in November 2020) was specifically focused on assessing IFRs excluding deaths in nursing homes.[8] That study estimated the link between age and IFR using seroprevalence and fatality data for adults less than 65 years old, and then showed that the model predictiions were consistent with data on fatalities among community-dwelling adults ages 65+. Moreover, that study used seroprevalence data adjusted for assay characteristics, and the results were obtained using a rigorous Bayesian statistical model that incorporated random variations in the time lags between infection, seropositivity, and fatal outcomes – a striking contrast to this manuscript, which uses rudimentary assumptions to address those issues.

5. Other Meta-Analyses. The introduction of this manuscript briefly refers to two other meta-analysis studies of the link between age and IFR.[5, 9] However, the manuscript then asserts: “Importantly, the vast majority of seroprevalence studies include very few elderly people.” (p.5) That assertion is supported by a single citation to the SeroTracker database, which provides comprehensive coverage of all existing national, regional, and local seroprevalence studies across the globe.[10] However, this assertion is completely incorrect as a characterization of the preceding meta-analysis of age-specific IFRs. As indicated in Levin et al. (2020, figure 5), that meta-analysis study included seroprevalence data on older adults (including narrow brackets for ages 60-69, 65-74, 70-79, and 75-84 as well as open-ended brackets for ages 60+, 65+, 70+, 80+, and 85+) from nine national studies (Belgium, France, Hungary, Italy, Netherlands, Portugal, Spain, Sweden, and the U.K.) and eight regional locations (Ontario, Canada; Geneva, Switzerland; Connecticut, Indiana, Louisiana, Miami, Missouri, and San Francisco, USA).[5]

On 2021-12-31 07:47:11, user eriugena wrote:

The current stats in countries like Ireland show 1/3 to 1/2 people testing positive every day. Ok, the PCR test - putting it mildly - is imperfect. However, we can take it using very simple math that 100% of the population is infected within a week. End of "pandemic".

On 2022-01-08 03:26:25, user Neven Karlovac wrote:

Interesting article but the author's explanation of the negative infectivity seems arbitrary speculation and the article would be better without it.

On 2021-07-11 19:31:18, user geek49203 wrote:

Published reports state: "“The big takeaway was that if you are not vaccinated, and were not previously infected, one, you have a very high risk getting infected—24 percent of employees over a year tested positive." (Epoch times). But I see only 254 out of 4,313, which is 5.8%. Even allowing for a doubling of the timeline (to a full year?) that's around 11-12%, which is fairly in line with what the Brits published in May in The Lancet.<br /> SECOND - it's obvious to me that there is a likelihood that the number of infections are seasonal. IF those who got injections were vaccinated on the downward slope (ie, Feb 21 forward) instead of the upward slope of Dec 20-Jan 21, wouldn't the results be much different? So studying those who got the vaccine would have to take into account the prevailing non-study environment, correct? A non-vaccinated person wasn't very likely to get COVID in June of this year, or June of last year, correct?

On 2021-12-15 11:10:40, user DatenNarr wrote:

The main Shortcoming of this publication is that it accentuates the participation of unvaccinated individuals on new infections (91.1%, or 8–9 of 10), but does not mention the participation of vaccinated (49% or 5-6 of 10). 51 + 25 + 15 = 91 and 9 + 25 + 15 = 49 according to picture "FIG. 1". You see, the portion of vaccinated is not as small as someone would image (9%).

Because of the withholding of the participation of vaccinated individuals, the result description is insufficient and misleading. It gives people a wrong illusion as if the unvaccinated would be responsible for 91% infections. Such illusion leads to the effect that people fail to see the necessity to reduce the contacts of vaccinated persons in fight against the pandemic disease., with wrong politics about Covid-19 as result.

My proposal to the publication:<br /> 1) Change please the publication title to "participation of vaccinated and unvaccinated individuals on new Covid infections"<br /> 2) Add please the statement "vaccinated are involved in 5-6 of 10 new infections" after the statement "unvaccinated are involved in 8–9 of 10 new infections".<br /> 3) Add please the statement "the vaccinated population plays a role in 49% of cases" after the statement "the unvaccinated population plays a role in 91.1% ... of cases".<br /> 3) Take please real data about symptomatic cases from the RKI's weekly report of KW 41-44 for checking your modeling result, from which one can get that the vaccinated population plays at least a role in 40% of infection cases.

