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In less than nine months, the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) killed over a million people, including >25,000 in New York City (NYC) alone. The COVID-19 pandemic caused by SARS-CoV-2 highlights clinical needs to detect infection, track strain evolution, and identify biomarkers of disease course. To address these challenges, we designed a fast (30-minute) colorimetric test (LAMP) for SARS-CoV-2 infection from naso/oropharyngeal swabs and a large-scale shotgun metatranscriptomics platform (total-RNA-seq) for host, viral, and microbial profiling. We applied these methods to clinical specimens gathered from 669 patients in New York City during the first two months of the outbreak, yielding a broad molecular portrait of the emerging COVID-19 disease. We find significant enrichment of a NYC-distinctive clade of the virus (20C), as well as host responses in interferon, ACE, hematological, and olfaction pathways. In addition, we use 50,821 patient records to find that renin-angiotensin-aldosterone system inhibitors have a protective effect for severe COVID-19 outcomes, unlike similar drugs. Finally, spatial transcriptomic data from COVID-19 patient autopsy tissues reveal distinct ACE2 expression loci, with macrophage and neutrophil infiltration in the lungs. These findings can inform public health and may help develop and drive SARS-CoV-2 diagnostic, prevention, and treatment strategies.

Background Hetnets, short for "heterogeneous networks," contain multiple node and relationship types and offer a way to encode biomedical knowledge. One such example, Hetionet, connects 11 types of nodes—including genes, diseases, drugs, pathways, and anatomical structures—with over 2 million edges of 24 types. Previous work has demonstrated that supervised machine learning methods applied to such networks can identify drug repurposing opportunities. However, a training set of known relationships does not exist for many types of node pairs, even when it would be useful to examine how nodes of those types are meaningfully connected. For example, users may be curious about not only how metformin is related to breast cancer but also how a given gene might be involved in insomnia. Findings We developed a new procedure, termed hetnet connectivity search , that proposes important paths between any 2 nodes without requiring a supervised gold standard. The algorithm behind connectivity search identifies types of paths that occur more frequently than would be expected by chance (based on node degree alone). Several optimizations were required to precompute significant instances of node connectivity at the scale of large knowledge graphs. Conclusion We implemented the method on Hetionet and provide an online interface at https://het.io/search. We provide an open-source implementation of these methods in our new Python package named hetmatpy. [ABSTRACT FROM AUTHOR]

As a subfield of machine learning, reinforcement learning (RL) aims at optimizing decision making by using interaction samples of an agent with its environment and the potentially delayed feedbacks. In contrast to traditional supervised learning that typically relies on one-shot, exhaustive, and supervised reward signals, RL tackles sequential decision-making problems with sampled, evaluative, and delayed feedbacks simultaneously. Such a distinctive feature makes RL techniques a suitable candidate for developing powerful solutions in various healthcare domains, where diagnosing decisions or treatment regimes are usually characterized by a prolonged period with delayed feedbacks. By first briefly examining theoretical foundations and key methods in RL research, this survey provides an extensive overview of RL applications in a variety of healthcare domains, ranging from dynamic treatment regimes in chronic diseases and critical care, automated medical diagnosis, and many other control or scheduling problems that have infiltrated every aspect of the healthcare system. In addition, we discuss the challenges and open issues in the current research and highlight some potential solutions and directions for future research. [ABSTRACT FROM AUTHOR]

As a subfield of machine learning, reinforcement learning (RL) aims at optimizing decision making by using interaction samples of an agent with its environment and the potentially delayed feedbacks. In contrast to traditional supervised learning that typically relies on one-shot, exhaustive, and supervised reward signals, RL tackles sequential decision-making problems with sampled, evaluative, and delayed feedbacks simultaneously. Such a distinctive feature makes RL techniques a suitable candidate for developing powerful solutions in various healthcare domains, where diagnosing decisions or treatment regimes are usually characterized by a prolonged period with delayed feedbacks. By first briefly examining theoretical foundations and key methods in RL research, this survey provides an extensive overview of RL applications in a variety of healthcare domains, ranging from dynamic treatment regimes in chronic diseases and critical care, automated medical diagnosis, and many other control or scheduling problems that have infiltrated every aspect of the healthcare system. In addition, we discuss the challenges and open issues in the current research and highlight some potential solutions and directions for future research. [ABSTRACT FROM AUTHOR]

