Showing papers by "Brown University published in 2021"

Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)

Abstract: In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field.

Physics-informed machine learning

Abstract: Despite great progress in simulating multiphysics problems using the numerical discretization of partial differential equations (PDEs), one still cannot seamlessly incorporate noisy data into existing algorithms, mesh generation remains complex, and high-dimensional problems governed by parameterized PDEs cannot be tackled. Moreover, solving inverse problems with hidden physics is often prohibitively expensive and requires different formulations and elaborate computer codes. Machine learning has emerged as a promising alternative, but training deep neural networks requires big data, not always available for scientific problems. Instead, such networks can be trained from additional information obtained by enforcing the physical laws (for example, at random points in the continuous space-time domain). Such physics-informed learning integrates (noisy) data and mathematical models, and implements them through neural networks or other kernel-based regression networks. Moreover, it may be possible to design specialized network architectures that automatically satisfy some of the physical invariants for better accuracy, faster training and improved generalization. Here, we review some of the prevailing trends in embedding physics into machine learning, present some of the current capabilities and limitations and discuss diverse applications of physics-informed learning both for forward and inverse problems, including discovering hidden physics and tackling high-dimensional problems. The rapidly developing field of physics-informed learning integrates data and mathematical models seamlessly, enabling accurate inference of realistic and high-dimensional multiphysics problems. This Review discusses the methodology and provides diverse examples and an outlook for further developments.

Surviving sepsis campaign: international guidelines for management of sepsis and septic shock 2021.

Abstract: Background Sepsis poses a global threat to millions of lives. The Surviving Sepsis Campaign (SSC) guidelines provide evidence-based recommendations on the recognition and management of sepsis and its complications. Methods We formed a panel of 60 experts from 22 countries and 11 members of the public. The panel prioritized questions that are relevant to the recognition and management of sepsis and septic shock in adults. New questions and sections were addressed, relative to the previous guidelines. These questions were grouped under 6 subgroups (screening and early treatment, infection, hemodynamics, ventilation, additional therapies, and long-term outcomes and goals of care). With input from the panel and methodologists, professional medical librarians performed the search strategy tailored to either specific questions or a group of relevant questions. A dedicated systematic review team performed screening and data abstraction when indicated. For each question, the methodologists, with input from panel members, summarized the evidence assessed and graded the quality of evidence using the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach. The panel generated recommendations using the evidence-to-decision framework. Recommendations were either strong or weak, or in the form of best practice statements. When evidence was insufficient to support a recommendation, the panel was surveyed to generate “in our practice” statements. Results The SSC panel issued 93 statements: 15 best practice statements, 15 strong recommendations, and 54 weak recommendations and no recommendation was provided for 9 questions. The recommendations address several important clinical areas related to screening tools, acute resuscitation strategies, management of fluids and vasoactive agents, antimicrobials and diagnostic tests and the use of additional therapies, ventilation management, goals of care, and post sepsis care. Conclusion The SSC panel issued evidence-based recommendations to help support key stakeholders caring for adults with sepsis or septic shock and their families.

Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program.

Abstract: The Trans-Omics for Precision Medicine (TOPMed) programme seeks to elucidate the genetic architecture and biology of heart, lung, blood and sleep disorders, with the ultimate goal of improving diagnosis, treatment and prevention of these diseases The initial phases of the programme focused on whole-genome sequencing of individuals with rich phenotypic data and diverse backgrounds Here we describe the TOPMed goals and design as well as the available resources and early insights obtained from the sequence data The resources include a variant browser, a genotype imputation server, and genomic and phenotypic data that are available through dbGaP (Database of Genotypes and Phenotypes)1 In the first 53,831 TOPMed samples, we detected more than 400 million single-nucleotide and insertion or deletion variants after alignment with the reference genome Additional previously undescribed variants were detected through assembly of unmapped reads and customized analysis in highly variable loci Among the more than 400 million detected variants, 97% have frequencies of less than 1% and 46% are singletons that are present in only one individual (53% among unrelated individuals) These rare variants provide insights into mutational processes and recent human evolutionary history The extensive catalogue of genetic variation in TOPMed studies provides unique opportunities for exploring the contributions of rare and noncoding sequence variants to phenotypic variation Furthermore, combining TOPMed haplotypes with modern imputation methods improves the power and reach of genome-wide association studies to include variants down to a frequency of approximately 001% The goals, resources and design of the NHLBI Trans-Omics for Precision Medicine (TOPMed) programme are described, and analyses of rare variants detected in the first 53,831 samples provide insights into mutational processes and recent human evolutionary history

An evidence review of face masks against COVID-19.

