Observational study protocol for an arrhythmia notification feature.

INTRODUCTION: Screening for atrial fibrillation (AF) in the general population may help identify individuals at risk, enabling further assessment of risk factors and institution of appropriate treatment. Algorithms deployed on wearable technologies such as smartwatches and fitness bands may be trained to screen for such arrhythmias. However, their performance needs to be assessed for safety and accuracy prior to wide-scale implementation. METHODS AND ANALYSIS: This study will assess the ability of the WHOOP strap to detect AF using its WHOOP Arrhythmia Notification Feature (WARN) algorithm in an enriched cohort with a 2:1 distribution of previously diagnosed AF (persistent and paroxysmal) and healthy controls. Recruited participants will collect data for 7 days with the WHOOP wrist-strap and BioTel ePatch (electrocardiography gold-standard). Primary outcome will be participant level sensitivity and specificity of the WARN algorithm in detecting AF in analysable windows compared with the ECG gold-standard. Similar analyses will be performed on an available epoch-level basis as well as comparison of these findings in important subgroups. ETHICS AND DISSEMINATION: The study was approved by the ethics board at the study site. Participants will be enrolled after signing an online informed consent document. Updates will be shared via clinicaltrials.gov. The data obtained from the conclusion of this study will be presented in national and international conferences with publication in clinical research journals. TRIAL REGISTRATION NUMBER: NCT05809362.

Machine learning identifies T cell receptor repertoire signatures associated with COVID-19 severity.

T cell receptor (TCR) repertoires are critical for antiviral immunity. Determining the TCR repertoire composition, diversity, and dynamics and how they change during viral infection can inform the molecular specificity of host responses to viruses such as SARS-CoV-2. To determine signatures associated with COVID-19 disease severity, here we perform a large-scale analysis of over 4.7 billion sequences across 2130 TCR repertoires from COVID-19 patients and healthy donors. TCR repertoire analyses from these data identify and characterize convergent COVID-19-associated CDR3 gene usages, specificity groups, and sequence patterns. Here we show that T cell clonal expansion is associated with the upregulation of T cell effector function, TCR signaling, NF-kB signaling, and interferon-gamma signaling pathways. We also demonstrate that machine learning approaches accurately predict COVID-19 infection based on TCR sequence features, with certain high-power models reaching near-perfect AUROC scores. These analyses provide a systems immunology view of T cell adaptive immune responses to COVID-19.

Genome Engineering for Next-Generation Cellular Immunotherapies.

Over the past decade, cellular immunotherapies such as CAR-T, TCR-T, and NK cell therapies have achieved tremendous success in cancer treatment. However, various challenges and obstacles remain, including antigen escape, immunosuppression in the tumor microenvironment, toxicities, and on-target off-tumor effects. Recent strategies for overcoming these roadblocks have included the use of genome engineering. Multiplexed CRISPR-Cas and synthetic biology approaches facilitate the development of cell therapies with higher potency and sophisticated modular control; they also offer a toolkit for allogeneic therapy development. Engineering approaches have targeted genetic modifications to enhance long-term persistence through cytokine modulation, knockout of genes mediating immunosuppressive signals, and genes such as the endogenous TCR and MHC-I that elicit adverse host-graft interactions in an allogeneic context. Genome engineering approaches for other immune cell types are also being explored, such as CAR macrophages and CAR-NK cells. Future therapeutic development of cellular immunotherapies may also be guided by novel target discovery through unbiased CRISPR genetic screening approaches.