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http://dx.doi.org/10.1055/s-0029-1244184DOI Listing

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SampleExplorer: Using language models to discover relevant transcriptome data.

Bioinformatics

December 2024

The Kids Research Institute Australia, University of Western Australia, Nedlands, WA 6009, Australia.

Motivation: Over the last two decades, transcriptomics has become a standard technique in biomedical research. We now have large databases of RNA-seq data, accompanied by valuable metadata detailing scientific objectives and the experimental procedures employed. The metadata is crucial in understanding and replicating published studies, but so far has been underutilised in helping researchers to discover existing datasets.

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Background: Exposure to asbestos in the workplace is currently recognized as one of the leading causes of work-related deaths, with more than half of deaths attributable to cancer.

Aims: The aim of this systematic literature review was to investigate the mental health and psychological distress of patients affected by asbestos-related diseases and their caregivers.

Methods: The review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses.

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Combination immune checkpoint inhibitors (nivolumab and ipilimumab) are currently a first-line treatment for mesothelioma; however, not all patients respond. The efficacy of treatment is influenced by the tumor microenvironment. Murine mesothelioma tumors were irritated with various radiotherapy doses.

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Cancers of the mesothelium, such as malignant mesothelioma (MM), historically have been attributed solely to exposure to asbestos. Recent large scale genetic and genomic functional studies now show that approximately 20% of all human mesotheliomas are causally linked to highly penetrant inherited (germline) pathogenic mutations in numerous cancer related genes. The rarity of these mutations in humans makes it difficult to perform statistically conclusive genetic studies to understand their biological effects.

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