Abstract: The science around the use of masks by the public to impede COVID-19 transmission is advancing rapidly. In this narrative review, we develop an analytical framework to examine mask usage, synthesizing the relevant literature to inform multiple areas: population impact, transmission characteristics, source control, wearer protection, sociological considerations, and implementation considerations. A primary route of transmission of COVID-19 is via respiratory particles, and it is known to be transmissible from presymptomatic, paucisymptomatic, and asymptomatic individuals. Reducing disease spread requires two things: limiting contacts of infected individuals via physical distancing and other measures and reducing the transmission probability per contact. The preponderance of evidence indicates that mask wearing reduces transmissibility per contact by reducing transmission of infected respiratory particles in both laboratory and clinical contexts. Public mask wearing is most effective at reducing spread of the virus when compliance is high. Given the current shortages of medical masks, we recommend the adoption of public cloth mask wearing, as an effective form of source control, in conjunction with existing hygiene, distancing, and contact tracing strategies. Because many respiratory particles become smaller due to evaporation, we recommend increasing focus on a previously overlooked aspect of mask usage: mask wearing by infectious people ("source control") with benefits at the population level, rather than only mask wearing by susceptible people, such as health care workers, with focus on individual outcomes. We recommend that public officials and governments strongly encourage the use of widespread face masks in public, including the use of appropriate regulation.

Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators

Abstract: It is widely known that neural networks (NNs) are universal approximators of continuous functions. However, a less known but powerful result is that a NN with a single hidden layer can accurately approximate any nonlinear continuous operator. This universal approximation theorem of operators is suggestive of the structure and potential of deep neural networks (DNNs) in learning continuous operators or complex systems from streams of scattered data. Here, we thus extend this theorem to DNNs. We design a new network with small generalization error, the deep operator network (DeepONet), which consists of a DNN for encoding the discrete input function space (branch net) and another DNN for encoding the domain of the output functions (trunk net). We demonstrate that DeepONet can learn various explicit operators, such as integrals and fractional Laplacians, as well as implicit operators that represent deterministic and stochastic differential equations. We study different formulations of the input function space and its effect on the generalization error for 16 different diverse applications. Neural networks are known as universal approximators of continuous functions, but they can also approximate any mathematical operator (mapping a function to another function), which is an important capability for complex systems such as robotics control. A new deep neural network called DeepONet can lean various mathematical operators with small generalization error.

Surviving Sepsis Campaign: International Guidelines for Management of Sepsis and Septic Shock 2021.

Abstract: Background Sepsis poses a global threat to millions of lives. The Surviving Sepsis Campaign (SSC) guidelines provide evidence-based recommendations on the recognition and management of sepsis and its complications. Methods We formed a panel of 60 experts from 22 countries and 11 members of the public. The panel prioritized questions that are relevant to the recognition and management of sepsis and septic shock in adults. New questions and sections were addressed, relative to the previous guidelines. These questions were grouped under 6 subgroups (screening and early treatment, infection, hemodynamics, ventilation, additional therapies, and long-term outcomes and goals of care). With input from the panel and methodologists, professional medical librarians performed the search strategy tailored to either specific questions or a group of relevant questions. A dedicated systematic review team performed screening and data abstraction when indicated. For each question, the methodologists, with input from panel members, summarized the evidence assessed and graded the quality of evidence using the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach. The panel generated recommendations using the evidence-to-decision framework. Recommendations were either strong or weak, or in the form of best practice statements. When evidence was insufficient to support a recommendation, the panel was surveyed to generate “in our practice” statements. Results The SSC panel issued 93 statements: 15 best practice statements, 15 strong recommendations, and 54 weak recommendations and no recommendation was provided for 9 questions. The recommendations address several important clinical areas related to screening tools, acute resuscitation strategies, management of fluids and vasoactive agents, antimicrobials and diagnostic tests and the use of additional therapies, ventilation management, goals of care, and post sepsis care. Conclusion The SSC panel issued evidence-based recommendations to help support key stakeholders caring for adults with sepsis or septic shock and their families.

