Goto

Collaborating Authors

Experimental Study


ROBOTS swim in schools 'school' to save energy, study finds

Daily Mail - Science & tech

It has been known for centuries that many fish swim in schools, with large groups moving in unison. But scientists have never fully known why, and have been unable to prove how this behaviour benefits them. Now, European researchers have used robotic fish to show it it is up to 13.5 per cent more efficient for fish to swim in a group than alone, allowing them to save energy. This had been the long-standing theory, but it had never been conclusively proven. European researchers used robotic fish to prove it is because they allow fish to save energy and up to 13.5 per cent more efficient than swimming alone Scientists from the Max Planck Institute of Animal Behavior (MPI-AB), the University of Konstanz, and Peking University, set about building a lifelike robot to test the hypothesis.


Machine Learning Helped Predict Short-Term Cancer Mortality – Cancer Therapy Advisor – IAM Network

#artificialintelligence

Researchers have validated a machine-learning algorithm that was integrated into an electronic health record to generate real-time, accurate predictions of the short-term mortality risk for patients with cancer, according to a recent study. Additionally, this machine-learning algorithm outperformed other prognostic indices. "Such an automated tool may complement clinician intuition and lead to improved targeting of supportive care interventions for high-risk patients with cancer," the researchers wrote. The prospective study included 24,582 patients with outpatient oncology encounters from March 2019 to April 2019. Encounters occurred at 1 tertiary and 17 general oncology practices.


Essential data science skills that no one talks about.

#artificialintelligence

The top results are long lists of technical terms, named hard skills. Python, algebra, statistics, and SQL are some of the most popular ones. Later, there come soft skills -- communication, business acumen, team player, etc. Let's pretend that you are a super-human possessing all the above abilities. You code from the age of five, you are a Kaggle grandmaster and your conference papers are guaranteed to get a best-paper award. There is still a very high chance that your projects struggle to reach maturity and become full-fledged commercial products. Recent studies estimate that more than 85% of data science projects fail to reach production. The studies provide numerous reasons for the failures.


AI Assesses Alzheimer's Risk by Analyzing Word Usage

#artificialintelligence

Artificial intelligence could soon help screen for Alzheimer's disease by analyzing writing. A team from IBM and Pfizer says it has trained AI models to spot early signs of the notoriously stealthy illness by looking at linguistic patterns in word usage. Other researchers have already trained various models to look for signs of cognitive impairments, including Alzheimer's, by using different types of data, such as brain scans and clinical test results. But the latest work stands out because it used historical information from the multigenerational Framingham Heart Study, which has been tracking the health of more than 14,000 people from three generations since 1948. If the new models' ability to pick up trends in such data holds up in forward-looking studies of bigger and more diverse populations, researchers say they could predict the development of Alzheimer's a number of years before symptoms become severe enough for typical diagnostic methods to pick up.


