AIMC Topic: Cell Line, Tumor

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A Machine Learning-Driven Electrophysiological Platform for Real-Time Tumor-Neural Interaction Analysis and Modulation.

Nature communications
Neural-tumor electrophysiology-marked by pathological membrane potentials and ion channel dysregulation-emerges as actionable targets to curb tumor aggression. Yet, how neural-driven bioelectrical crosstalk dynamically regulates tumors within functio...

Magnetically Driven Lasing Microrobots for Precise Photodynamic Therapy.

ACS nano
Photodynamic therapy (PDT) is an emerging approach for tumor treatment, valued for its noninvasive and stimuli-responsive properties. However, its therapeutic efficacy is often constrained by unintended damage to healthy tissues, largely due to the s...

Entropy-driven signal amplification integrated with machine learning in multiplex lateral flow immunoassay for sensitive Point-of-Care colon cancer diagnosis.

Journal of nanobiotechnology
Investigations on epithelial-mesenchymal transition (EMT) events occurring on circulating tumor cells (CTCs) are poised to significantly advance nanoliquid biopsy methodologies. This study presented a colorimetric multiplex lateral flow immunoassay s...

Revealing the anti-tumor mechanisms of aromatic oil from Amomum villosum through integrated network pharmacology, bioinformatics, machine learning, single-cell sequencing, and cell experiments.

Biochemical and biophysical research communications
The dry fruits of Amomum villosum (Av) are a traditional Chinese medicine used for gastrointestinal disease. Aromatic oil has been reported to have anti-tumor properties. However, its therapeutic potential and molecular mechanisms remain unclear. Int...

Systems pharmacology approaches decipher the anti-cancer efficacy of ethnopharmacological agents in hepatocellular carcinoma.

Scientific reports
Hepatocellular carcinoma (HCC) poses a significant global health burden with limited therapeutic efficacy. Chinese herbal medicines (CHMs) offer multi-target potential, yet their systematic screening and mechanistic elucidation remain challenging. We...

An open-source screening platform accelerates discovery of drug combinations.

Nature communications
Drug combinations are essential to modern medicine, but their discovery remains slow and inefficient as experimental complexity expands rapidly with each additional drug tested. Although modern liquid handling systems enable complex and highly custom...

SHIFT-DRP: Dynamic Multi-Scale Active Learning for Drug Response Prediction.

Journal of chemical information and modeling
Deep learning models show promise for drug response prediction in personalized cancer treatment, but exhibit limited prediction capability for novel drug-cell line combinations due to insufficient coverage of the chemical spaces in training data. The...

The Clinical Prognostic Value of Lactylation-Regulated Proteins in Gastric Cancer.

Journal of proteome research
Gastric cancer (GC) is a leading cause of cancer-related mortality globally. Histone lactylation, an emerging post-translational modification, holds promise as a therapeutic target and prognostic biomarker, though its expression patterns and clinical...

Identification and validation of palmitoylation-related signature genes based on machine learning for prostate cancer.

PloS one
Prostate cancer (PCa) remains a leading cause of cancer-related mortality in men, with challenges in diagnosis and treatment due to tumor heterogeneity. This study identifies palmitoylation-related signature genes as potential diagnostic and therapeu...