Predictors of COVID-19 severity: A literature review.

Abstract: The coronavirus disease 2019 (COVID-19) pandemic is a rapidly evolving global emergency that continues to strain healthcare systems. Emerging research describes a plethora of patient factors-including demographic, clinical, immunologic, hematological, biochemical, and radiographic findings-that may be of utility to clinicians to predict COVID-19 severity and mortality. We present a synthesis of the current literature pertaining to factors predictive of COVID-19 clinical course and outcomes. Findings associated with increased disease severity and/or mortality include age > 55 years, multiple pre-existing comorbidities, hypoxia, specific computed tomography findings indicative of extensive lung involvement, diverse laboratory test abnormalities, and biomarkers of end-organ dysfunction. Hypothesis-driven research is critical to identify the key evidence-based prognostic factors that will inform the design of intervention studies to improve the outcomes of patients with COVID-19 and to appropriately allocate scarce resources.

Donanemab in Early Alzheimer's Disease.

Abstract: Background A hallmark of Alzheimer’s disease is the accumulation of amyloid-β (Aβ) peptide. Donanemab, an antibody that targets a modified form of deposited Aβ, is being investigated for t...

Mental health and clinical psychological science in the time of COVID-19: Challenges, opportunities, and a call to action.

Abstract: COVID-19 presents significant social, economic, and medical challenges. Because COVID-19 has already begun to precipitate huge increases in mental health problems, clinical psychological science must assert a leadership role in guiding a national response to this secondary crisis. In this article, COVID-19 is conceptualized as a unique, compounding, multidimensional stressor that will create a vast need for intervention and necessitate new paradigms for mental health service delivery and training. Urgent challenge areas across developmental periods are discussed, followed by a review of psychological symptoms that likely will increase in prevalence and require innovative solutions in both science and practice. Implications for new research directions, clinical approaches, and policy issues are discussed to highlight the opportunities for clinical psychological science to emerge as an updated, contemporary field capable of addressing the burden of mental illness and distress in the wake of COVID-19 and beyond. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

The impact of the COVID-19 pandemic on maternal and perinatal health: a scoping review.

Abstract: The Covid-19 pandemic affects maternal health both directly and indirectly, and direct and indirect effects are intertwined. To provide a comprehensive overview on this broad topic in a rapid format behooving an emergent pandemic we conducted a scoping review. A scoping review was conducted to compile evidence on direct and indirect impacts of the pandemic on maternal health and provide an overview of the most significant outcomes thus far. Working papers and news articles were considered appropriate evidence along with peer-reviewed publications in order to capture rapidly evolving updates. Literature in English published from January 1st to September 11 2020 was included if it pertained to the direct or indirect effects of the COVID-19 pandemic on the physical, mental, economic, or social health and wellbeing of pregnant people. Narrative descriptions were written about subject areas for which the authors found the most evidence. The search yielded 396 publications, of which 95 were included. Pregnant individuals were found to be at a heightened risk of more severe symptoms than people who are not pregnant. Intrauterine, vertical, and breastmilk transmission were unlikely. Labor, delivery, and breastfeeding guidelines for COVID-19 positive patients varied. Severe increases in maternal mental health issues, such as clinically relevant anxiety and depression, were reported. Domestic violence appeared to spike. Prenatal care visits decreased, healthcare infrastructure was strained, and potentially harmful policies implemented with little evidence. Women were more likely to lose their income due to the pandemic than men, and working mothers struggled with increased childcare demands. Pregnant women and mothers were not found to be at higher risk for COVID-19 infection than people who are not pregnant, however pregnant people with symptomatic COVID-19 may experience more adverse outcomes compared to non-pregnant people and seem to face disproportionate adverse socio-economic consequences. High income and low- and middle-income countries alike faced significant struggles. Further resources should be directed towards quality epidemiological studies. The Covid-19 pandemic impacts reproductive and perinatal health both directly through infection itself but also indirectly as a consequence of changes in health care, social policy, or social and economic circumstances. The direct and indirect consequences of COVID-19 on maternal health are intertwined. To provide a comprehensive overview on this broad topic we conducted a scoping review. Pregnant women who have symptomatic COVID-19 may experience more severe outcomes than people who are not pregnant. Intrauterine and breastmilk transmission, and the passage of the virus from mother to baby during delivery are unlikely. The guidelines for labor, delivery, and breastfeeding for COVID-19 positive patients vary, and this variability could create uncertainty and unnecessary harm. Prenatal care visits decreased, healthcare infrastructure was strained, and potentially harmful policies are implemented with little evidence in high and low/middle income countries. The social and economic impact of COVID-19 on maternal health is marked. A high frequency of maternal mental health problems, such as clinically relevant anxiety and depression, during the epidemic are reported in many countries. This likely reflects an increase in problems, but studies demonstrating a true change are lacking. Domestic violence appeared to spike. Women were more vulnerable to losing their income due to the pandemic than men, and working mothers struggled with increased childcare demands. We make several recommendations: more resources should be directed to epidemiological studies, health and social services for pregnant women and mothers should not be diminished, and more focus on maternal mental health during the epidemic is needed.