On 2020-04-17 17:43:15, user Wouter wrote:

Hi Christian!<br /> Some short answers: we double checked that our results are in line with all reliable data we had available as up to Long Friday.<br /> ICU cases in Sweden are lower than in many other countries because in Sweden, apparently more people are treated paliatively rather than sent to ICU. This is partly apparent in the low death rate on ICU (20% as reported lately) in Sweden; partly as you may have seen in the press today: https://www.svt.se/nyheter/... partly because SLL has guidelines as to not take >80 year olds into ICU.<br /> We know about the ICU data, but in a scientific report you need to write the figures you can refer to, not a moving target. ICU used to be 540 places in Sweden, now around 1000 or more.<br /> The statistical data we use (from FHM) is at any moment in time an underestimation, which makes comparison with current numbers difficult. Also, apparently, many elderly die without being tested, and do not occur in the reported figures. We will need to look at the "överdödlighet" - which we do, but figure here are also delayed.<br /> Our model (figs B) predict an exponential rise with doubling time 5 days. This is in line with the daily reported fatality numbers in the week before Long Friday. This exponential rate may have slowed down somewhat (8-9 day doubling time?) but it is still too early to know this for sure. <br /> The overall estimate of # dead, however, is reasonable. Pls see the interview with Tegnell in which he estimates 40-50% of Sweden infected and fatality rate "as low as 1% or even lower" (http://www.gmfus.org/events... "http://www.gmfus.org/events/europes-response-coronavirus-virtual-update-swedens-chief-epidemiologist)"). check video min 38-41. Our model shows ~80% infection before immunity in the community...<br /> Best greetings! //Wouter

On 2023-03-17 20:15:27, user Guilherme Mattos Jardim Costa wrote:

Peer-reviewed version available at: https://doi.org/10.1186/s12...

On 2020-10-23 14:56:07, user Caetano Filho wrote:

The study states that 38 participants of the<br /> nitazoxanide arm reported complete absence of symptoms after 1 week follow-up. How this number of participants could correspond to 78% of the 194 ones enrolled on that arm?

On 2025-02-16 02:16:55, user Michael Pazianas, MD wrote:

Low BMD can be a common finding in both osteoporosis and renal osteodystrophy—two distinct histological diagnoses with distinct pathophysiology. While a low BMD and a T-score below -2.5 are often used to define osteoporosis, this finding does not necessarily indicate an osteoporotic etiology. Non-osteoporotic causes should be considered.

In this study, the authors included patients with CKD who were diagnosed with osteopenia or osteoporosis based solely on BMD measurements, rather than bone biopsy findings. However, low BMD in these patients could stem from other forms of renal osteodystrophy, such as adynamic bone disease or osteomalacia, rather than true osteoporosis.

"Given this premise, a more accurate and clinically relevant title might be: 'Low BMD Prevalence in Cardiovascular Kidney Metabolic Syndrome: Implications for Mortality.' The current title promotes an overly simplified approach that risks making the already challenging task of successfully managing these patients—particularly those with CKD—an even more distant prospect. This concern is especially relevant because antiresorptive therapies, commonly prescribed for osteoporosis, are contraindicated in adynamic bone disease, a pathology prevalent in CKD, as well as in osteomalacia."

On 2025-03-21 22:17:58, user Catherine wrote:

Oestrogen is a inflammatory hormone whereas progesterone is the opposite. I would suggest use of progesterone is the way forward not Oestrogen.

On 2020-08-18 12:42:45, user Saban Öztürk wrote:

The peer-reviewed and published version of this article can be accessed using the following DOI:<br /> https://doi.org/10.1002/ima...

Also, this link can be used for direct access to this article: <br /> https://onlinelibrary.wiley...

On 2020-06-25 15:01:25, user Kirielson wrote:

I think this paper is fine, my question I would have for the authors: Did you attempt to evaluate if the patient could relay back those risks to you through any metric? Finding a way to see if a patient understands it by evaluation may see how effective one is over the other while looking at their preferneces.

On 2020-04-06 06:00:12, user Sócrates Ufrb Menezes wrote:

Is it possible that the anchoring and transmission of the SARS-CoV-2 (RNA) genetic material, is related to HYPERTONICITY AND OR HYPOTONICITY and the attraction to the target, as well as its replication, is related to Na levels in the cell gradient? Thanks and good work!

On 2021-09-07 14:39:30, user Brett Tyler wrote:

Interesting approach to use Kallisto. I have a couple of questions. 1. How do you account for variability in the amplification efficiency of different ARTIC amplicons. 2. How do you account for the numerous reads that match non-informatic regions of the genome (i.e. those with no informative SNPs)? 3. How do you account for reads that match multiple different variants?

On 2021-01-31 17:34:25, user Paul Hunter wrote:

Very misleading analyses in this paper and the conclusions and recommendations that the authors make are not supprted by their data. How can the say “The decrement in<br /> incidence was evident from day 18 after first dose” and then estimate efficacy using data from days when they know that vaccine was not yet working. What they have shown is by three weeks after injection a single dose of Pfizer gives about 80 to 90% protection

On 2022-08-31 21:35:38, user Luis Graca wrote:

Note that the protection efficacy in the peer-reviewed publication has slightly different values. The difference is due to the fact that in the medRxiv preprint the protection was calculated as (1-odds ratio)*100, while in the NEJM graph the protection was calculated as (1-relative risk)*100.<br /> Citation: Malato et al, N Engl J Med, 31 August 2022, DOI: 10.1056/NEJMc2209479, https://www.nejm.org/doi/fu... "https://www.nejm.org/doi/full/10.1056/NEJMc2209479?query=featured_home)")

On 2020-07-16 18:06:03, user Marcos Woelz wrote:

What about recovered people´s blood serum? Any good news from that already? Untill that, let´s keep on helping people stay at home

On 2021-05-06 19:34:30, user disqus_p0Pq7NxFg7 wrote:

Maybe I missed it, but you did not include a control group of individuals that had Covid and no vaccination. So, I curious how you can reach a conclusion that the vaccination improves immunity for individuals that had Covid.