Autoantibodies against type I IFNs in patients with life-threatening COVID-19

Science

The immune system is complex and involves many genes, including those that encode cytokines known as interferons (IFNs). Individuals that lack specific IFNs can be more susceptible to infectious diseases. Furthermore, the autoantibody system dampens IFN response to prevent damage from pathogen-induced inflammation. Two studies now examine the likelihood that genetics affects the risk of severe coronavirus disease 2019 (COVID-19) through components of this system (see the Perspective by Beck and Aksentijevich). Q. Zhang et al. used a candidate gene approach and identified patients with severe COVID-19 who have mutations in genes involved in the regulation of type I and III IFN immunity. They found enrichment of these genes in patients and conclude that genetics may determine the clinical course of the infection. Bastard et al. identified individuals with high titers of neutralizing autoantibodies against type I IFN-α2 and IFN-ω in about 10% of patients with severe COVID-19 pneumonia. These autoantibodies were not found either in infected people who were asymptomatic or had milder phenotype or in healthy individuals. Together, these studies identify a means by which individuals at highest risk of life-threatening COVID-19 can be identified. Science , this issue p. [eabd4570][1], p. [eabd4585][2]; see also p. [404][3] ### INTRODUCTION Interindividual clinical variability is vast in humans infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), ranging from silent infection to rapid death. Three risk factors for life-threatening coronavirus disease 2019 (COVID-19) pneumonia have been identified—being male, being elderly, or having other medical conditions—but these risk factors cannot explain why critical disease remains relatively rare in any given epidemiological group. Given the rising toll of the COVID-19 pandemic in terms of morbidity and mortality, understanding the causes and mechanisms of life-threatening COVID-19 is crucial. ### RATIONALE B cell autoimmune infectious phenocopies of three inborn errors of cytokine immunity exist, in which neutralizing autoantibodies (auto-Abs) against interferon-γ (IFN-γ) (mycobacterial disease), interleukin-6 (IL-6) (staphylococcal disease), and IL-17A and IL-17F (mucocutaneous candidiasis) mimic the clinical phenotypes of germline mutations of the genes that encode the corresponding cytokines or receptors. Human inborn errors of type I IFNs underlie severe viral respiratory diseases. Neutralizing auto-Abs against type I IFNs, which have been found in patients with a few underlying noninfectious conditions, have not been unequivocally shown to underlie severe viral infections. While searching for inborn errors of type I IFN immunity in patients with life-threatening COVID-19 pneumonia, we also tested the hypothesis that neutralizing auto-Abs against type I IFNs may underlie critical COVID-19. We searched for auto-Abs against type I IFNs in 987 patients hospitalized for life-threatening COVID-19 pneumonia, 663 asymptomatic or mildly affected individuals infected with SARS-CoV-2, and 1227 healthy controls from whom samples were collected before the COVID-19 pandemic. ### RESULTS At least 101 of 987 patients (10.2%) with life-threatening COVID-19 pneumonia had neutralizing immunoglobulin G (IgG) auto-Abs against IFN-ω (13 patients), against the 13 types of IFN-α (36), or against both (52) at the onset of critical disease; a few also had auto-Abs against the other three individual type I IFNs. These auto-Abs neutralize high concentrations of the corresponding type I IFNs, including their ability to block SARS-CoV-2 infection in vitro. Moreover, all of the patients tested had low or undetectable serum IFN-α levels during acute disease. These auto-Abs were present before infection in the patients tested and were absent from 663 individuals with asymptomatic or mild SARS-CoV-2 infection ( P < 10−16). They were present in only 4 of 1227 (0.33%) healthy individuals ( P < 10−16) before the pandemic. The patients with auto-Abs were 25 to 87 years old (half were over 65) and of various ancestries. Notably, 95 of the 101 patients with auto-Abs were men (94%). ### CONCLUSION A B cell autoimmune phenocopy of inborn errors of type I IFN immunity accounts for life-threatening COVID-19 pneumonia in at least 2.6% of women and 12.5% of men. In these patients, adaptive autoimmunity impairs innate and intrinsic antiviral immunity. These findings provide a first explanation for the excess of men among patients with life-threatening COVID-19 and the increase in risk with age. They also provide a means of identifying individuals at risk of developing life-threatening COVID-19 and ensuring their enrolment in vaccine trials. Finally, they pave the way for prevention and treatment, including plasmapheresis, plasmablast depletion, and recombinant type I IFNs not targeted by the auto-Abs (e.g., IFN-β). ![Figure][4] Neutralizing auto-Abs to type I IFNs underlie life-threatening COVID-19 pneumonia. We tested the hypothesis that neutralizing auto-Abs against type I IFNs may underlie critical COVID-19 by impairing the binding of type I IFNs to their receptor and the activation of the downstream responsive pathway. Neutralizing auto-Abs are represented in red, and type I IFNs are represented in blue. In these patients, adaptive autoimmunity impairs innate and intrinsic antiviral immunity. ISGs, IFN-stimulated genes; TLR, Toll-like receptor; IFNAR, IFN-α/β receptor; pSTAT, phosphorylated signal transducers and activators of transcription; IRF, interferon regulatory factor. Interindividual clinical variability in the course of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is vast. We report that at least 101 of 987 patients with life-threatening coronavirus disease 2019 (COVID-19) pneumonia had neutralizing immunoglobulin G (IgG) autoantibodies (auto-Abs) against interferon-ω (IFN-ω) (13 patients), against the 13 types of IFN-α (36), or against both (52) at the onset of critical disease; a few also had auto-Abs against the other three type I IFNs. The auto-Abs neutralize the ability of the corresponding type I IFNs to block SARS-CoV-2 infection in vitro. These auto-Abs were not found in 663 individuals with asymptomatic or mild SARS-CoV-2 infection and were present in only 4 of 1227 healthy individuals. Patients with auto-Abs were aged 25 to 87 years and 95 of the 101 were men. A B cell autoimmune phenocopy of inborn errors of type I IFN immunity accounts for life-threatening COVID-19 pneumonia in at least 2.6% of women and 12.5% of men. [1]: /lookup/doi/10.1126/science.abd4570 [2]: /lookup/doi/10.1126/science.abd4585 [3]: /lookup/doi/10.1126/science.abe7591 [4]: pending:yes