Clinical diagnosis of Alzheimer's disease: recommendations of the International Working Group.

Abstract: In 2018, the US National Institute on Aging and the Alzheimer's Association proposed a purely biological definition of Alzheimer's disease that relies on biomarkers. Although the intended use of this framework was for research purposes, it has engendered debate and challenges regarding its use in everyday clinical practice. For instance, cognitively unimpaired individuals can have biomarker evidence of both amyloid β and tau pathology but will often not develop clinical manifestations in their lifetime. Furthermore, a positive Alzheimer's disease pattern of biomarkers can be observed in other brain diseases in which Alzheimer's disease pathology is present as a comorbidity. In this Personal View, the International Working Group presents what we consider to be the current limitations of biomarkers in the diagnosis of Alzheimer's disease and, on the basis of this evidence, we propose recommendations for how biomarkers should and should not be used for diagnosing Alzheimer's disease in a clinical setting. We recommend that Alzheimer's disease diagnosis be restricted to people who have positive biomarkers together with specific Alzheimer's disease phenotypes, whereas biomarker-positive cognitively unimpaired individuals should be considered only at-risk for progression to Alzheimer's disease.

Physics-Informed Neural Networks for Heat Transfer Problems

Abstract: Physics-informed neural networks (PINNs) have gained popularity across different engineering fields due to their effectiveness in solving realistic problems with noisy data and often partially missing physics. In PINNs, automatic differentiation is leveraged to evaluate differential operators without discretization errors, and a multitask learning problem is defined in order to simultaneously fit observed data while respecting the underlying governing laws of physics. Here, we present applications of PINNs to various prototype heat transfer problems, targeting in particular realistic conditions not readily tackled with traditional computational methods. To this end, we first consider forced and mixed convection with unknown thermal boundary conditions on the heated surfaces and aim to obtain the temperature and velocity fields everywhere in the domain, including the boundaries, given some sparse temperature measurements. We also consider the prototype Stefan problem for two-phase flow, aiming to infer the moving interface, the velocity and temperature fields everywhere as well as the different conductivities of a solid and a liquid phase, given a few temperature measurements inside the domain. Finally, we present some realistic industrial applications related to power electronics to highlight the practicality of PINNs as well as the effective use of neural networks in solving general heat transfer problems of industrial complexity. Taken together, the results presented herein demonstrate that PINNs not only can solve ill-posed problems, which are beyond the reach of traditional computational methods, but they can also bridge the gap between computational and experimental heat transfer.

Surviving Sepsis Campaign Guidelines on the Management of Adults With Coronavirus Disease 2019 (COVID-19) in the ICU: First Update.

