AIMC Topic: Extracellular Matrix

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Deep learning reveals how cells pull, buckle, and navigate fibrous environments.

Proceedings of the National Academy of Sciences of the United States of America
Cells in tissues navigate fibrous environments fundamentally differently than they do on flat substrates, but the establishment of cell forces in physiological fibrous settings remains poorly understood. Although factors such as the stiffness of the ...

Decellularized tumor matrices as biomimetic cancer niche: a new perspective on cancer research and therapy.

Biomedical materials (Bristol, England)
Cancer is among the major causes of mortality, responsible for approximately 15% of all deaths worldwide. Despite remarkable progress in modern medicine, it remains a significant global health challenge. Nevertheless, conventional therapies such as c...

AI-driven discovery of novel extracellular matrix biomarkers in pelvic organ prolapse.

PLoS computational biology
Deep learning for protein function prediction faces significant challenges in identifying disease-specific proteins. We present Extracellular Matrix Protein Predictor (EPOP), an advanced transfer learning framework leveraging protein language models ...

Deep learning-powered high-efficient atomic force microscopy single-cell nanomechanical analysis on diverse biointerfaces.

Biochemical and biophysical research communications
The extracellular matrix (ECM) is crucial in tuning cellular behavior, and quantifying cellular mechanical changes in response to ECM stimuli can help reveal the underlying physical mechanisms of cell-ECM interactions for a comprehensive understandin...

Recellularization of scaffolds derived from precision-cut kidney slices.

Biomedical materials (Bristol, England)
The global rise in chronic kidney disease necessitates innovative solutions for end-stage renal disease that can help to overcome the limitations of the only available treatment options, transplantation and dialysis. Tissue engineering presents a pro...

Identification of core genes in the extracellular matrix and the regulatory mechanisms of the immune microenvironment in idiopathic pulmonary fibrosis using WGCNA and machine learning methods.

PloS one
OBJECTIVE: This research aims to detect genes associated with the extracellular matrix (ECM) in idiopathic pulmonary fibrosis (IPF) using bioinformatics techniques and investigate their relationships with immune infiltration, with the goal of identif...

Quantitative image analysis of the extracellular matrix of esophageal squamous cell carcinoma and high grade dysplasia via two-photon microscopy.

Scientific reports
Squamous cell carcinoma (SCC) and high-grade dysplasia (HGD) are two different pathological entities; however, they sometimes share similarities in histological structure depending on the context. Thus, distinguishing between the two may require care...

Machine learning derived development and validation of extracellular matrix related signature for predicting prognosis in adolescents and young adults glioma.

Scientific reports
The mortality rates have been increasing for glioma in adolescents and young adults (AYAs, aged 15-39 years). However, current biomarkers for clinical assessment in AYAs glioma are limited, prompting the urgent need for identifying ideal prognostic s...

Machine Learning-Enhanced Single-Particle Tracking for Rapid Screening of Tumor Immunomodulatory Drugs.

ACS nano
The tumor microenvironment plays a critical role in tumor progression and immune response, with the extracellular matrix (ECM) regulating immune cell infiltration. However, the interplay between ECM dynamics and tumor immunity remains poorly understo...

FIRM image analysis: A machine learning workflow for quantifying extracellular matrix components from electron microscopy images.

PloS one
The extracellular matrix (ECM) is a complex network of biomolecules that plays an integral role in the structure, processes, and signaling mechanisms of cells and tissues. Identifying and quantifying changes in these matrix components provides insigh...