On 2022-05-12 03:50:58, user Stuart wrote:

Published in JCI Insight here: https://insight.jci.org/art...

On 2021-09-25 10:12:08, user Jan Podhajsky wrote:

I forgot to add that researchers allowed persons below 15yo to enter the survey without parental/guardian consent. This is illegal in Czechia.

On 2021-10-29 17:29:16, user Kevin Zabow wrote:

Despite what mainstream media continued to say, asymptomatic carriers are rare and they didn’t find asymptomatic people spreading it according to the data. But yes if a vaccine when it works as intended reduces symptoms in some people, then yes those people are more likely to spread the virus because they will still use public transport, go to work, especially nurses, Doctors, Aged Care workers and soldier on with Codral or Panadol (If they feel they need to) Or whatever it may be, not thinking they have it, which ends up spreading to vulnerable people. Rather than staying at home. But this tends to go over people’s heads, even though it seems so obvious E.g. Flu shot.

On 2019-07-12 02:50:41, user Guyguy wrote:

EVOLUTION OF THE EBOLA EPIDEMIC IN THE PROVINCES OF NORTH KIVU AND ITURI

Thursday, July 11, 2019

The epidemiological situation of the Ebola Virus Disease dated July 10, 2019:<br /> Since the beginning of the epidemic, the cumulative number of cases is 2,451, of which 2,357 are confirmed and 94 are probable. In total, there were 1,647 deaths (1,553 confirmed and 94 probable) and 683 people healed.<br /> 364 suspected cases under investigation;<br /> 14 new confirmed cases, including 9 in Beni, 3 in Katwa, 1 in Mabalako and 1 in Kalunguta;<br /> 2 new confirmed cases deaths:<br /> 2 community deaths in Beni;<br /> Data on deaths of confirmed cases in CTEs are not available this Thursday.<br /> NEWS<br /> OPERATIONS OF THE RESPONSE<br /> Adaptation of the vaccination strategy in areas affected by EVD<br /> Since June 13, 2019, the Ebola vaccination protocol has been adapted to better respond to the particular context of this tenth epidemic, especially the security context and the high rate of confirmed cases that are not listed on the contact lists.<br /> This new protocol contains three strategies that can be used depending on the environment in which confirmed cases are found. These three strategies are:<br /> Classic Ring: The classic strategy of vaccinating contacts of confirmed cases and contact contacts.<br /> Enlarged ring: It is also possible to vaccinate all inhabitants of houses within 5 meters around the outbreak of a confirmed case.<br /> Geographical Ring: In an area where team safety can not be guaranteed, they can vaccinate an entire village or neighborhood.<br /> In addition, following the international meeting on Ebola vaccination last month, the Minister of Health adopted a circular on Wednesday, July 10, 2019 concerning the use of other experimental Ebola vaccines in the context of this tenth epidemic. . Due to the lack of sufficient scientific evidence on the efficacy and safety of other vaccines as well as the risk of confusion among the population, it was decided that no clinical vaccine trials will be allowed throughout the country, the extent of the territory of the Democratic Republic of Congo during the ongoing Ebola outbreak.

HEALTH WORKERS<br /> 131 Contaminated health workers<br /> The cumulative number of confirmed / probable cases among health workers is 131 (5% of all confirmed / probable cases), including 41 deaths.

On 2020-12-01 04:48:42, user Sandeep B wrote:

Another one !

https://www.statnews.com/20...

On 2022-01-20 18:37:15, user Sam Smith wrote:

Why to use EchinaForce 3 times a day? Why not 1 time a day or 5 times a day?

On 2020-10-19 16:19:34, user Angry Cardiologist wrote:

This is an interesting set of observations among a self-selected series of patients who are suffering chronically following respiratory infection that may or may not have been from SARS-CoV2. As the paper explains, 73 of the 201 subjects did not have PCR or antibody confirmation of SARS-CoV2 infection. For a paper purporting to describe a syndrome that is specific to “long COVID” it behoves them to be more selective in their inclusion.

My next criticisms will focus on the heart, as this is my are of specialty.

1) The identification of “borderline” LVEF in MR was based on *echo* derived data in the Framingham Heart Study, per their citation (S6). The mean age of this cohort of 363 individuals was 57 years old (±SD 13y). This contrasts greatly with this population of mean age of 44 years, ± SD 11.0 years. Wrong measurement. Wrong population.