Abstract: Background The coronavirus disease 2019 pandemic continues to affect millions worldwide. Given the rapidly growing evidence base, we implemented a living guideline model to provide guidance on the management of patients with severe or critical coronavirus disease 2019 in the ICU. Methods The Surviving Sepsis Campaign Coronavirus Disease 2019 panel has expanded to include 43 experts from 14 countries; all panel members completed an electronic conflict-of-interest disclosure form. In this update, the panel addressed nine questions relevant to managing severe or critical coronavirus disease 2019 in the ICU. We used the World Health Organization's definition of severe and critical coronavirus disease 2019. The systematic reviews team searched the literature for relevant evidence, aiming to identify systematic reviews and clinical trials. When appropriate, we performed a random-effects meta-analysis to summarize treatment effects. We assessed the quality of the evidence using the Grading of Recommendations, Assessment, Development, and Evaluation approach, then used the evidence-to-decision framework to generate recommendations based on the balance between benefit and harm, resource and cost implications, equity, and feasibility. Results The Surviving Sepsis Campaign Coronavirus Diease 2019 panel issued nine statements (three new and six updated) related to ICU patients with severe or critical coronavirus disease 2019. For severe or critical coronavirus disease 2019, the panel strongly recommends using systemic corticosteroids and venous thromboprophylaxis but strongly recommends against using hydroxychloroquine. In addition, the panel suggests using dexamethasone (compared with other corticosteroids) and suggests against using convalescent plasma and therapeutic anticoagulation outside clinical trials. The Surviving Sepsis Campaign Coronavirus Diease 2019 panel suggests using remdesivir in nonventilated patients with severe coronavirus disease 2019 and suggests against starting remdesivir in patients with critical coronavirus disease 2019 outside clinical trials. Because of insufficient evidence, the panel did not issue a recommendation on the use of awake prone positioning. Conclusion The Surviving Sepsis Campaign Coronavirus Diease 2019 panel issued several recommendations to guide healthcare professionals caring for adults with critical or severe coronavirus disease 2019 in the ICU. Based on a living guideline model the recommendations will be updated as new evidence becomes available.

Interfacial toughening with self-assembled monolayers enhances perovskite solar cell reliability

Abstract: Iodine-terminated self-assembled monolayer (I-SAM) was used in perovskite solar cells (PSCs) to achieve a 50% increase of adhesion toughness at the interface between the electron transport layer (ETL) and the halide perovskite thin film to enhance mechanical reliability. Treatment with I-SAM also increased the power conversion efficiency from 20.2% to 21.4%, reduced hysteresis, and improved operational stability with a projected T80 (time to 80% initial efficiency retained) increasing from ~700 hours to 4000 hours under 1-sun illumination and with continuous maximum power point tracking. Operational stability-tested PSC without SAMs revealed extensive irreversible morphological degradation at the ETL/perovskite interface, including voids formation and delamination, whereas PSCs with I-SAM exhibited minimal damage accumulation. This difference was attributed to a combination of a decrease in hydroxyl groups at the interface and the higher interfacial toughness.

Neurological Involvement in COVID-19 and Potential Mechanisms: A Review.

Abstract: As the current understanding of COVID-19 continues to evolve, a synthesis of the literature on the neurological impact of this novel virus may help inform clinical management and highlight potentially important avenues of investigation. Additionally, understanding the potential mechanisms of neurologic injury may guide efforts to better detect and ameliorate these complications. In this review, we synthesize a range of clinical observations and initial case series describing potential neurologic manifestations of COVID-19 and place these observations in the context of coronavirus neuro-pathophysiology as it may relate to SARS-CoV-2 infection. Reported nervous system manifestations range from anosmia and ageusia, to cerebral hemorrhage and infarction. While the volume of COVID-19-related case studies continues to grow, previous work examining related viruses suggests potential mechanisms through which the novel coronavirus may impact the CNS and result in neurological complications. Namely, animal studies examining the SARS-CoV have implicated the angiotensin-converting-enzyme-2 receptor as a mediator of coronavirus-related neuronal damage and have shown that SARS-CoV can infect cerebrovascular endothelium and brain parenchyma, the latter predominantly in the medial temporal lobe, resulting in apoptosis and necrosis. Human postmortem brain studies indicate that human coronavirus variants and SARS-CoV can infect neurons and glia, implying SARS-CoV-2 may have similar neurovirulence. Additionally, studies have demonstrated an increase in cytokine serum levels as a result of SARS-CoV infection, consistent with the notion that cytokine overproduction and toxicity may be a relevant potential mechanism of neurologic injury, paralleling a known pathway of pulmonary injury. We also discuss evidence that suggests that SARS-CoV-2 may be a vasculotropic and neurotropic virus. Early reports suggest COVID-19 may be associated with severe neurologic complications, and several plausible mechanisms exist to account for these observations. A heightened awareness of the potential for neurologic involvement and further investigation into the relevant pathophysiology will be necessary to understand and ultimately mitigate SARS-CoV-2-associated neurologic injury.