Machine learning and statistical techniques are powerful tools for analyzing large amounts of medical and genomic data. On the other hand, ethical concerns and privacy regulations prevent free sharing of this data. Encryption techniques such as fully homomorphic encryption (FHE) enable evaluation over encrypted data. Using FHE, machine learning models such as deep learning, decision trees, and Naive Bayes have been implemented for privacy-preserving applications using medical data. These applications include classifying encrypted data and training models on encrypted data. FHE has also been shown to enable secure genomic algorithms, such as paternity and ancestry testing and privacy-preserving applications of genome-wide association studies. This survey provides an overview of fully homomorphic encryption and its applications in medicine and bioinformatics. The high-level concepts behind FHE and its history are introduced, and details on current open-source implementations are provided. The state of fully homomorphic encryption for privacypreserving techniques in machine learning and bioinformatics is reviewed, along with descriptions of how these methods can be implemented in the encrypted domain. [ABSTRACT FROM AUTHOR]

Purpose of Review: Real-world data (RWD) applications in healthcare that support learning health systems and pragmatic clinical trials are gaining momentum, largely due to legislation supporting real-world evidence (RWE) for drug approvals. Clinical notes are thought to be the cornerstone of RWD applications, particularly for conditions with limited effective treatments, extrapolation of treatments from other conditions, or heterogenous disease biology and clinical phenotypes. Recent Findings: Here, we discuss current issues in applying RWD captured at the point-of-care and provide a framework for clinicians to engage in RWD collection. To achieve clinically meaningful results, RWD must be reliably captured using consistent terminology in the description of our patients. Summary: RWD complements traditional clinical trials and research by informing the generalizability of results, generating new hypotheses, and creating a large data network for scientific discovery. Effective clinician engagement in the development of RWD applications is necessary for continued progress in the field. [ABSTRACT FROM AUTHOR]

A healthy voice is crucial for verbal communication and hence in daily as well as professional life. The basis for a healthy voice are the sound producing vocal folds in the larynx. A hallmark of healthy vocal fold oscillation is the symmetric motion of the left and right vocal fold. Clinically, videoendoscopy is applied to assess the symmetry of the oscillation and evaluated subjectively. High-speed videoendoscopy, an emerging method that allows quantification of the vocal fold oscillation, is more commonly employed in research due to the amount of data and the complex, semi-automatic analysis. In this study, we provide a comprehensive evaluation of methods that detect fully automatically the glottal midline. We used a biophysical model to simulate different vocal fold oscillations, extended the openly available BAGLS dataset using manual annotations, utilized both, simulations and annotated endoscopic images, to train deep neural networks at different stages of the analysis workflow, and compared these to established computer vision algorithms. We found that classical computer vision perform well on detecting the glottal midline in glottis segmentation data, but are outperformed by deep neural networks on this task. We further suggest GlottisNet, a multi-task neural architecture featuring the simultaneous prediction of both, the opening between the vocal folds and the symmetry axis, leading to a huge step forward towards clinical applicability of quantitative, deep learning-assisted laryngeal endoscopy, by fully automating segmentation and midline detection. [ABSTRACT FROM AUTHOR]

Background: Eukaryotes acquired the trait of oxygenic photosynthesis through endosymbiosis of the cyanobacterial progenitor of plastid organelles. Despite recent advances in the phylogenomics of Cyanobacteria, the phylogenetic root of plastids remains controversial. Although a single origin of plastids by endosymbiosis is broadly supported, recent phylogenomic studies are contradictory on whether plastids branch early or late within Cyanobacteria. One underlying cause may be poor fit of evolutionary models to complex phylogenomic data. Results: Using Posterior Predictive Analysis, we show that recently applied evolutionary models poorly fit three phylogenomic datasets curated from cyanobacteria and plastid genomes because of heterogeneities in both substitution processes across sites and of compositions across lineages. To circumvent these sources of bias, we developed CYANO-MLP, a machine learning algorithm that consistently and accurately phylogenetically classifies ("phyloclassifies") cyanobacterial genomes to their clade of origin based on bioinformatically predicted function-informative features in tRNA gene complements. Classification of cyanobacterial genomes with CYANO-MLP is accurate and robust to deletion of clades, unbalanced sampling, and compositional heterogeneity in input tRNA data. CYANO-MLP consistently classifies plastid genomes into a late-branching cyanobacterial sub-clade containing single-cell, starch-producing, nitrogen-fixing ecotypes, consistent with metabolic and gene transfer data. Conclusions: Phylogenomic data of cyanobacteria and plastids exhibit both site-process heterogeneities and compositional heterogeneities across lineages. These aspects of the data require careful modeling to avoid bias in phylogenomic estimation. Furthermore, we show that amino acid recoding strategies may be insufficient to mitigate bias from compositional heterogeneities. However, the combination of our novel tRNA-specific strategy with machine learning in CYANO-MLP appears robust to these sources of bias with high accuracy in phyloclassification of cyanobacterial genomes. CYANO-MLP consistently classifies plastids as late-branching Cyanobacteria, consistent with independent evidence from signature-based approaches and some previous phylogenetic studies. [ABSTRACT FROM AUTHOR]