2) For their determination of myocarditis by MR, they say “T1 is a field-strength specific parameter in line with study-specific. Thresholds based on healthy controls in the same setting n=5.” This is not based on any published data. Only 5 controls (not otherwise described) is quite flimsy to base normal values.

For this paper to be taken seriously, they need to address these very obvious weaknesses in subject selection and cardiac image analysis. I will leave further analyses of other areas to their respective experts.

On 2020-10-20 01:11:07, user Larry Weisenthal wrote:

Enrollment criteria are confusing: Abstract states that study subjects were symptomatic. Methods inclusion criteria state that the study was open to patients with CV19 diagnosis, but to my knowledge it wasn't clearly stated whether or not patients had to have lingering, long term symptoms.

We'd all like to know the denominator, i.e. of all young low risk patients, what percent go on to have long covid symptoms? This paper is confusing, with regard to this question.

Larry Weisenthal/Huntington Beach CA

On 2025-11-17 02:37:56, user Charin Modchang wrote:

Now published in BMC Infectious Diseases. DOI: https://doi.org/10.1186/s12879-025-11793-7

On 2021-02-16 19:11:19, user Tim Pollington wrote:

A really relevant study and definitely agree that future modelling should include HIV-VL; in fact reading your other paper I think M/F would be worth including in a mechanistic model too.

Sorry if I misread your paper, however I thought that the main result may not necessarily be that surprising, given that one would expect to find a higher probability of observing presence of PKDL (say) cases at higher VL incidences.

If there was a way of incorporating in your statistical model the current VL, PKDL and VL-HIV counts (as opposed to 'presence of' binary variables) in predicting future VL then could then get at the relative contribution of these three groups, accounting for the infectious time they are around (before treatment or unfortunately their death for HIV-VL patients). I wonder if VL-HIV may be superspreaders wrt the others (as parasite loads would be higher? and not reduce to zero following drugs) which would strengthen your argument re VL-HIV being a forgotten group in VL control.

Tim Pollington.

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

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

On 2021-03-23 18:30:37, user Moshe Elitzur wrote:

To CL: <br /> Do lockdowns work??

They certainly do, but we could not prove that decisively because lockdowns were implemented, on average, just before "flattening of the curve" was already occuring. So lockdowns were not put to the test during the first wave.

On 2020-06-16 17:01:12, user Dr Jim Porter wrote:

Is there a fractal structure linking a "second wave" of spread with "second phase" of disease progression?

On 2021-06-16 22:06:17, user thomas wrote:

I got the idea from people in the comments. They are suggesting that the recommendations from this study are wrong, and people who have had COVID need to get the vax. That would imply that people aren't getting immunity naturally, or that natural immunity is inferior. That's all my first question was about.

On 2021-07-07 10:03:14, user ateamrdr wrote:

This is very interesting. What about the issue of transmission? If you do come into contact with the virus after having been previously infected, you might recover faster, but are you more likely to transmit if you have natural immunity vs vaccine immunity?

On 2021-10-17 02:07:31, user X Basch wrote:

Xu, Katherine et al. “Elevated NGAL is Associated with the Severity of Kidney Injury and Poor Prognosis of Patients with COVID-19.” Kidney international reports, 10.1016/j.ekir.2021.09.005. 8 Oct. 2021, doi:10.1016/j.ekir.2021.09.005

On 2020-03-25 19:31:48, user Charles Haas wrote:

My concern with their disinfection experiments is that there is no indication that they neutralized the disinfectant prior to culturing. This is an absolute necessity.

On 2020-06-21 12:03:07, user Isaac Nault wrote:

I'd be interested to see this methodology applied to estimating the hospitalization rate.

On 2020-07-20 17:29:44, user Kamran Kadkhoda wrote:

The following finding <br /> Using the pre-defined cutoffs, the sensitivity of IgG antibodies rose from 7% (<=7days) to<br /> 7% after 14 days of symptoms. The sensitivity of IgA and IgM rose to 91% and 81% 2-4 weeks<br /> post-symptom onset but dropped after 4 weeks to 57% and 40%, respectively.<br /> ...is classic for an anamnestic immune response especially given<br /> IgG showing up early similar to other studies and the half-life of<br /> IgG-plasmablasts suggesting response to previous response to common CoVs.

On 2021-08-19 13:20:53, user Libby wrote:

Thank you for this important study.

On 2022-01-28 20:32:03, user Ranya Srour wrote:

The article is a good baseline for future studies involving suicidal ideation and bar graphs are very clear and easy to interpret. By extension, the study addresses the question: is it necessary to wait for sobriety before defining a patient as suicidal?

Maybe discuss this question directly in the discussion more; since it is the main question it may be valuable to expand on the in discussion.

On 2022-01-28 20:30:20, user Sarah wrote:

What tool was used to determine suicidal ideation?