In vivo base editing rescues Hutchinson-Gilford progeria syndrome in mice.

Abstract: Hutchinson–Gilford progeria syndrome (HGPS or progeria) is typically caused by a dominant-negative C•G-to-T•A mutation (c.1824 C>T; p.G608G) in LMNA, the gene that encodes nuclear lamin A. This mutation causes RNA mis-splicing that produces progerin, a toxic protein that induces rapid ageing and shortens the lifespan of children with progeria to approximately 14 years1–4. Adenine base editors (ABEs) convert targeted A•T base pairs to G•C base pairs with minimal by-products and without requiring double-strand DNA breaks or donor DNA templates5,6. Here we describe the use of an ABE to directly correct the pathogenic HGPS mutation in cultured fibroblasts derived from children with progeria and in a mouse model of HGPS. Lentiviral delivery of the ABE to fibroblasts from children with HGPS resulted in 87–91% correction of the pathogenic allele, mitigation of RNA mis-splicing, reduced levels of progerin and correction of nuclear abnormalities. Unbiased off-target DNA and RNA editing analysis did not detect off-target editing in treated patient-derived fibroblasts. In transgenic mice that are homozygous for the human LMNA c.1824 C>T allele, a single retro-orbital injection of adeno-associated virus 9 (AAV9) encoding the ABE resulted in substantial, durable correction of the pathogenic mutation (around 20–60% across various organs six months after injection), restoration of normal RNA splicing and reduction of progerin protein levels. In vivo base editing rescued the vascular pathology of the mice, preserving vascular smooth muscle cell counts and preventing adventitial fibrosis. A single injection of ABE-expressing AAV9 at postnatal day 14 improved vitality and greatly extended the median lifespan of the mice from 215 to 510 days. These findings demonstrate the potential of in vivo base editing as a possible treatment for HGPS and other genetic diseases by directly correcting their root cause. In a mouse model of progeria, an adenine base editor delivered with adeno-associated virus corrects the pathogenic mutation in LMNA, rescues vascular pathology and markedly extends the lifespan of the mice.

The trans-ancestral genomic architecture of glycemic traits

Abstract: Glycemic traits are used to diagnose and monitor type 2 diabetes and cardiometabolic health. To date, most genetic studies of glycemic traits have focused on individuals of European ancestry. Here we aggregated genome-wide association studies comprising up to 281,416 individuals without diabetes (30% non-European ancestry) for whom fasting glucose, 2-h glucose after an oral glucose challenge, glycated hemoglobin and fasting insulin data were available. Trans-ancestry and single-ancestry meta-analyses identified 242 loci (99 novel; P < 5 × 10-8), 80% of which had no significant evidence of between-ancestry heterogeneity. Analyses restricted to individuals of European ancestry with equivalent sample size would have led to 24 fewer new loci. Compared with single-ancestry analyses, equivalent-sized trans-ancestry fine-mapping reduced the number of estimated variants in 99% credible sets by a median of 37.5%. Genomic-feature, gene-expression and gene-set analyses revealed distinct biological signatures for each trait, highlighting different underlying biological pathways. Our results increase our understanding of diabetes pathophysiology by using trans-ancestry studies for improved power and resolution.

Copper-coordinated cellulose ion conductors for solid-state batteries.

