causes the infection. The model predicts that individuals with short telomeres, principally seniors, might be at a higher risk of death from COVID-19.Declining immunity with advancing age is a general explanation for the increased mortality from COVID-19 among older adults. This mortality far exceeds that from viral illnesses such as the seasonal influenza, and it thus requires specific explanations. One of these might be diminished ability with age to offset the development of severe T-cell lymphopenia (a low T-cell count in the blood) that often complicates COVID-19. We constructed a model showing that age-dependent shortening of telomeres might constrain the ability of T-cells of some older COVID-19 patients to undertake the massive proliferation required to clear the virus that causes the infection. The model predicts that individuals with short telomeres, principally seniors, might be at a higher risk of death from COVID-19.One of the most critical challenges in managing complex diseases like COVID-19 is to establish an intelligent triage system that can optimize the clinical decision-making at the time of a global pandemic. The clinical presentation and patients’ characteristics are usually utilized to identify those patients who need more critical care. However, the clinical evidence shows an unmet need to determine more accurate and optimal clinical biomarkers to triage patients under a condition like the COVID-19 crisis. Here we have presented a machine learning approach to find a group of clinical indicators from the blood tests of a set of COVID-19 patients that are predictive of poor prognosis and morbidity. Our approach consists of two interconnected schemes Feature Selection and Prognosis Classification. The former is based on different Matrix Factorization (MF)-based methods, and the latter is performed using Random Forest algorithm. Our model reveals that Arterial Blood Gas (ABG) O 2 Saturation and C-Reactive Protein (CRP) are the most important clinical biomarkers determining the poor prognosis in these patients. Our approach paves the path of building quantitative and optimized clinical management systems for COVID-19 and similar diseases. We aimed to measure SARS-CoV-2 seroprevalence in a cohort of healthcare workers (HCWs) during the first UK wave of the COVID-19 pandemic, explore risk factors associated with infection, and investigate the impact of antibody titres on assay sensitivity. HCWs at Sheffield Teaching Hospitals NHS Foundation Trust (STH) were prospectively enrolled and sampled at two time points. SARS-CoV-2 antibodies were tested using an in-house assay for IgG and IgA reactivity against Spike and Nucleoprotein (sensitivity 99·47%, specificity 99·56%). Data were analysed using three statistical models a seroprevalence model, an antibody kinetics model, and a heterogeneous sensitivity model. As of 12th June 2020, 24·4% (n=311/1275) HCWs were seropositive. Of these, 39·2% (n=122/311) were asymptomatic. The highest adjusted seroprevalence was measured in HCWs on the Acute Medical Unit (41·1%, 95% CrI 30·0-52·9) and in Physiotherapists and Occupational Therapists (39·2%, 95% CrI 24·4-56·5). Older age groups showed overall higherOVID-19 infection to healthcare workers is greatest, and this knowledge should be used to prioritise infection prevention control and other measures to protect healthcare workers. Serological assays may have different sensitivity profiles across different age groups, especially if assay validation was undertaken using samples from older and/or hospitalised patients, who tend to have higher antibody titres. Future seroprevalence studies should consider adjusting for age-specific assay sensitivities to estimate true seroprevalence rates.SARS-CoV-2 mRNA vaccines are highly effective, although weak antibody responses are seen in some individuals with correlates of immunity that remain poorly understood. MC3 order Here we longitudinally dissected antibody, plasmablast, and memory B cell (MBC) responses to the two-dose Moderna mRNA vaccine in SARS-CoV-2-uninfected adults. Robust, coordinated IgA and IgG antibody responses were preceded by bursts of spike-specific plasmablasts after both doses, but earlier and more intensely after dose two. Distinct antigen-specific MBC populations also emerged post-vaccination with varying kinetics. We identified antigen non-specific pre-vaccination MBC and post-vaccination plasmablasts after dose one and their spike-specific counterparts early after dose two that correlated with subsequent antibody levels. These baseline and response signatures can thus provide early indicators of serological efficacy and explain response variability in the population. Ground-glass opacity (GGO) - a hazy, gray appearing density on computed tomography (CT) of lungs - is one of the hallmark features of SARS-CoV-2 in COVID-19 patients. This AI-driven study is focused on segmentation, morphology, and distribution patterns of GGOs. We use an AI-driven unsupervised machine learning approach called PointNet++ to detect and quantify GGOs in CT scans of COVID-19 patients and to assess the severity of the disease. We have conducted our study on the "MosMedData", which contains CT lung scans of 1110 patients with or without COVID-19 infections. We quantify the morphologies of GGOs using Minkowski tensors and compute the abnormality score of individual regions of segmented lung and GGOs. PointNet++ detects GGOs with the highest evaluation accuracy (98%), average class accuracy (95%), and intersection over union (92%) using only a fraction of 3D data. On average, the shapes of GGOs in the COVID-19 datasets deviate from sphericity by 15% and anisotropies in GGOs are dominated by diGGOs within the COVID-19 cohort.Methods of antibody detection are used to assess exposure or immunity to a pathogen. Here, we present Ig-MS , a novel serological readout that captures the immunoglobulin (Ig) repertoire at molecular resolution, including entire variable regions in Ig light and heavy chains. Ig-MS uses recent advances in protein mass spectrometry (MS) for multi-parametric readout of antibodies, with new metrics like Ion Titer (IT) and Degree of Clonality (DoC) capturing the heterogeneity and relative abundance of individual clones without sequencing of B cells. We apply Ig-MS to plasma from subjects with severe & mild COVID-19, using the receptor-binding domain (RBD) of the spike protein of SARS-CoV-2 as the bait for antibody capture. Importantly, we report a new data type for human serology, with compatibility to any recombinant antigen to gauge our immune responses to vaccination, pathogens, or autoimmune disorders.