On 2021-03-30 17:34:43, user Gustavo Bellini wrote:

Dr. Alex Vasquez published a critical analysis in relation to that study. He is demanding a retraction from JAMA:

https://youtu.be/_sfGV_xz0_Q

On 2024-04-27 18:39:09, user Haley DelPlato wrote:

As a young adult whose life has been put on hold for the past 3 years due to Topical Steroid Withdrawal, I can't thank you enough for this work!

Seeing studies about TSW not only helps validate my pain that so many medical professionals have dismissed, but also contributes greatly to advancements in dermatopathology looking forward. The current stigma that makes TSW such a controversial concept NEEDS to be eradiated, a complex task that ultimately relies on substantiated clinical proof to combat misinformation. Unfortunately, the current scope of dermatopathology has kept so many folks unaware of TSW and trapped in harmful cycles of topical corticosteroid addiction. I hope this will be the first of many legitimate works seeking to uncover the truths about this tragic condition so future generations will be believed, treated, and cared for with dignity, in ways the current dermatological standards simply haven't allowed for.

Appreciate the strides this study has taken toward a more compassionate reality for TSW sufferers!

On 2020-11-12 12:47:34, user leesohyun19 wrote:

is it possible without masking image?

On 2021-06-11 09:52:46, user Schupp wrote:

Over 56% of the test candidates involved had been vaccinated with BCG 12 months previously.<br /> Unfortunately, it is not clear from the study what the distribution of the reduced immune response to the previous vaccination is.<br /> A reduced response of the toll like receptors is even desired on certain time!<br /> For this reason it would be interesting, how it looks 2 or 3 months after the 2nd vaccination?<br /> Unfortunately, there is no data on this either.<br /> Therefore, as an absolute layman, I can not classify this study as very questionable.

On 2021-09-18 17:00:44, user Ruben wrote:

Would love to see events stratified by age. There are almost 700 more people age > 65 in the biontech group as compared to moderna. With total hospitalizations being only 43 and icu only 7 patients for pfizer vaccine, it makes you wonder how much of this is skewed by age.

On 2020-11-17 00:14:37, user Laurence Renshaw wrote:

Apart from one sentence, this paper does not discuss deaths that are not directly attributable to the disease - for example, it does not appear to consider future deaths caused by the massive economic downturn as a result of people staying at home and businesses failing or downsizing.<br /> So how can it predict that people born in 2020 will expect to live 1 year less? People born in 2020 will certainly not die from Covid19, and the paper does not discuss anything else that could affect their life expectancy.<br /> Even for the over-65 group, how can a 0.1% population fatality rate (let's say that's 0.3 or 0.4% over over-65's) bring down their future life expectancy by several percent?<br /> This paper is very short on methods and data, and very long on conclusions.<br /> It also dismisses the impact of what it refers to as 'harvesting', and claims that few of the Covid19 deaths would have died soon - this contradicts all other studies that I have seen.<br /> It may well be that life expectancy, for those not killed by Covid19, will be reduced for decades to come, due to the economic and social impacts of the virus and our reactions to it (lockdowns and other restrictions), but deaths from the virus itself (a one-time loss of 0.1% of the population, with the vast majority over 70) can only have a tiny impact on life expectancy.

On 2020-04-21 23:28:15, user sammmy wrote:

Hilarious abuse of statistics. Patients were NOT randomized between different treatment groups. It is very possible that patients in a grave condition selected hydroxychloroquine as the only available drug and their higher death rate is attributed to their condition not the drug. Observational studies like this one do NOT prove cause and effect. I hope the people that were criticizing the other studies that they were 'not randomized and double blind' will apply that critique to this study as well.

On 2021-06-18 16:19:49, user Jim D wrote:

Hi, I have CLL, asymptomatic and very low count. I had the Pfizer vaccine, both doses. My arm was sore for 3 weeks after the first shot even tho I moved it around alot. On the third day after my second shot, it seemed that my shingles was reactivated, big pain, couldn't sleep. My PCP sent me to emergency for CT scan. Other than spike in white cell count, nothing showed from the scan. I shared the info about this study with PCP and Hemotology oncologist to try to get a quantitative antibody test. I have not heard back, other than those tests are extremely difficult to get. Also then with further reading, I learned that even with a quantitative test, I would not learn much since there is no standard for determining what number is safe.<br /> My question is: has there been any new info since this study was posted. Thanks. J

On 2020-08-04 19:58:40, user Marm Kilpatrick wrote:

Thank you for this study. Could you please report your raw results by age categories? Specifically, please indicate the sample size for each age range and the number that were seropositive. <br /> Could you also post the deaths by age of people in the study area?<br /> Thank you!<br /> marm

On 2020-08-25 08:12:24, user Bart Rijnders wrote:

The analysis is done with days after diagnosis (I presume the positive PCR this is) and not days since start of symptoms and the most important variable. This means that the diagnosis can thus be made somewhere between >14 days preceding hospital admission (if the test was done by a GP of testing venue) but can also be several days after hospital admission (e.g. when the first PCR is false negative but the second is positive)

Important bias may happen when patients who get tested easily / earlier on in the disease course while still outside the hospital also can get hospitalized easier (e.g. good health insurance). These patients will be overrepresented in the "treatment within 3 days after diagnosis" group. I do not see how this was (and can) be accounted for.

