Messenger RNA vaccines against SARS-CoV-2.

The first two vaccines proven to be effective for inhibiting COVID-19 illness were both mRNA, achieving 95% efficacy (and safety) among 74,000 participants (half receiving placebo) after intramuscular delivery of two shots, 3-4 weeks apart. To view this Bench to Bedside, open or download the PDF.

Unveiling the Role of the Most Impactful Cardiovascular Risk Locus through Haplotype Editing.

The 9p21.3 cardiovascular disease locus is the most influential common genetic risk factor for coronary artery disease (CAD), accounting for ∼10%-15% of disease in non-African populations. The ∼60 kb risk haplotype is human-specific and lacks coding genes, hindering efforts to decipher its function. Here, we produce induced pluripotent stem cells (iPSCs) from risk and non-risk individuals, delete each haplotype using genome editing, and generate vascular smooth muscle cells (VSMCs). Risk VSMCs exhibit globally altered transcriptional networks that intersect with previously identified CAD risk genes and pathways, concomitant with aberrant adhesion, contraction, and proliferation. Unexpectedly, deleting the risk haplotype rescues VSMC stability, while expressing the 9p21.3-associated long non-coding RNA ANRIL induces risk phenotypes in non-risk VSMCs. This study shows that the risk haplotype selectively predisposes VSMCs to adopt a cell state associated with CAD phenotypes, defines new VSMC-based networks of CAD risk genes, and establishes haplotype-edited iPSCs as powerful tools for functionally annotating the human genome.

High-Definition Medicine.

The foundation for a new era of data-driven medicine has been set by recent technological advances that enable the assessment and management of human health at an unprecedented level of resolution-what we refer to as high-definition medicine. Our ability to assess human health in high definition is enabled, in part, by advances in DNA sequencing, physiological and environmental monitoring, advanced imaging, and behavioral tracking. Our ability to understand and act upon these observations at equally high precision is driven by advances in genome editing, cellular reprogramming, tissue engineering, and information technologies, especially artificial intelligence. In this review, we will examine the core disciplines that enable high-definition medicine and project how these technologies will alter the future of medicine.

Whole-Genome Sequencing of a Healthy Aging Cohort.

Studies of long-lived individuals have revealed few genetic mechanisms for protection against age-associated disease. Therefore, we pursued genome sequencing of a related phenotype-healthy aging-to understand the genetics of disease-free aging without medical intervention. In contrast with studies of exceptional longevity, usually focused on centenarians, healthy aging is not associated with known longevity variants, but is associated with reduced genetic susceptibility to Alzheimer and coronary artery disease. Additionally, healthy aging is not associated with a decreased rate of rare pathogenic variants, potentially indicating the presence of disease-resistance factors. In keeping with this possibility, we identify suggestive common and rare variant genetic associations implying that protection against cognitive decline is a genetic component of healthy aging. These findings, based on a relatively small cohort, require independent replication. Overall, our results suggest healthy aging is an overlapping but distinct phenotype from exceptional longevity that may be enriched with disease-protective genetic factors. VIDEO ABSTRACT.

Individualized medicine from prewomb to tomb.

That each of us is truly biologically unique, extending to even monozygotic, "identical" twins, is not fully appreciated. Now that it is possible to perform a comprehensive "omic" assessment of an individual, including one's DNA and RNA sequence and at least some characterization of one's proteome, metabolome, microbiome, autoantibodies, and epigenome, it has become abundantly clear that each of us has truly one-of-a-kind biological content. Well beyond the allure of the matchless fingerprint or snowflake concept, these singular, individual data and information set up a remarkable and unprecedented opportunity to improve medical treatment and develop preventive strategies to preserve health.

Foundation models for generalist medical artificial intelligence.

The exceptionally rapid development of highly flexible, reusable artificial intelligence (AI) models is likely to usher in newfound capabilities in medicine. We propose a new paradigm for medical AI, which we refer to as generalist medical AI (GMAI). GMAI models will be capable of carrying out a diverse set of tasks using very little or no task-specific labelled data. Built through self-supervision on large, diverse datasets, GMAI will flexibly interpret different combinations of medical modalities, including data from imaging, electronic health records, laboratory results, genomics, graphs or medical text. Models will in turn produce expressive outputs such as free-text explanations, spoken recommendations or image annotations that demonstrate advanced medical reasoning abilities. Here we identify a set of high-impact potential applications for GMAI and lay out specific technical capabilities and training datasets necessary to enable them. We expect that GMAI-enabled applications will challenge current strategies for regulating and validating AI devices for medicine and will shift practices associated with the collection of large medical datasets.

A foundation model for generalizable disease detection from retinal images.

