(1/6) Most drugs are not effective at treating #COVID, and do not address the root-cause symptoms. #Remdesivir is one such example—the drug mimics #RNA, and gets incorporated into the viral RNA transcripts preferentially versus human RNA transcripts.
(2/6) Mechanistically, this drug was promsising. However, in today’s @TheLancet paper, "#Remdesivir in adults with severe #COVID-19: a randomised, double-blind, placebo-controlled, multicentre trial," findings are inconclusive at best, and damning at worst:
(3/6) 1) The study enrolled already-hospitalized patients with pneumonia, with <94% oxygen saturation, who were already hospitalized 2) They measured clinical improvement for 28 days 3) "#Remdesivir use was not associated with a difference in time to clinical improvement"
(4/6) 4) 66% of #remdesivir patients experienced adverse events, versus 64% of placebo patients; 12% of remdesivir patients had to stop early because of adverse events vs. 5% for placebo patients
(5/6) The very authors conclude that “#remdesivir was not associated with statistically significant clinical benefits. However, the numerical reduction in time to clinical improvement in those treated earlier requires confirmation in larger studies.”
(6/6) Anthony Fauci, head of #NIAID at the @NIH is saying that there is "clear-cut" evidence that the drug works.
Is this a market pump-and-dump and to prop up @GileadSciences shares and give false hope in the economy being stable, while people make off with trillions of $$$?
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Bacterial immunity to viruses is incredible. We have so much more to learn and unlock to master not only genome engineering with CRISPR-Cas systems, but also the next generation of gene editing and modulating tools as well as replicating adaptive, genomic immunity.
What is bacterial adaptive immunity? When phages (viruses that infect bacteria) insert their genetic materials into bacteria, bacteria use a host of evolved genetic immunity techniques to fend off invaders.
In animals, our adaptive immune systems rely primarily on B cells to generate antibodies and T cells to generate T cell receptors that continuously evolve to bind to and neutralize invaders such as viruses, bacteria, fungi and parasites.
SARS-BLOCK peptides docking with the ACE2 receptor
Using the original SARS-CoV-1 structure and the new sequence of SARS-CoV-2, we were able to design and simulate efficacious peptide inhibitors of SARS-CoV-2 binding to the ACE2 receptor in 5 hours in February of 2020. These peptides effectively block the virus that causes COVID.
These sorts of approaches can be applied to far more than infectious disease. @Ligandal has developed a broad approach for simulating and designing synthetic peptides that can bind to virtually any class of surface markers, with many applications.
There are many companies that have various gene editing materials, and a small subset of companies that specialize in delivery of the gene editing, gene-reprogramming instructions.
In order to create the next generation of precision genetic medicine, a number of approaches will have to be used to engineer cells, tissue and organs to be free of disease states or programmed to carry out specific functions, such as killing cancer cells.
How does light exposure affect circadian rhythms and sleep cycles? This review article provides an analysis of many studies exploring the effects of different wavelengths and durations of light exposure on sleep. I was curious about blue vs. red-orange:
“Appleman et al. (2013) conducted a 12-day study where 21 subjects, randomly divided into two groups, received either short-wavelength (blue, peak 476 nm) light for 2h in the morning and light filtered (<535 nm) with orange-tinted glasses for 3h in the evening (advance group)…”
“…or at opposite times, that is orange-tinted glasses in the morning and blue light in the evening (delay group). Subjects kept their normal schedule for the first 5d and received morning and evening light exposures the following 7d in addition to a fixed sleep schedule…”
…and why it is so important to understand with pandemics…
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Some basic math on exponents:
y = x(r^n)
d = x(r^n)/c
If r is # of people infected per case (R0)
x is starting # of cases
n is number of exponential infection increases
1/c is fraction of cases leading to death
Then y is cases at time point n and d is deaths at time point n.
People must understand that cases increasing exponentially more (e.g. if r is twice as high between omicron and delta / wildtype) means that half, quarter or even 1/10th lethality per case will yield more deaths than if this exponent isn’t in place.