The study should therefore analyse the treatment effect in function of symptom duration at time of plasma transfusion as well and how this relates to a possible therapeutic effect of plasma. Hope this will be possible

On 2021-07-26 20:40:57, user Double_Up wrote:

So far, so good. No infectious agents added to the SARS-2 shot, that's a plus, and if Phase 3 goes as well this Medicago-GSK shot may be safe enough for many I know to take, with so many possibly having SARS-2 already but no way to prove it since tests are garbage and antibody tests are about 50% accurate at best. Safety over hype. People I work with cannot take any SARS-2 shots due to medical conditions they have but they're being treated like cattle, horrible.

On 2024-09-23 06:08:48, user Stuart Quan wrote:

This preprint is now published and is available in PubMed Central. The citation is:<br /> Singh V, Haynes PL, Quan SF. Assessing Depression and Suicidality Among Recently Unemployed Persons with Obstructive Sleep Apnea and Socioeconomic Inequality. Southwest J Pulm Crit Care Sleep. 2022 May;24(5):81-88. doi: 10.13175/swjpcc020-22. Epub 2022 May 16. PMID: 35702528; PMCID: PMC9190213.

On 2020-04-13 00:38:02, user Craig wrote:

The safety of HCQ alone has already been proven. It's the efficacy, both as prophylaxis and as treatment, that needs to be studied.

Studying HCQ in combination with a drug that is already known to have adverse cardiac effects seems like a study designed to produce data with a negative bias.

On 2020-04-11 17:21:49, user Francis Nunes wrote:

I fully agree that the title needs to be revised and provide more accurate information about the findings.

On 2020-04-11 12:00:55, user Sherlock Holmes wrote:

From an article in The Hill: "France has recorded 100 health incidents and four fatalities linked to experimental drugs for those with the coronavirus since late March..

France has has some 13.500 deaths of coronavirus. Is someone doing the maths?

On 2021-03-10 15:41:21, user Theodore Petrou wrote:

Thank you very much for this study. I have a concern regarding your calendar adjusted calculation.

You report the overall IR of the unvaccinated LTCF to be 0.46. Looking at the VEca, I calculated the IRca for the unvaccinated to be 0.39, 0.23, 0.19, 0.05. Not one is above 0.46, the overall rate. How is this possible? Can you provide your code used to calculate VEca?

Also, I would have liked to see the all-cause death rate in unvaccinated vs vaccinated group.

Thank you,<br /> Ted Petrou

On 2021-04-06 17:16:29, user Rick Clem wrote:

I was infected in December along with my whole family. Loss of smell and<br /> a little lethargy was all we experienced. I have wondered if our luck <br /> was attributed to low loading factor or other. So I wonder on the <br /> degree of anitbody presence I attained from the infection. I received <br /> my 1st Moderna shot three weeks ago. Hit me like a freight train after <br /> 10 hours. Extreme fatigue, some headache. My thought is now directed <br /> to skipping my 2nd shot. Reading in the current studies on the <br /> necessity of a second shot, I hope they consider intensity of the <br /> previous infection in their studies. It would help folks like me to <br /> make a more informed decision on whether or not to ignore Fauci and the <br /> CDC's generalisms on needing a second shot.

On 2021-05-04 02:06:41, user Uri Kartoun wrote:

Ref 9 actually does rely on combining structured and unstructured data elements. The paper is one of the earliest to identify NAFLD patients using EMRs - indeed it is limited, but I wouldn't write "fail to provide the full clinical picture of NAFLD" because it is not true.

On 2021-11-03 00:28:57, user Josh wrote:

65% chance of ending up with at least one symptom of long Covid, and still rising significantly 6 months out? That seems extremely high. Is there a reason why the risk of developing long Covid appears so much higher in your study than in others, many of which report numbers in the 30-35% range? Also, is there a reason why your study shows virtually no reduction in the chances of developing long Covid in breakthrough vs. non-breakthrough cases, whereas an earlier British study shows a roughly 50% reduction?

On 2022-02-26 15:01:29, user Rogerblack wrote:

The paper investigates symptoms remaining after 12 weeks. 15 weeks ago, 11M/46M of the UK population was boosted. (3 weeks for immune response and paperwork)

While perhaps smaller numbers than we might like, this would lead to approximately 63 (294(2-dose)*0.22) boosted in your cohort developing LC, if there is no change in risk from doubly vaxxed.<br /> This is not good enough to show if the protection from LC is slightly increased, but it is certainly enough to exclude an OR of (say) 0.1 or 0.2, which would be valulable to report.

It is my understanding that the CIS also contains questions related to employment/role.

