Diabetes is a global disease that can lead to a range of complications. Currently, the treatment of type 2 diabetes focuses on oral hypoglycemic drugs and insulin analogues. Studies have shown that drugs such as oral metformin are useful in the treatment of diabetes but can limit the liver’s ability to release sugar. The development of glucose-lowering peptides has provided new options for the treatment of type 2 diabetes. Peptide drugs have low oral utilization due to their easy degradation, short half-life, and difficulty passing through the intestinal mucosa. Therefore, improving the oral utilization of peptide drugs remains an urgent problem. This paper reviews the research progress of peptide drugs in the treatment of diabetes mellitus and proposes that different types of nano-formulation carriers, such as liposomes, self-emulsifying drug delivery systems, and polymer particles, should be combined with peptide drugs for oral administration to improve their absorption in the gastrointestinal tract.

Quality control is central to the safety and consistency of traditional Chinese medicine (TCM), yet the chemical complexity of herbal materials complicates the interpretation of single-marker tests. Machine learning can extract patterns from high-dimensional analytical data and support defined quality-control tasks. This narrative review examines dimensionality reduction, spectral preprocessing, classical and deep models, and knowledge-driven fusion across four application scenarios: authentication and origin traceability, metabolomics-based assessment, processing and manufacturing control, and consistency evaluation of decoction pieces and preparations. Two machine-learning case studies and one process-analytical monitoring example are assessed against a common validation checklist, and the reporting of validation in cited performance studies is summarized. Current limitations include small datasets, distribution shift, and limited independent-domain validation. We outline priorities for process monitoring and for linking analytical predictions to pharmacological and clinical evidence. The review provides a task- and evidence-based guide to applying machine learning in TCM quality control.