Inborn errors of type I IFN immunity in patients with life-threatening COVID-19

Science

The immune system is complex and involves many genes, including those that encode cytokines known as interferons (IFNs). Individuals that lack specific IFNs can be more susceptible to infectious diseases. Furthermore, the autoantibody system dampens IFN response to prevent damage from pathogen-induced inflammation. Two studies now examine the likelihood that genetics affects the risk of severe coronavirus disease 2019 (COVID-19) through components of this system (see the Perspective by Beck and Aksentijevich). Q. Zhang et al. used a candidate gene approach and identified patients with severe COVID-19 who have mutations in genes involved in the regulation of type I and III IFN immunity. They found enrichment of these genes in patients and conclude that genetics may determine the clinical course of the infection. Bastard et al. identified individuals with high titers of neutralizing autoantibodies against type I IFN-α2 and IFN-ω in about 10% of patients with severe COVID-19 pneumonia. These autoantibodies were not found either in infected people who were asymptomatic or had milder phenotype or in healthy individuals. Together, these studies identify a means by which individuals at highest risk of life-threatening COVID-19 can be identified. Science , this issue p. [eabd4570][1], p. [eabd4585][2]; see also p. [404][3] ### INTRODUCTION Clinical outcomes of human severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection range from silent infection to lethal coronavirus disease 2019 (COVID-19). Epidemiological studies have identified three risk factors for severe disease: being male, being elderly, and having other medical conditions. However, interindividual clinical variability remains huge in each demographic category. Discovering the root cause and detailed molecular, cellular, and tissue- and body-level mechanisms underlying life-threatening COVID-19 is of the utmost biological and medical importance. ### RATIONALE We established the COVID Human Genetic Effort ([www.covidhge.com][4]) to test the general hypothesis that life-threatening COVID-19 in some or most patients may be caused by monogenic inborn errors of immunity to SARS-CoV-2 with incomplete or complete penetrance. We sequenced the exome or genome of 659 patients of various ancestries with life-threatening COVID-19 pneumonia and 534 subjects with asymptomatic or benign infection. We tested the specific hypothesis that inborn errors of Toll-like receptor 3 (TLR3)– and interferon regulatory factor 7 (IRF7)–dependent type I interferon (IFN) immunity that underlie life-threatening influenza pneumonia also underlie life-threatening COVID-19 pneumonia. We considered three loci identified as mutated in patients with life-threatening influenza: TLR3 , IRF7 , and IRF9 . We also considered 10 loci mutated in patients with other viral illnesses but directly connected to the three core genes conferring influenza susceptibility: TICAM1/TRIF , UNC93B1 , TRAF3 , TBK1 , IRF3 , and NEMO/IKBKG from the TLR3-dependent type I IFN induction pathway, and IFNAR1 , IFNAR2 , STAT1 , and STAT2 from the IRF7- and IRF9-dependent type I IFN amplification pathway. Finally, we considered various modes of inheritance at these 13 loci. ### RESULTS We found an enrichment in variants predicted to be loss-of-function (pLOF), with a minor allele frequency <0.001, at the 13 candidate loci in the 659 patients with life-threatening COVID-19 pneumonia relative to the 534 subjects with asymptomatic or benign infection ( P = 0.01). Experimental tests for all 118 rare nonsynonymous variants (including both pLOF and other variants) of these 13 genes found in patients with critical disease identified 23 patients (3.5%), aged 17 to 77 years, carrying 24 deleterious variants of eight genes. These variants underlie autosomal-recessive (AR) deficiencies ( IRF7 and IFNAR1 ) and autosomal-dominant (AD) deficiencies ( TLR3 , UNC93B1 , TICAM1 , TBK1 , IRF3 , IRF7 , IFNAR1 , and IFNAR2 ) in four and 19 patients, respectively. These patients had never been hospitalized for other life-threatening viral illness. Plasmacytoid dendritic cells from IRF7-deficient patients produced no type I IFN on infection with SARS-CoV-2, and TLR3−/−, TLR3+/−, IRF7−/−, and IFNAR1−/− fibroblasts were susceptible to SARS-CoV-2 infection in vitro. ### CONCLUSION At least 3.5% of patients with life-threatening COVID-19 pneumonia had known (AR IRF7 and IFNAR1 deficiencies or AD TLR3, TICAM1, TBK1, and IRF3 deficiencies) or new (AD UNC93B1, IRF7, IFNAR1, and IFNAR2 deficiencies) genetic defects at eight of the 13 candidate loci involved in the TLR3- and IRF7-dependent induction and amplification of type I IFNs. This discovery reveals essential roles for both the double-stranded RNA sensor TLR3 and type I IFN cell-intrinsic immunity in the control of SARS-CoV-2 infection. Type I IFN administration may be of therapeutic benefit in selected patients, at least early in the course of SARS-CoV-2 infection. ![Figure][5] Inborn errors of TLR3- and IRF7-dependent type I IFN production and amplification underlie life-threatening COVID-19 pneumonia. Molecules in red are encoded by core genes, deleterious variants of which underlie critical influenza pneumonia with incomplete penetrance, and deleterious variants of genes encoding biochemically related molecules in blue underlie other viral illnesses. Molecules represented in bold are encoded by genes with variants that also underlie critical COVID-19 pneumonia. Clinical outcome upon infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ranges from silent infection to lethal coronavirus disease 2019 (COVID-19). We have found an enrichment in rare variants predicted to be loss-of-function (LOF) at the 13 human loci known to govern Toll-like receptor 3 (TLR3)– and interferon regulatory factor 7 (IRF7)–dependent type I interferon (IFN) immunity to influenza virus in 659 patients with life-threatening COVID-19 pneumonia relative to 534 subjects with asymptomatic or benign infection. By testing these and other rare variants at these 13 loci, we experimentally defined LOF variants underlying autosomal-recessive or autosomal-dominant deficiencies in 23 patients (3.5%) 17 to 77 years of age. We show that human fibroblasts with mutations affecting this circuit are vulnerable to SARS-CoV-2. Inborn errors of TLR3- and IRF7-dependent type I IFN immunity can underlie life-threatening COVID-19 pneumonia in patients with no prior severe infection. [1]: /lookup/doi/10.1126/science.abd4570 [2]: /lookup/doi/10.1126/science.abd4585 [3]: /lookup/doi/10.1126/science.abe7591 [4]: https://www.covidhge.com [5]: pending:yes


