AIMC Topic: Cell Line, Tumor
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Sonopermeation combined with stroma normalization enables complete cure using nano-immunotherapy in murine breast tumors.
Journal of controlled release : official journal of the Controlled Release Society
Apr 14, 2025
Nano-immunotherapy shows great promise in improving patient outcomes, as seen in advanced triple-negative breast cancer, but it does not cure the disease, with median survival under two years. Therefore, understanding resistance mechanisms and develo...
Engineering TCR-controlled fuzzy logic into CAR T cells enhances therapeutic specificity.
Cell
Apr 11, 2025
Chimeric antigen receptor (CAR) T cell immunotherapy represents a breakthrough in the treatment of hematological malignancies, but poor specificity has limited its applicability to solid tumors. By contrast, natural T cells harboring T cell receptors...
MLG2Net: Molecular Global Graph Network for Drug Response Prediction in Lung Cancer Cell Lines.
Journal of medical systems
Apr 10, 2025
Drug response prediction (DRP) is a central task in the era of precision medicine. Over the past decade, the emergence of deep learning (DL) has greatly contributed to addressing DRP challenges. Notably, the prediction of DRP for cancer cell lines be...
Comprehensive analysis of scRNA-seq and bulk RNA-seq data via machine learning and bioinformatics reveals the role of lysine metabolism-related genes in gastric carcinogenesis.
BMC cancer
Apr 9, 2025
BACKGROUND: Gastric cancer (GC) is a highly aggressive and heterogeneous cancer with extremely complex biological characteristics. Lysine and its metabolism are closely related to human cancer, but little is known about how lysine metabolism-related ...
Carcinogenesis
Vimentin
Lysine
Sequence Analysis, RNA
Gene Expression Regulation, Neoplastic
Nomograms
Cell Proliferation
Gene Regulatory Networks
Single-Cell Gene Expression Analysis
Computational Biology
Humans
Machine Learning
Prognosis
Stomach Neoplasms
Biomarkers, Tumor
Cell Line, Tumor
Single-Cell Analysis
RNA-Seq
Combining multi-omics analysis with machine learning to uncover novel molecular subtypes, prognostic markers, and insights into immunotherapy for melanoma.
BMC cancer
Apr 7, 2025
BACKGROUND: Melanoma (SKCM) is an extremely aggressive form of cancer, characterized by high mortality rates, frequent metastasis, and limited treatment options. Our study aims to identify key target genes and enhance the diagnostic accuracy of melan...
Identification and validation of HOXC6 as a diagnostic biomarker for Ewing sarcoma: insights from machine learning algorithms and experiments.
Frontiers in immunology
Apr 4, 2025
INTRODUCTION: Early diagnosis of Ewing sarcoma (ES) is critical for improving patient prognosis. However, the accurate diagnosis of ES remains challenging, underscoring the need for novel diagnostic biomarkers to enhance diagnostic precision and reli...
A Novel Theranostic Strategy for Malignant Pulmonary Nodules by Targeted CECAM6 with Zr/I-Labeled Tinurilimab.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Apr 3, 2025
Lung adenocarcinoma (LUAD) constitutes a major cause of cancer-related fatalities worldwide. Early identification of malignant pulmonary nodules constitutes the most effective approach to reducing the mortality of LUAD. Despite the wide application o...
Deep Learning-driven Microfluidic-SERS to Characterize the Heterogeneity in Exosomes for Classifying Non-Small Cell Lung Cancer Subtypes.
ACS sensors
Apr 1, 2025
Lung cancer exhibits strong heterogeneity, and its early diagnosis and precise subtyping are of great importance, as they can increase the ability to deliver personalized medicines by tailoring therapy regimens. Tissue biopsy, albeit the gold standar...
Anticancer drug response prediction integrating multi-omics pathway-based difference features and multiple deep learning techniques.
PLoS computational biology
Mar 31, 2025
Individualized prediction of cancer drug sensitivity is of vital importance in precision medicine. While numerous predictive methodologies for cancer drug response have been proposed, the precise prediction of an individual patient's response to drug...
Extract optimization and biological activities of Otidea onotica using Artificial Neural Network-Genetic Algorithm and response surface methodology techniques.
BMC biotechnology
Mar 28, 2025
In this study, the biological activities of Otidea onotica were investigated using two optimization methods, Response Surface Methodology (RSM) and Artificial Neural Network-Genetic Algorithm (ANN-GA). The extracts were tested for phenolic content, a...
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