Progress in the Regulation of Immune Cells in the Tumor Microenvironment by Bioactive Compounds of Traditional Chinese Medicine.

The tumor microenvironment (TME) can aid tumor cells in evading surveillance and clearance by immune cells, creating an internal environment conducive to tumor cell growth. Consequently, there is a growing focus on researching anti-tumor immunity through the regulation of immune cells within the TME. Various bioactive compounds in traditional Chinese medicine (TCM) are known to alter the immune balance by modulating the activity of immune cells in the TME. In turn, this enhances the body's immune response, thus promoting the effective elimination of tumor cells. This study aims to consolidate recent findings on the regulatory effects of bioactive compounds from TCM on immune cells within the TME. The bioactive compounds of TCM regulate the TME by modulating macrophages, dendritic cells, natural killer cells and T lymphocytes and their immune checkpoints. TCM has a long history of having been used in clinical practice in China. Chinese medicine contains various chemical constituents, including alkaloids, polysaccharides, saponins and flavonoids. These components activate various immune cells, thereby improving systemic functions and maintaining overall health. In this review, recent progress in relation to bioactive compounds derived from TCM will be covered, including TCM alkaloids, polysaccharides, saponins and flavonoids. This study provides a basis for further in-depth research and development in the field of anti-tumor immunomodulation using bioactive compounds from TCM.

Glaucoma Detection through a Novel Hyperspectral Imaging Band Selection and Vision Transformer Integration.

Conventional diagnostic methods for glaucoma primarily rely on non-dynamic fundus images and often analyze features such as the optic cup-to-disc ratio and abnormalities in specific retinal locations like the macula and fovea. However, hyperspectral imaging techniques focus on detecting alterations in oxygen saturation within retinal vessels, offering a potentially more comprehensive approach to diagnosis. This study explores the diagnostic potential of hyperspectral imaging for glaucoma by introducing a novel hyperspectral imaging conversion technique. Digital fundus images are transformed into hyperspectral representations, allowing for a detailed analysis of spectral variations. Spectral regions exhibiting differences are identified through spectral analysis, and images are reconstructed from these specific regions. The Vision Transformer (ViT) algorithm is then employed for classification and comparison across selected spectral bands. Fundus images are used to identify differences in lesions, utilizing a dataset of 1291 images. This study evaluates the classification performance of models using various spectral bands, revealing that the 610-780 nm band outperforms others with an accuracy, precision, recall, F1-score, and AUC-ROC all approximately at 0.9007, indicating its superior effectiveness for the task. The RGB model also shows strong performance, while other bands exhibit lower recall and overall metrics. This research highlights the disparities between machine learning algorithms and traditional clinical approaches in fundus image analysis. The findings suggest that hyperspectral imaging, coupled with advanced computational techniques such as the ViT algorithm, could significantly enhance glaucoma diagnosis. This understanding offers insights into the potential transformation of glaucoma diagnostics through the integration of hyperspectral imaging and innovative computational methodologies.