AIMC Topic: Cellular Senescence
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Nuclear morphology is a deep learning biomarker of cellular senescence.
Nature aging
Aug 15, 2022
Cellular senescence is an important factor in aging and many age-related diseases, but understanding its role in health is challenging due to the lack of exclusive or universal markers. Using neural networks, we predict senescence from the nuclear mo...
Simple Detection of Unstained Live Senescent Cells with Imaging Flow Cytometry.
Cells
Aug 12, 2022
Cellular senescence is a hallmark of aging and a promising target for therapeutic approaches. The identification of senescent cells requires multiple biomarkers and complex experimental procedures, resulting in increased variability and reduced sensi...
Automated characterisation of microglia in ageing mice using image processing and supervised machine learning algorithms.
Scientific reports
Feb 2, 2022
The resident macrophages of the central nervous system, microglia, are becoming increasingly implicated as active participants in neuropathology and ageing. Their diverse and changeable morphology is tightly linked with functions they perform, enabli...
Anti-senescent drug screening by deep learning-based morphology senescence scoring.
Nature communications
Jan 11, 2021
Advances in deep learning technology have enabled complex task solutions. The accuracy of image classification tasks has improved owing to the establishment of convolutional neural networks (CNN). Cellular senescence is a hallmark of ageing and is im...
ARDD 2020: from aging mechanisms to interventions.
Aging
Dec 30, 2020
Aging is emerging as a druggable target with growing interest from academia, industry and investors. New technologies such as artificial intelligence and advanced screening techniques, as well as a strong influence from the industry sector may lead t...
Predicting direct hepatocyte toxicity in humans by combining high-throughput imaging of HepaRG cells and machine learning-based phenotypic profiling.
Archives of toxicology
Jun 12, 2020
Accurate prediction of drug- and chemical-induced hepatotoxicity remains to be a problem for pharmaceutical companies as well as other industries and regulators. The goal of the current study was to develop an in vitro/in silico method for the rapid ...
Chemical and Drug Induced Liver Injury
Primary Cell Culture
Actin Cytoskeleton
Dose-Response Relationship, Drug
Risk Assessment
High-Throughput Screening Assays
Proto-Oncogene Mas
DNA Damage
Toxicity Tests
Humans
Cellular Senescence
Image Processing, Computer-Assisted
Microscopy, Fluorescence
Transcription Factor RelA
Machine Learning
Cell Death
Hep G2 Cells
Phenotype
Hepatocytes
Advancing T-cell immunotherapy for cellular senescence and disease: Mechanisms, challenges, and clinical prospects.
Ageing research reviews
Jul 1, 2025
Cellular senescence is a complex biological process with a dual role in tissue homeostasis and aging-related pathologies. Accumulation of senescent cells promotes chronic inflammation, tissue dysfunction, age-related diseases, and tumor suppression. ...
The promise of machine learning approaches to capture cellular senescence heterogeneity.
Nature aging
Sep 1, 2024
Unraveling the complexity of the senescence-associated secretory phenotype in adamantinomatous craniopharyngioma using multimodal machine learning analysis.
Neuro-oncology
Jun 3, 2024
BACKGROUND: Cellular senescence can have positive and negative effects on the body, including aiding in damage repair and facilitating tumor growth. Adamantinomatous craniopharyngioma (ACP), the most common pediatric sellar/suprasellar brain tumor, p...
[Identification of Osteoarthritis Inflamm-Aging Biomarkers by Integrating Bioinformatic Analysis and Machine Learning Strategies and the Clinical Validation].
Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition
Mar 20, 2024
OBJECTIVE: To identify inflamm-aging related biomarkers in osteoarthritis (OA).
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