Abstract: Although solid-state lithium (Li)-metal batteries promise both high energy density and safety, existing solid ion conductors fail to satisfy the rigorous requirements of battery operations. Inorganic ion conductors allow fast ion transport, but their rigid and brittle nature prevents good interfacial contact with electrodes. Conversely, polymer ion conductors that are Li-metal-stable usually provide better interfacial compatibility and mechanical tolerance, but typically suffer from inferior ionic conductivity owing to the coupling of the ion transport with the motion of the polymer chains1–3. Here we report a general strategy for achieving high-performance solid polymer ion conductors by engineering of molecular channels. Through the coordination of copper ions (Cu2+) with one-dimensional cellulose nanofibrils, we show that the opening of molecular channels within the normally ion-insulating cellulose enables rapid transport of Li+ ions along the polymer chains. In addition to high Li+ conductivity (1.5 × 10−3 siemens per centimetre at room temperature along the molecular chain direction), the Cu2+-coordinated cellulose ion conductor also exhibits a high transference number (0.78, compared with 0.2–0.5 in other polymers2) and a wide window of electrochemical stability (0–4.5 volts) that can accommodate both the Li-metal anode and high-voltage cathodes. This one-dimensional ion conductor also allows ion percolation in thick LiFePO4 solid-state cathodes for application in batteries with a high energy density. Furthermore, we have verified the universality of this molecular-channel engineering approach with other polymers and cations, achieving similarly high conductivities, with implications that could go beyond safe, high-performance solid-state batteries. By coordinating copper ions with the oxygen-containing groups of cellulose nanofibrils, the molecular spacing in the nanofibrils is increased, allowing fast transport of lithium ions and offering hopes for solid-state batteries.

Multiscale modeling meets machine learning: What can we learn?

Abstract: Machine learning is increasingly recognized as a promising technology in the biological, biomedical, and behavioral sciences. There can be no argument that this technique is incredibly successful in image recognition with immediate applications in diagnostics including electrophysiology, radiology, or pathology, where we have access to massive amounts of annotated data. However, machine learning often performs poorly in prognosis, especially when dealing with sparse data. This is a field where classical physics-based simulation seems to remain irreplaceable. In this review, we identify areas in the biomedical sciences where machine learning and multiscale modeling can mutually benefit from one another: Machine learning can integrate physics-based knowledge in the form of governing equations, boundary conditions, or constraints to manage ill-posted problems and robustly handle sparse and noisy data; multiscale modeling can integrate machine learning to create surrogate models, identify system dynamics and parameters, analyze sensitivities, and quantify uncertainty to bridge the scales and understand the emergence of function. With a view towards applications in the life sciences, we discuss the state of the art of combining machine learning and multiscale modeling, identify applications and opportunities, raise open questions, and address potential challenges and limitations. We anticipate that it will stimulate discussion within the community of computational mechanics and reach out to other disciplines including mathematics, statistics, computer science, artificial intelligence, biomedicine, systems biology, and precision medicine to join forces towards creating robust and efficient models for biological systems.

Investigation of Long COVID Prevalence and Its Relationship to Epstein-Barr Virus Reactivation

Abstract: Coronavirus disease 2019 (COVID-19) patients sometimes experience long-term symptoms following resolution of acute disease, including fatigue, brain fog, and rashes. Collectively these have become known as long COVID. Our aim was to first determine long COVID prevalence in 185 randomly surveyed COVID-19 patients and, subsequently, to determine if there was an association between occurrence of long COVID symptoms and reactivation of Epstein–Barr virus (EBV) in 68 COVID-19 patients recruited from those surveyed. We found the prevalence of long COVID symptoms to be 30.3% (56/185), which included 4 initially asymptomatic COVID-19 patients who later developed long COVID symptoms. Next, we found that 66.7% (20/30) of long COVID subjects versus 10% (2/20) of control subjects in our primary study group were positive for EBV reactivation based on positive titers for EBV early antigen-diffuse (EA-D) IgG or EBV viral capsid antigen (VCA) IgM. The difference was significant (p < 0.001, Fisher’s exact test). A similar ratio was observed in a secondary group of 18 subjects 21–90 days after testing positive for COVID-19, indicating reactivation may occur soon after or concurrently with COVID-19 infection. These findings suggest that many long COVID symptoms may not be a direct result of the SARS-CoV-2 virus but may be the result of COVID-19 inflammation-induced EBV reactivation.

Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning.