What fraction of those reporting activity limitations have had changes on those metrics?<br /> An impatient advocate.

On 2021-11-09 07:26:10, user Željko Serdar wrote:

Janssen<br /> March, 92%<br /> August, 3%

Moderna<br /> March, 91%<br /> August, 64%

Pfizer<br /> March, 95%<br /> August, 50%

On 2021-05-27 17:47:20, user Right Warrior wrote:

This hasn't been subject to peer review yet. Why are we supposed to take this at face value?

On 2020-04-12 22:24:36, user Eric Francis Coppolino wrote:

Pavel it would seem this is more like head counts, not a formal study. But it would certainly be easy enough to do the numbers. One problem is that this is all moving so fast that cause of death is not being ascertained, and my understanding is that even on a good day, it's up to the attending physician to scribble something on the line. Now we really need to know. And it's unlikely that we ever will.

On 2020-04-13 08:45:15, user Alberto Villena wrote:

Please, have a look to this preprint: Lianne Abrahams, 2020: Covid-19: acquired acute porphyria hypothesis. https://osf.io/4wkfy/<br /> "Macaques infected with SARS-CoV-2 also have<br /> decreased red blood cell numbers (Munster 2020) and susceptibility to<br /> SARS-CoV-2 appears to be determined by blood group; blood group A is most<br /> affected whereas blood group O seems to be protected (Yang 2020). This finding<br /> is concordant with previous studies showing that susceptibility to the 2003<br /> strain of SARS-CoV was determined by blood group (Guillon 2008)."

On 2025-04-28 10:51:23, user Hazel A Smith wrote:

This immunisation programme is also delivered in Children’s Health Ireland (CHI) Temple Street and Crumlin. I appreciate that many of these neonates will be high risk.

With hospitalization, these would be ward, NICU (CHI at Crumlin) and PICUs combined? Where (setting within hospital) was the effect seen? As the overall lower rate may, or may not, mask a continued high RSV admission rates in CHI at Crumlin's NICU and PICUs. Also, the NICU in Crumlin is not that long established so previously all ICU admissions were to PICU.

I am struggling to read the figures (but this is my limitation with stats and I cant expand the images which is just how the preview is displayed) for age specific admissions so I can't tell which age group benefited the most. We know that the younger the infant the more likely it is to be not only a ward admission but a PICU admission.

Can you look at duration of hospitalization? As it could be that even if admitted you stayed for a shorter duration. Also, could be that if admitted it was to the ward and not PICU as would have previously happened (especially for those two months old or younger)?

With the data HSE has to hand, are there any concerns about how neonates/ infants of mothers in Northern Ireland (so vaccine is given in pregnancy) but are cared for in a RoI hospital will be managed? This could be that numbers are so small that there is no effect (and the time it would take to clean is not of value).

If 532 RSV hospitalisations were averted than how many operations etc were not cancelled (compared to previous years) due to reduced pressure on beds or did the flu figures for the last winter replace the 532 hospitalisations?

On 2020-03-25 14:52:42, user Arturo Tozzi cns wrote:

Here you are a more focused writing on the issue of COVIFD-19/ mandatory childhood vaccinations: https://science.sciencemag....

On 2021-07-29 08:14:23, user Alenita Luz Mateo wrote:

Thank you for this very informative research, my mom is currently on dialysis and suffering from comorbidities. As of today 7/29/31, we are on the MECQ status here due to a surge of covid19 cases, with delta variant on the loose. I think our health care system is breaking, from 5:18 nurse to patient ratio it now down to 3:18. Our health care providers are already contracting the virus, are now exhausted. I'm afraid they cannot function well anymore to do their duties for their patients. Yesterday another EKD patient died and contracted the covid19. We are a third world country and access to materials needed are very crucial. I hope we can survive this pandemic.

On 2022-02-22 12:56:40, user Dennis Fantoni wrote:

How many of the 1.8 million were tested twice in the 20 to 60 window?

As I understand it, Danish authorities urges people not to get tested again when they recently have had an infection (https://coronasmitte.dk/emn... "https://coronasmitte.dk/emner/smittefri)"), except if they have covid-19 symptoms, so perhaps the 187 is out of a smaller pool than 1.8 million, esp. if the secondary infection is mild enough to not alert the person to think it might be covid19 again.

so... it would be very nice to know how big the pool of people who has been tested twice in the 20 to 60 days window is?

On 2020-04-14 05:46:51, user Alexander wrote:

The same results: d-dimer>2500 (OR 6.9, 95% CI, 3.2-15.2), ferritin >2500 (OR 6.9, 95% CI, 3.2-15.2). Is it correct?

On 2021-06-25 10:48:30, user ScottK wrote:

Since this was an observational study with a small number of survivors, the fear is that you draw conclusions that are tied to survival and not to treatments. Simply put...<br /> MDs won't prescribe HCQ/Zithro to certain risk pools.<br /> Cumulative dose may rise with survival, not the other way around.<br /> You've got 15% of the total population in the study on this regimen and 20% of the population survived. If the MDs picked 10-15 of the 'least contraindicated' patients to do anything with, chances are that you'd see higher survival.

