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Hadley et al. characterize COVID-19 re-infections utilizing electronic health record study cohort data of over 3 million patients. They find severe initial COVID-19 infection linked to severe reinfections and less frequent long COVID diagnosis after reinfection.
Zang et al. use RECOVER EHR data to study Long COVID risk factors and apply mathematical modeling to predict the development of long COVID conditions. They find that severe acute SARS-CoV-2 infection, being underweight, and having baseline comorbidities are likely associated with increased risk of having Long COVID.
Han et al. present a framework to map the clusters of multimorbidity progression by examining the bi-directional causal progress between hundreds of diseases. This provides a comprehensive picture of how one disease influences and is influenced by the development of others, and how they form clusters of disease trajectories.
Somani, Jain et al. evaluate public perceptions of glucagon-like peptide-1 receptor agonists with large language model-based analysis of social media posts. Sentiments were neutral-to-positive, and discussions included use in weight loss, side effects, and issues with access and supply.
Sulaiman et al. performed a systematic review and meta-analysis of the evidence on global prevalence and correlates of mpox vaccine acceptance and uptake in different populations. They show large variation in vaccination rates across the six WHO global regions for different population sub-groups, suggesting a tailored approach to vaccination.
Pietzner et al. demonstrate the need for a holistic approach to identify people at highest risk for severe COVID-19 based on integrating health records across primary and secondary care. Integrating human genetic evidence, they find that the symptoms of malaise and fatigue are a largely unrecognized risk factor for severe COVID-19.
Alcolea-Medina et al. develop a rapid, unified metagenomics method for the detection of bacteria, fungi, and viruses from clinical respiratory samples. This method reduces human DNA content, allowing efficient pathogen detection and possibility of same-day reporting to meet requirements of clinical implementation.
Klimek et al. perform a simulation study using SARS-CoV-2 epidemic network models. They show that, under some specific epidemic conditions, the risk of infection at the individual level may be lower when fewer people who are closely connected wear masks, compared to when more people who are not in close contact do so
Holste et al. present a deep learning method for efficient cardiac disease diagnosis from echocardiography videos. Their method, EchoCLR, uses self-supervised learning to accurately detect heart diseases from a smaller number of labeled examples than existing methods.
Ang et al. present the results from analysing the hourly step patterns in a population-wide physical activity programme. Recommended hourly targets to enable daily step goals to be achieved vary based on different participants’ characteristics.
Black et al apply machine learning techniques to a large dataset of endmember abundance vectors derived from hyperspectral fluorescence measurements of brain tumors. They show that it is possible in this way to classify various tissue types, grades, and biomarkers with a high degree of accuracy.
Huang, Yoon et al. provide a comprehensive single cell protein atlas of CEACAM1 expression on immune cells in human melanoma samples. Their analysis identifies an association between increased CEACAM1 expression and treatment-resistant disease.
Summan et al. estimate changes in anthropometric outcomes in Indian children during the COVID-19 pandemic. They find short-term increased adverse growth events such as stunting and lower weight with trends towards recovery in 2021.
Yang, Zhou et al. undertake a nationwide cross-sectional study of weather and mortality. Mortality risks of compound daytime-nighttime temperature extremes are stronger than those occurring only in daytime or nighttime, with patients with ischemic heart disease and respiratory disease being particularly vulnerable to adverse temperature events.
Aksnes et al. evaluate the association between matrix metalloproteinases and their tissue inhibitors with delirium. Multivariate regression analyses find that while most associations are explained by acute trauma or pre-existing cognitive impairment, low TIMP-4 could be directly linked to delirium.
Hossain, Lungu et al. develop SQuHIVLa, a sensitive and specific method to quantify the frequency of cells expressing HIV-1 tat/rev msRNA as a surrogate for the inducible HIV-1 reservoir. SQuHIVLa offers a high throughput, scalable HIV-1 reservoir quantitation tool that is amenable to resource-limited settings.
Haag et al. examine the theories of persons with multiple sclerosis (MS) about the causes of MS using natural language processing techniques. Their study identifies unique topics and highlights the role of mental health and the need for an open dialogue about the scientific and mental health aspects of MS.
Zismanov, Shalem, Margolin-Miller et al. present the validation and clinical rollout of P-BEST, a combinatorial single-stage pooling method used for mass testing during the SARS-CoV-2 pandemic in Israel. The analysis is a proof-of-concept for the feasibility of using combinatorial pooling solutions for mass testing in clinical settings.
Chenoweth et al. investigate heterogeneity in sepsis using host gene expression from multi-site international cohorts. Endotypes differentiated by mortality are identified and related molecular signatures implicate immune dysfunction in sepsis pathophysiology.
Rabea et al. assess spinal muscular atrophy (SMA) incidence in Arab countries where rates of consanguinity are high. Findings from SMA newborn screening in Emiratis underscore a need for universal premarital screening.