The now retracted paper halted hydroxychloroquine trials. Studies like this determine how people live or die tomorrow
The Lancet has made one of the biggest retractions in modern history. How could this happen?
by Dr James Heathers
This article is mostly abt possible flaws in peer review. The article is an apology for the Lancet's mistake in publishing bad science
A mysterious company's coronavirus papers in top medical journals may be unraveling
Scientists and journals express concern over influential studies of COVID-19 patient data that evaluated possible treatments such as hydroxychloroquine
Science 2 Jun 2020
Reuters August 1, 2016 GlaxoSmithKline and Google parent Alphabet's life sciences unit are creating a new company focused on fighting diseases by targeting electrical signals in the body, jump-starting a novel field of medicine called bioelectronics.
On October 3, 2016, the Nobel Prize in Physiology or Medicine was awarded to Yoshinori Ohsumi for “discoveries of the mechanisms for autophagy.” Just a few weeks earlier, at an acceptance speech for the 2016 Paul Janssen Award, Yoshinori Ohsumi stated that although he performs research in a simple organism—baker’s yeast—he always hoped his research would have an impact upon human health.
(ur abstract för https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5240711/)
«Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort the structures of around 100,000 unique proteins have been determined, but this represents a small fraction of the billions of known protein sequences. Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’—has been an important open research problem for more than 50 years. Despite recent progress existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14), demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm.»