The scale of biological microscopy has increased dramatically over the past ten years, with the development of new modalities supporting collection of high-resolution fluorescence image volumes spanning hundreds of microns if not millimeters. The size and complexity of these volumes is such that quantitative analysis requires automated methods of image processing to identify and characterize individual cells. For many workflows, this process starts with segmentation of nuclei that, due to their ubiquity, ease-of-labeling and relatively simple structure, make them appealing targets for automated detection of individual cells. However, in the context of large, three-dimensional image volumes, nuclei present many challenges to automated segmentation, such that conventional approaches are seldom effective and/or robust. Techniques based upon deep-learning have shown great promise, but enthusiasm for applying these techniques is tempered by the need to generate training data, an arduous task, particularly in three dimensions. Here we present results of a new technique of nuclear segmentation using neural networks trained on synthetic data. Comparisons with results obtained using commonly-used image processing packages demonstrate that DeepSynth provides the superior results associated with deep-learning techniques without the need for manual annotation. [ABSTRACT FROM AUTHOR]

Background: Premature birth is a growing and serious public health problem affecting more than one of every ten infants worldwide. Bronchopulmonary dysplasia (BPD) is the most common neonatal morbidity associated with prematurity and infants with BPD suffer from increased incidence of respiratory infections, asthma, other forms of chronic lung illness, and death (Day and Ryan, Pediatr Res 81: 210-213, 2017; Isayama et la., JAMA Pediatr 171:271-279, 2017). BPD is now understood as a longitudinal disease process influenced by the intrauterine environment during gestation and modulated by gene-environment interactions throughout the neonatal and early childhood periods. Despite of this concept, there remains a paucity of multidisciplinary team-based approaches dedicated to the comprehensive study of this complex disease.Methods: The Discovery BPD (D-BPD) Program involves a cohort of infants < 1,250 g at birth prospectively followed until 6 years of age. The program integrates analysis of detailed clinical data by machine learning, genetic susceptibility and molecular translation studies.Discussion: The current gap in understanding BPD as a complex multi-trait spectrum of different disease endotypes will be addressed by a bedside-to-bench and bench-to-bedside approach in the D-BPD program. The D-BPD will provide enhanced understanding of mechanisms, evolution and consequences of lung diseases in preterm infants. The D-BPD program represents a unique opportunity to combine the expertise of biologists, neonatologists, pulmonologists, geneticists and biostatisticians to examine the disease process from multiple perspectives with a singular goal of improving outcomes of premature infants.Trial Registration: Does not apply for this study. [ABSTRACT FROM AUTHOR]

Cancer remains the second leading cause of mortality worldwide, and overall cancer-related deaths are increasing. Despite the survival benefit from early detection, screening has to date targeted only those few organs that harbor tumors of sufficient prevalence to show cost-effectiveness at population levels, leaving most cancer types unscreened. In this perspective overview, a case is made for universal cancer screening as a logical and more inclusive approach with potentially high impact. The centrally important conceptual drivers to universal screening are biological and epidemiological. The shared biology of tumor marker release into a common distant medium, like blood, can be exploited for multi-cancer detection from a single test. And, by aggregating prevalence rates, universal screening allows all cancers (including less common ones) to be included as targets, increases screening efficiency and integration across tumor types, and potentially improves cost-effectiveness over single-organ approaches. The identification of new tumor marker classes with both broad expression across tumor types and site-prediction, remarkable advances in assay technologies, and compelling early clinical data increase the likelihood of actualizing this new paradigm. Multi-organ screening could be achieved by targeting markers within or stemming from the circulation (including blood, urine, saliva, and expired breath) or those exfoliated into common excretory pathways (including the gastrointestinal and female reproductive tracts). Rigorous clinical studies in intended use populations and collaborations between academia, industry, professional societies, and government will be required to bring this lofty vision to a population application. [ABSTRACT FROM AUTHOR]