Youth, working age and the elderly: On a timeline, chronological age (CA) and biological age (BA) may dissociate; nosological entities manifest themselves at different BAs. In determining which disease corresponds to a given age decade, statistical registries of causes of death are unreliable and this does not change with SARS CoV-2 infection. Beyond adolescence, ageing metrics involve estimations of changes in fitness, including prediction models to estimate the number of remaining years left to live. A substantial disparity in biomarker levels and health status of ageing can be observed: the difference in CA and BA in the large cohorts under consideration is glaring. Here, we focus more closely on ageing and senescence metrics in order to make information available for risk analysis non the least with COVID-19, including the most recent risk factors of ABO blood type and 3p21.31 chromosome cluster impacting on C5a and SC5b-9 plasma levels. From the multitude of routine medical laboratory assays, a potentially meaningful set of assays aimed to best reflect the stage of individual senescence; hence risk factors the observational prospective SENIORLABOR study of 1,467 healthy elderly performed since 2009 and similar approaches since 1958 can be instantiated as a network to combine a set of elementary laboratory assays quantifying senescence.
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http://dx.doi.org/10.1159/000517659 | DOI Listing |
J Am Soc Nephrol
January 2025
State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, 210009, China.
Background: Cardiac surgery-associated acute kidney injury is a common serious complication after cardiac surgery. Currently, there are no specific pharmacological therapies. Our understanding of its pathophysiology remains preliminary.
View Article and Find Full Text PDFSyst Biol
January 2025
Simon F. S. Li Marine Science Laboratory, School of Life Sciences and State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Shatin, Hong Kong SAR.
Obtaining a timescale for bacterial evolution is crucial to understand early life evolution but is difficult owing to the scarcity of bacterial fossils. Here, we introduce multiple new time constraints to calibrate bacterial evolution based on ancient symbiosis. This idea is implemented using a bacterial tree constructed with genes found in the mitochondrial lineages phylogenetically embedded within Proteobacteria.
View Article and Find Full Text PDFBrief Bioinform
November 2024
Department of Automation, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang District, Shanghai 200240, China.
Studying the changes in cellular transcriptional profiles induced by small molecules can significantly advance our understanding of cellular state alterations and response mechanisms under chemical perturbations, which plays a crucial role in drug discovery and screening processes. Considering that experimental measurements need substantial time and cost, we developed a deep learning-based method called Molecule-induced Transcriptional Change Predictor (MiTCP) to predict changes in transcriptional profiles (CTPs) of 978 landmark genes induced by molecules. MiTCP utilizes graph neural network-based approaches to simultaneously model molecular structure representation and gene co-expression relationships, and integrates them for CTP prediction.
View Article and Find Full Text PDFNeuro Oncol
January 2025
Department of Neurosurgery, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg.
Background: Peripheral nerve sheath tumors (PNSTs) encompass entities with different cellular differentiation and degrees of malignancy. Spatial heterogeneity complicates diagnosis and grading of PNSTs in some cases. In malignant PNST (MPNST) for example, single cell sequencing data has shown dissimilar differentiation states of tumor cells.
View Article and Find Full Text PDFJ Clin Invest
January 2025
Laboratory of Genome Dynamics in the Immune, INSERM UMR 116, Équipe Labellisée LIGUE 2023, Paris, France.
Oncostatin M (OSM) is a cytokine with the unique ability to interact with both the OSM receptor (OSMR) and the leukemia inhibitory factor receptor (LIFR). On the other hand, OSMR interacts with IL31RA to form the interleukin-31 receptor. This intricate network of cytokines and receptors makes it difficult to understand the specific function of OSM.
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