Evaluation of a machine learning-based metabolic marker for coronary artery disease in the UK Biobank.

Background And Aims: An in silico quantitative score of coronary artery disease (ISCAD), built using machine learning and clinical data from electronic health records, has been shown to result in gradations of risk of subclinical atherosclerosis, coronary artery disease (CAD) sequelae, and mortality. Large-scale metabolite biomarker profiling provides increased portability and objectivity in machine learning for disease prediction and gradation. However, these models have not been fully leveraged.

The role of gene copy number variation in antimicrobial resistance in human fungal pathogens.

Faced with the burden of increasing resistance to antifungals in many fungal pathogens and the constant emergence of new drug-resistant strains, it is essential to assess the importance of various resistance mechanisms. Fungi have relatively plastic genomes and can tolerate genomic copy number variation (CNV) caused by aneuploidy and gene amplification or deletion. In many cases, these genomic changes lead to adaptation to stressful conditions, including those caused by antifungal drugs.

Development of a Body of Knowledge for the Clinical Bioinformatician: Perspectives from the Association for Molecular Pathology's Clinical Genomics Bioinformatician Body of Knowledge Steering Committee.

The use of next-generation sequencing and other high-throughput technologies in the clinical molecular diagnostics laboratory requires the application of bioinformatics pipelines and other computational tools to analyze, visualize, and store these clinical data. Clinical bioinformaticians, individuals with the skills to develop, validate, and deploy these tools in a clinical setting, are needed to ensure that these molecular diagnostic technologies can be appropriately used for clinical care. Building on existing expertise in informatics, next-generation sequencing, and clinical molecular diagnostics, the Association for Molecular Pathology has generated a series to establish an initial clinical bioinformatician body of knowledge.

Multi-omic profiles of Sorghum genotypes with contrasting heat tolerance connect pathways related to thermotolerance.

Understanding how crop varieties acclimate to elevated temperatures is key to priming them for future climates. Here, we exposed two genotypes of Sorghum bicolor (one sensitive to heat shock (Sen) and one tolerant (Tol)) from multiple growth temperatures to a six-day heat shock (reaching 45°C), carrying out a suite of measurements before and during heat shock. Sen consistently reduced photosynthetic functioning during heat shock, while Tol increased its photosynthetic rate.