AIMC Topic: Cellular Senescence
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Pan-cancer single cell and spatial transcriptomics analysis deciphers the molecular landscapes of senescence related cancer-associated fibroblasts and reveals its predictive value in neuroblastoma via integrated multi-omics analysis and machine learning.
Frontiers in immunology
Dec 5, 2024
INTRODUCTION: Cancer-associated fibroblasts (CAFs) are a diverse group of cells that significantly contribute to reshaping the tumor microenvironment (TME), and no research has systematically explored the molecular landscapes of senescence related CA...
Discovering geroprotectors through the explainable artificial intelligence-based platform AgeXtend.
Nature aging
Dec 3, 2024
Aging involves metabolic changes that lead to reduced cellular fitness, yet the role of many metabolites in aging is unclear. Understanding the mechanisms of known geroprotective molecules reveals insights into metabolic networks regulating aging and...
Deciphering the impact of senescence in kidney transplant rejection: An integrative machine learning and multi-omics analysis via bulk and single-cell RNA sequencing.
PloS one
Nov 27, 2024
BACKGROUND: The demographic shift towards an older population presents significant challenges for kidney transplantation (KTx), particularly due to the vulnerability of aged donor kidneys to ischemic damage, delayed graft function, and reduced graft ...
An object detection-based model for automated screening of stem-cells senescence during drug screening.
Neural networks : the official journal of the International Neural Network Society
Nov 23, 2024
Deep learning-based cell senescence detection is crucial for accurate quantitative analysis of senescence assessment. However, senescent cells are small in size and have little differences in appearance and shape in different states, which leads to i...
Identification of diagnostic genes and the miRNA‒mRNA‒TF regulatory network in human oocyte aging via machine learning methods.
Journal of assisted reproduction and genetics
Nov 14, 2024
PURPOSE: Oocyte aging is a significant factor in the negative reproductive outcomes of older women. However, the pathogenesis of oocyte aging remains unclear. This study aimed to identify the hub genes involved in oocyte aging via bioinformatics meth...
Integrated machine learning identifies a cellular senescence-related prognostic model to improve outcomes in uterine corpus endometrial carcinoma.
Frontiers in immunology
Jun 27, 2024
BACKGROUND: Uterine Corpus Endometrial Carcinoma (UCEC) stands as one of the prevalent malignancies impacting women globally. Given its heterogeneous nature, personalized therapeutic approaches are increasingly significant for optimizing patient outc...
D-MAINS: A Deep-Learning Model for the Label-Free Detection of Mitosis, Apoptosis, Interphase, Necrosis, and Senescence in Cancer Cells.
Cells
Jun 8, 2024
BACKGROUND: Identifying cells engaged in fundamental cellular processes, such as proliferation or living/death statuses, is pivotal across numerous research fields. However, prevailing methods relying on molecular biomarkers are constrained by high c...
Identification of key genes and biological pathways associated with vascular aging in diabetes based on bioinformatics and machine learning.
Aging
May 27, 2024
Vascular aging exacerbates diabetes-associated vascular damage, a major cause of microvascular and macrovascular complications. This study aimed to elucidate key genes and pathways underlying vascular aging in diabetes using integrated bioinformatics...
Identification of diagnostic markers related to inflammatory response and cellular senescence in endometriosis using machine learning and in vitro experiment.
Inflammation research : official journal of the European Histamine Research Society ... [et al.]
May 4, 2024
OBJECTIVE: To understand the association between chronic inflammation, cellular senescence, and immunological infiltration in endometriosis.
Single-cell senescence identification reveals senescence heterogeneity, trajectory, and modulators.
Cell metabolism
Apr 10, 2024
Cellular senescence underlies many aging-related pathologies, but its heterogeneity poses challenges for studying and targeting senescent cells. We present here a machine learning program senescent cell identification (SenCID), which accurately ident...
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