Trump was administered REGN-COV2 by compassionate use request. "...this type of compassionate use..is..intended for patients with serious or life-threatening conditions who do not have any viable or available treatment options..." investor.regeneron.com/static-files/f…
The only published evidence on effectiveness of REGN-COV2 so far is a preprint that showed "REGN-COV2 reduced the amount of virus and associated damage in the lungs of non-human primates." It was tested in rhsesus macaques and hamsters. biorxiv.org/content/10.110…
A recent descriptive analysis of the ongoing trial shows "..a 0.51 log10 copies/mL greater reduction (p=0.0049) in patients treated with high dose, & a 0.23 log10 copies/mL greater reduction (p= 0.20) in patients treated with low dose, compared to placebo" investor.regeneron.com/news-releases/…
"Among the first 275 patients, approximately 56% were Hispanic, 13% were African American and 64% had one or more underlying risk factors for severe COVID-19, including obesity (more than 40%). On average, patients were 44 years of age."
Here's what @EricTopol has to say about REGN-COV2: "There’s nothing bad about these results, you just can’t say much about how transformative this is going to be." He warned that the data so far should not be enough for the @US_FDA to grant an EUA. statnews.com/2020/09/29/reg…
Meanwhile... $REGN
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Arguably the most boring step in genomics is the first one: normalization. Settled science. Scale + log. Move on.
Except that here's been a huge blind spot in the field. And it matters for AIxBio. A 🧵about what I think may be one of the most important papers I've written. 1/
The standard normalization is log(x/s*K+1) w/ K=10,000 in Seurat and Scanpy. It's been used in hundreds of thousands of studies. AI agents nowadays run it routinely.
In an expansive benchmark in @naturemethods, Ahlmann-Eltze & Huber conclude it's pretty much best in class. 2/
Except it isn't. Not even close. In a project that is four years in the making, we show that another transformation massively outperforms existing methods on the Ahlmann-Eltze & Huber benchmarks (red dots below). Moreover, it's optimal. What is this new method? How can it be? 3/
So this plagiarism thing has happened to our lab.. again. This time it's plagiarism of our poseidon syringe pump paper @booeshaghi et al., 2019 in @SciReports:
Text has been plagiarized, as well as figures copied directly here: 1/🧵nature.com/articles/s4159… ijirset.com/upload/2024/ma…
Here is figure 1 from our paper (LHS) and figure 1 in the plagiarized paper (RHS) published in the "International Journal of Innovative Research" 2/ ijirset.com/upload/2024/ma…
The text seems to have been rewritten with an LLM. Our introduction (LHS) vs. the plagiarized version (RHS): 3/
I've checked this paper out, as instructed. I was also interested in the main result for personal reasons: I'm 51 years old. Is it true that I've just gone through a major change? And that another one awaits me in just a few years?
The main result about major changes in the mid 40s and 60s is shown in this plot (Fig. 4a). First, I redrew it with axes that start at 0, so the scale of change here was clearer. Not as impressive, but maybe it's a thing? 2/
The authors say that this finding is even corroborated in another study (ref 14). But that's not true. I looked it up, and it shows something totally different (see RHS Fig 3c from ref 14). No change in mid 40s, but a change in the mid 30s, and the real change in the 80s 😕 3/
I recently posted on @bound_to_love's work quantifying long-read RNA-seq. In response, a scientist acting in bad faith (Rob Patro @nomad421) trashed our work. This kind of mold in science's bathroom is extremely damaging so here's a bit of bleach. 1/🧵
At issue are benchmarking results we performed comparing our tool, lr-kallisto, to other programs including Patro's Oarfish. Shortly after we posted our preprint Patro started subtweeting our work, claiming we'd run an "appallingly wrong benchmark" and that we're "bullies". 2/
This was followed, within days, by Patro posting a hastily written preprint disguised as research work on benchmarking, but really just misusing @biorxivpreprint to broadcast the lie that our work "... may be repeatable, but it appears neither replicable nor reproducible." 3/