A redox-responsive hyaluronic acid-vitamin E polyethylene glycol succinate nanoparticle loaded with paclitaxel (HA-SS-TPGS@PTX) was designed to investigate its mechanism for overcoming multidrug resistance (MDR) in non-small cell lung cancer (NSCLC) in vitro. HA-SS-TPGS@PTX nanoparticles were prepared using an emulsion-ultrasonication method. Techniques such as flow cytometry and confocal laser scanning microscopy (CLSM) were employed to study their effects on apoptosis induction, mitochondrial function, and the regulation of P-glycoprotein (P-gp) expression in PTX-resistant lung cancer cells (A549/T). Results showed that HA-SS-TPGS@PTX nanoparticles significantly inhibited the proliferation of A549/T cells in vitro, with an IC50 of 1.35 μg/mL. The nanoparticles entered the cells via CD44 receptor-mediated endocytosis. The high intracellular concentration of glutathione (GSH) triggered the release of PTX and TPGS, which subsequently induced a decrease in mitochondrial membrane potential, leading to apoptosis. Meanwhile, HA-SS-TPGS@PTX also inhibited P-gp expression and ATP consumption, thereby blocking drug efflux. The design of HA-SS-TPGS@PTX provides a new strategy for overcoming MDR in NSCLC.

Aiming at the lack of quantitative evaluation methods in clinical diagnosis of lung cancer, a classification and prediction model of lung cancer based on Support Vector Machine (SVM) was constructed by using radiomics method. Firstly, the definition and processing flow of radiomics were introduced. The experimental samples were selected from 816 lung cancer patients on LIDC. Firstly, ROI was extracted by central pooling convolution neural network segmentation method. Then, Pyradiomics and FSelector feature selection models were used to extract features and reduce dimension. Finally, SVM was used to construct the classification and prediction model of lung tumors. The predictive accuracy of the model is 80.4% for the classification of benign and malignant pulmonary nodules larger than 5 mm, and the value of the area under the curve (AUC) is 0.792. This indicates that the SVM classifier model can accurately distinguish benign and malignant pulmonary nodules larger than 5 mm.
Algorithms ; Humans ; Lung Neoplasms/diagnostic imaging* ; Neural Networks, Computer ; Radiometry ; Support Vector Machine ; Tomography, X-Ray Computed