Medical artificial intelligence (AI) offers great potential for recognizing signs of health conditions in retinal images and expediting the diagnosis of eye diseases and systemic disorders1. However, the development of AI models requires substantial annotation and models are usually task-specific with limited generalizability to different clinical applications2. Here, we present RETFound, a foundation model for retinal images that learns generalizable representations from unlabelled retinal images and provides a basis for label-efficient model adaptation in several applications. Specifically, RETFound is trained on 1.6 million unlabelled retinal images by means of self-supervised learning and then adapted to disease detection tasks with explicit labels. We show that adapted RETFound consistently outperforms several comparison models in the diagnosis and prognosis of sight-threatening eye diseases, as well as incident prediction of complex systemic disorders such as heart failure and myocardial infarction with fewer labelled data. RETFound provides a generalizable solution to improve model performance and alleviate the annotation workload of experts to enable broad clinical AI applications from retinal imaging.

The personal and clinical utility of polygenic risk scores.

Initial expectations for genome-wide association studies were high, as such studies promised to rapidly transform personalized medicine with individualized disease risk predictions, prevention strategies and treatments. Early findings, however, revealed a more complex genetic architecture than was anticipated for most common diseases - complexity that seemed to limit the immediate utility of these findings. As a result, the practice of utilizing the DNA of an individual to predict disease has been judged to provide little to no useful information. Nevertheless, recent efforts have begun to demonstrate the utility of polygenic risk profiling to identify groups of individuals who could benefit from the knowledge of their probabilistic susceptibility to disease. In this context, we review the evidence supporting the personal and clinical utility of polygenic risk profiling.

The Proportion of SARS-CoV-2 Infections That Are Asymptomatic : A Systematic Review.

BACKGROUND: Asymptomatic infection seems to be a notable feature of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the pathogen that causes coronavirus disease 2019 (COVID-19), but the prevalence is uncertain. PURPOSE: To estimate the proportion of persons infected with SARS-CoV-2 who never develop symptoms. DATA SOURCES: Searches of Google News, Google Scholar, medRxiv, and PubMed using the keywords antibodies, asymptomatic, coronavirus, COVID-19, PCR, seroprevalence, and SARS-CoV-2. STUDY SELECTION: Observational, descriptive studies and reports of mass screening for SARS-CoV-2 that were either cross-sectional or longitudinal in design; were published through 17 November 2020; and involved SARS-CoV-2 nucleic acid or antibody testing of a target population, regardless of current symptomatic status, over a defined period. DATA EXTRACTION: The authors collaboratively extracted data on the study design, type of testing performed, number of participants, criteria for determining symptomatic status, testing results, and setting. DATA SYNTHESIS: Sixty-one eligible studies and reports were identified, of which 43 used polymerase chain reaction (PCR) testing of nasopharyngeal swabs to detect current SARS-CoV-2 infection and 18 used antibody testing to detect current or prior infection. In the 14 studies with longitudinal data that reported information on the evolution of symptomatic status, nearly three quarters of persons who tested positive but had no symptoms at the time of testing remained asymptomatic. The highest-quality evidence comes from nationwide, representative serosurveys of England (n = 365 104) and Spain (n = 61 075), which suggest that at least one third of SARS-CoV-2 infections are asymptomatic. LIMITATION: For PCR-based studies, data are limited to distinguish presymptomatic from asymptomatic infection. Heterogeneity precluded formal quantitative syntheses. CONCLUSION: Available data suggest that at least one third of SARS-CoV-2 infections are asymptomatic. Longitudinal studies suggest that nearly three quarters of persons who receive a positive PCR test result but have no symptoms at the time of testing will remain asymptomatic. Control strategies for COVID-19 should be altered, taking into account the prevalence and transmission risk of asymptomatic SARS-CoV-2 infection. PRIMARY FUNDING SOURCE: National Institutes of Health.

Protective alleles and modifier variants in human health and disease.

The combination of next-generation sequencing technologies and high-throughput genotyping platforms has revolutionized the pursuit of genetic variants that contribute towards disease. Furthermore, these technologies have provided invaluable insight into the genetic factors that prevent individuals from developing disease. Exploiting the evolutionary mechanisms that were designed by nature to help prevent disease is an attractive line of enquiry. Such efforts have the potential to generate a therapeutic target roadmap and rejuvenate the current drug-discovery pathway. By delineating the genomic factors that are protective against disease, there is potential to derive highly effective, genomically anchored medicines that assist in maintaining health.

Prevalence of Asymptomatic SARS-CoV-2 Infection : A Narrative Review.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread rapidly throughout the world since the first cases of coronavirus disease 2019 (COVID-19) were observed in December 2019 in Wuhan, China. It has been suspected that infected persons who remain asymptomatic play a significant role in the ongoing pandemic, but their relative number and effect have been uncertain. The authors sought to review and synthesize the available evidence on asymptomatic SARS-CoV-2 infection. Asymptomatic persons seem to account for approximately 40% to 45% of SARS-CoV-2 infections, and they can transmit the virus to others for an extended period, perhaps longer than 14 days. Asymptomatic infection may be associated with subclinical lung abnormalities, as detected by computed tomography. Because of the high risk for silent spread by asymptomatic persons, it is imperative that testing programs include those without symptoms. To supplement conventional diagnostic testing, which is constrained by capacity, cost, and its one-off nature, innovative tactics for public health surveillance, such as crowdsourcing digital wearable data and monitoring sewage sludge, might be helpful.