How to Detect At-risk Patients with Real World Data - The Databricks Blog

#artificialintelligence

With the rise of low cost genome sequencing and AI-enabled medical imaging, there has been substantial interest in precision medicine. In precision medicine, we aim to use data and AI to come up with the best treatment for a disease. While precision medicine has improved outcomes for patients diagnosed with rare diseases and cancers, precision medicine is reactive: the patient has to be sick for precision medicine to be deployed. When we look at healthcare spending and outcomes, there is a tremendous opportunity to improve cost-of-care and quality of living by preventing chronic conditions such as diabetes, heart disease, or substance use disorders. In the United States, 7 out of 10 deaths and 85% of healthcare spending is driven by chronic conditions, and similar trends are found in Europe and Southeast Asia.


IBM is a step closer to developing accurate AI prediction model for Alzheimer's

ZDNet

IBM has partnered with pharmaceutical giant Pfizer to design an artificial intelligence (AI) model to predict the eventual onset of the neurological disease seven years before symptoms appear. Alzheimer's is currently incurable and is often diagnosed too late to prevent it from accelerating. Symptoms for the disease include the gradual degradation of memory, confusion, and difficulty in completing once-familiar daily tasks. Published in The Lancet eClinical Medicine, the researchers used small samples of language data from clinical verbal tests provided by the Framingham Heart Study, a long-term study that has been tracking the health of more than 5,000 people and their families since 1948, to train the AI models. The AI model's ability was then verified against data samples from a group of healthy individuals who eventually did and did not develop the disease later in life.


Fujifilm works with Shanghai firm to get Avigan approved in China

The Japan Times

Fujifilm Holdings Corp. said on Thursday it has partnered with Shanghai-based Carelink Pharmaceutical Co. to seek approval in China for Avigan to treat COVID-19 and influenza. Carelink will use Fujifilm's data on Avigan's treatment of novel coronavirus infections and influenza to seek imported drug approval in China, Fujifilm said in a statement. The two companies also plan to develop an injectable form of the drug. Fujifilm said last week it was seeking approval for Avigan as a treatment for COVID-19 in Japan. That followed results from a late-stage study in Japan that showed the antiviral drug reduced recovery time for patients with nonsevere symptoms.


Artificial Intelligence improves clinical trials

#artificialintelligence

In case anyone missed it: attention on AI's application to healthcare is apparently at'peak hype'. With the volume of healthcare data doubling every 2 to 5 years, it is no surprise that many are using AI to make sense of such vast amounts of data, and development of medical AI technologies is progressing rapidly. At the same time, the COVID-19 pandemic has exposed vulnerabilities in healthcare systems around the world, highlighting the need for technological interventions in healthcare. In line with these trends, the healthcare AI market is expected to grow from US$2 billion in 2018 to US$36 billion by 2025. The breadth of AI's application in healthcare is impressive, ranging from diagnostic chat bots to AI robot-assisted surgery.