Double blind, controlled or explain the heck out of the pooling and selection criteria.

On 2020-12-11 00:07:54, user Peter Novák wrote:

PARTICIPANT CONSENT?

Authors claim, cite: "All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived."

I find this proclamation highly dubious.

I'm not sure how many of the the mass tested people have signed a form of informed consent, and I'd like to see how would the authors prove that. I personally have asked several participants and they insist they did not sign anything. But even in the case some, or even the majority signed something, what weight would that have under circumstances?

The people subjected to the mass testing (that technically means biological material extraction) with consideration, that those who do not subject, will be quarantined for week or two - that means, forced to stay home under threat of penalty as high as 1650 EUR (average monthly income in Slovakia is 1100 EUR brut), with few exceptions (e.q. nearest grocery store, drugstore and necessary health care), but certainly denied the access to workplace with no lost worktime salary compensation at all, neither from state nor employer.[1] A proposition effectively resembling a home prison in my opinion, and what's even worse in situation of economical crisis - with published threats from some employers that untested employees may lose their job eventually, thus undermining the public confidence in freedom of choice furthermore.[2]

It is known that criminal complaints on the accounts of possible coercion, health care law violations, human rights violations etc have been filled to the public prosecutor office. These are yet to be resolved.<br /> I acknowledge that some of the people have attended the testing voluntarily indeed, probably a minority though, as indicated by low compliance (15%) to the third round of testing where quarantine threat was removed.

Nevertheless, I doubt anyone could assume the actions under such circumstances constitute a "participant consent" by standards of any possibly existing ethical guidelines.

Or maybe I just read the citation wrong and the authors did not mean the 3,6 million people undergoing the biologic material extraction to be the subject of the "necessary patient/participant consent"...?

[1] Public Health Office Edict No. 16 from 30.10.2020. Government Bulletin vol. 30 no. 12. http://www.minv.sk/swift_da...

[2] Dobrovolne nasilu? Niektorým ludom bez testovania hrozia výpovedou. Pravda, 26.10.2020. https://ekonomika.pravda.sk...

On 2020-05-15 16:48:46, user Will Wiegman wrote:

A combo of severe Thiocyanate and Iodine Deficiencies shuts down the pitting function of the spleen making it impossible for the body to eliminate the viruses trapped inside of mature red blood cells with no nucleus for the virus to use to replicate.

https://www.ncbi.nlm.nih.go...

https://pubmed.ncbi.nlm.nih...

Paragraph 4:<br /> https://pubmed.ncbi.nlm.nih...

On 2020-05-15 18:42:04, user Will Wiegman wrote:

Undiagnosed Borreliosis + Covid-19 + severe Thiocyanate and Iodine Deficiencies = respiratory failure

link.springer.com<br /> Expression of ICAM-1, ICAM-2, NCAM-1 and VCAM-1 by human synovial cells exposed to Borrelia burgdorferi in vitro<br /> Sunit K Singh, Verena Baar, Henner Morbach, Hermann J Girschick<br /> Rheumatology international 26 (9), 818-827, 2006<br /> The interaction of resident tissue cells with migratory inflammatory cells is essential for the recruitment of immune effector cells to inflammatory sites. The sustained expression of adhesion molecules in the synovium of patients with chronic Lyme arthritis seems to contribute to this chronic inflammation. Whether cell adhesion molecules influence the early steps of Borreliosis is unclear. Therefore, we examined the expression of ICAM-1, ICAM-2, VCAM-1 and NCAM-1 in synovial cells exposed to two different Borrelia burgdorferi sensu stricto strains Geho and B31. The mRNA expression of ICAM-1, ICAM-2, VCAM-1 and NCAM-1 was not changed in synovial cells exposed to B31. Whereas ICAM-2 and VCAM-1 was upregulated, NCAM-1 mRNA was downregulated and ICAM-1 mRNA was unchanged by strain Geho. The ICAM-1 protein expression on the synovial cell surface was downregulated by both strains. Differential regulation of adhesion molecule mRNA, and subsequent high turnover or elevated shedding from the cell membrane may contribute to early pathogenesis in Lyme arthritis.<br /> View at link.springer.com<br /> [PDF] researchgate.net<br /> Cited by 9<br /> Related articles<br /> All 12 versions

Borrelia burgdorferi activates nuclear factor-kappa B and is a potent inducer of chemokine and adhesion molecule gene expression in endothelial cells and fibroblasts.<br /> Klaus Ebnet, Keith D Brown, Ulrich K Siebenlist, Markus M Simon, Stephen Shaw<br /> The Journal of Immunology 158 (7), 3285-3292, 1997

On 2021-08-10 14:38:31, user Jessa Monterde Arabia wrote:

May I request for the questionnaire we are also conducting the same study and it would be a big help. Thank you