Abstract: A principal challenge in the analysis of tissue imaging data is cell segmentation—the task of identifying the precise boundary of every cell in an image. To address this problem we constructed TissueNet, a dataset for training segmentation models that contains more than 1 million manually labeled cells, an order of magnitude more than all previously published segmentation training datasets. We used TissueNet to train Mesmer, a deep-learning-enabled segmentation algorithm. We demonstrated that Mesmer is more accurate than previous methods, generalizes to the full diversity of tissue types and imaging platforms in TissueNet, and achieves human-level performance. Mesmer enabled the automated extraction of key cellular features, such as subcellular localization of protein signal, which was challenging with previous approaches. We then adapted Mesmer to harness cell lineage information in highly multiplexed datasets and used this enhanced version to quantify cell morphology changes during human gestation. All code, data and models are released as a community resource. Deep learning algorithms perform as well as humans in identifying cells in tissue images.

Amyloid-Related Imaging Abnormalities in 2 Phase 3 Studies Evaluating Aducanumab in Patients With Early Alzheimer Disease.

Abstract: Importance The EMERGE and ENGAGE phase 3 randomized clinical trials of aducanumab provide a robust data set to characterize amyloid-related imaging abnormalities (ARIA) that occur with treatment with aducanumab, an amyloid-β (Aβ)-targeting monoclonal antibody, in patients with mild cognitive impairment due to Alzheimer disease or mild Alzheimer disease dementia. Objective To describe the radiographic and clinical characteristics of ARIA that occurred in EMERGE and ENGAGE. Design, setting, and participants Secondary analysis of data from the EMERGE and ENGAGE trials, which were 2 double-blind, placebo-controlled, parallel-group, phase 3 randomized clinical trials that compared low-dose and high-dose aducanumab treatment with placebo among participants at 348 sites across 20 countries. Enrollment occurred from August 2015 to July 2018, and the trials were terminated early (March 21, 2019) based on a futility analysis. The combined studies consisted of a total of 3285 participants with Alzheimer disease who received 1 or more doses of placebo (n = 1087) or aducanumab (n = 2198; 2752 total person-years of exposure) during the placebo-controlled period. Primary data analyses were performed from November 2019 to July 2020, with additional analyses performed through July 2021. Interventions Participants were randomly assigned 1:1:1 to high-dose or low-dose intravenous aducanumab or placebo once every 4 weeks. Dose titration was used as a risk-minimization strategy. Main outcomes and measures Brain magnetic resonance imaging was used to monitor patients for ARIA; associated symptoms were reported as adverse events. Results Of 3285 included participants, the mean (SD) age was 70.4 (7.45) years; 1706 participants (52%) were female, 2661 (81%) had mild cognitive impairment due to Alzheimer disease, and 1777 (54%) used symptomatic medications for Alzheimer disease. A total of 764 participants from EMERGE and 709 participants from ENGAGE were categorized as withdrawn before study completion, most often owing to early termination of the study by the sponsor. Unless otherwise specified, all results represent analyses from the 10-mg/kg group. During the placebo-controlled period, 425 of 1029 patients (41.3%) experienced ARIA, with serious cases occurring in 14 patients (1.4%). ARIA-edema (ARIA-E) was the most common adverse event (362 of 1029 [35.2%]), and 263 initial events (72.7%) occurred within the first 8 doses of aducanumab; 94 participants (26.0%) with an event exhibited symptoms. Common associated symptoms among 103 patients with symptomatic ARIA-E or ARIA-H were headache (48 [46.6%]), confusion (15 [14.6%]), dizziness (11 [10.7%]), and nausea (8 [7.8%]). Incidence of ARIA-E was highest in aducanumab-treated participants who were apolipoprotein E e4 allele carriers. Most events (479 of 488 [98.2%]) among those with ARIA-E resolved radiographically; 404 of 488 (82.8%) resolved within 16 weeks. In the placebo group, 29 of 1076 participants (2.7%) had ARIA-E (apolipoprotein E e4 carriers: 16 of 742 [2.2%]; noncarriers, 13 of 334 [3.9%]). ARIA-microhemorrhage and ARIA-superficial siderosis occurred in 197 participants (19.1%) and 151 participants (14.7%), respectively. Conclusions and relevance In this integrated safety data set from EMERGE and ENGAGE, the most common adverse event in the 10-mg/kg group was ARIA-E, which occurred in 362 of the 1029 patients (35.2%) in the 10-mg/kg group with at least 1 postbaseline MRI scan, with 94 patients (26.0%) experiencing associated symptoms. The most common associated symptom was headache. Trial registrations ClinicalTrials.gov Identifiers: NCT02484547, NCT02477800.