1/ The MHRA has approved a longer gap between doses for both the AstraZeneca vaccine and the Pfizer vaccine. The latter has concerned some people. Specifically many are citing a figure of 52% for protection after the first dose.
Here is why this 52% figure isn't useful [1/n]
2/ The 52% value is a real figure, which comes from the Pfizer trial, for the period between the first and second doses. Here is what that period of time looked like (fda.gov/media/144325/d…):
In red are people who received placebo and in blue are those who received the vaccine
3/ We can see that until day 3 we have near identical results in both groups. This is *expected* - no vaccine has an effect until days later when the immune system has had time to develop a response against it.
4/ As an immune response builds the lines slowly diverge from each other. By day 10 they have completely different trajectories. We do have to be careful here, analysing the data post-hoc. But it is clear that (as one expects a priori) full protection isn't realised immediately.
5/ *The 52% figure is the average protection over these 21 days*, so it includes that initial time before the immune system has had time to create a response.
6/ If one instead looks at the day 10 to day 22 period one instead gets an efficacy value of 86% (there will be confidence intervals around that).
7/ Whereas if one looks at the day 0 to day 10 period there is an efficacy of 10%.
8/ The 52% figure is a mush of those two completely different scenarios. It's not useful. People shouldn't be citing it in this context.
9/ The Moderna vaccine documents (similar vaccine class) actually break down the results after the first dose into 14 day periods and show a very similar effect fda.gov/media/144434/d…
10/ There's lots to reasonably discuss about single-dosing. The big question is what the efficacy against severe COVID is in days 21-90. But please stop using this 52% figure.
📄 📄 Our molnupiravir work is now out after peer-review! We definitively demonstrate that molnupiravir has resulted in viable SARS-CoV-2 viruses with significant numbers of mutations, in some cases with onwards transmission of mutated viruses. nature.com/articles/s4158…
Molnupiravir is an antiviral drug. It works by creating mutations in the virus genome. Since many of these mutations will stop virus proteins from working correctly, molnupiravir can reduce viral load.
Molnupiravir creates mutations because of its chemistry. Its structure is like that of a base of RNA, but can exist in two forms. One looks like a C and so binds to G, but it can then switch to another form that binds to A. This means it causes mainly G→A and C→T mutations
We conclusively demonstrate that mutational events caused by molnupiravir treatment can be seen in globally sequenced SARS-CoV-2 genomes, in some cases with onwards transmission.
🧵 medrxiv.org/content/10.110…
Molnupiravir is a drug used to treat COVID-19. Tens of millions of doses have been sold, although recent studies have case doubt on its effectiveness. It works by creating mutations in the virus as it replicates.
Many of these mutations will be so major as to make the resulting virus non-viable, and so molnupiravir treatment can reduce the viral load. But there have also been concerns raised that it might accelerate viral evolution.
Malaria parasites replicate asexually in the bloodstream, however a subset form ♂️ and ♀️ sexual cell types that when sucked up by a 🦟 undergo sexual reproduction. But how?
🧵
We already knew that a single transcription factor, called AP2-G, is necessary and sufficient to move asexual cells onto a sexual developmental pathway. But why and how do these cells decide whether to become male and female, in the absence of – for example – sex chromosomes?
We investigated this with a large scale genetic screen, combined with single-cell RNA sequencing.
Anti-nucleocapsid seropositivity (i.e. antibodies that can be acquired only from infection) has gone up from 25% at the start of the year to almost 70% now.
(This underestimates cumulative infection due to waning, and some people not producing measurable anti-N antibodies.)
in my continuing quest to describe SARS-CoV-2 sequencing artifacts.. nt:nt:A14960T / ORF1b:N498I / ORF1ab:N4899I looks like it is caused by a primer artifact that affects a small number of sequencing sites sometimes.
Looking at the reads it looks like it can sort of be explained by a homodimer, (although I know the mechanism below doesn't quite work)
Should be resolved by checking all reads start at a known primer binding site
Today's ONS Infection Survey antibody data breaks up people who are positive into those who have lower (>42 ng/ml) and higher (>179 ng/ml) levels of antibodies, and reveals the effect of boosting
A) It's great that they've been agile and are providing this analysis
B) It shows to an extent a limitation of the public data previously available: a large set of continuous datapoints revealing *levels* of antibodies, and how they vary across a population have until now
been binarised simply into "positive/negative" in any public release (I think?). And now this continuous data is only being broken down into three categories. It would be really valuable to have more of this data in the public domain