This study compared the chemical composition differences among various root tissues of Astragalus membranaceus through optimization of histochemical localization methods, thereby providing some reference for targeted extraction and enrichment of bioactive components. The chloral hydrate permeabilization technique was enhanced with sodium nitrite-aluminum nitrate-sodium hydroxide reagent for flavonoid staining. Comparative analyses of total saponins, total flavonoids, and astragaloside IV content in xylem, phloem, and periderm tissues were performed using ultraviolet-visible spectroscopy (UV-Vis) and high-performance liquid chromatography (HPLC). By optimizing staining protocols, interference from natural pigmentation was successfully eliminated, thereby enhancing the visualization of tissue-specific chemical distribution. Quantitative analysis revealed that the phloem contained the highest total saponins (6.92%), while the periderm exhibited peak total flavonoids (0.876%) and exclusive enrichment of astragaloside IV (0.850%). The refined histochemical localization method enables precise characterization of phytochemical distribution across root tissues, which can offer guidance for selective extraction and enrichment strategies, and comprehensively promote the utilization and development of Astragalus membranaceus resources.

Breast cancer is one of the most common cancers in women,but there is currently a lack of accurate prognos-tic assessment systems.The Naples Prognostic Score(NPS)is a prognostic prediction system that incorporates inflammatory and nutritional indicators.It has been proven to have important clinical utility in predicting the prognosis of patients with malignancies such as colon cancer,gallbladder cancer,endometrial cancer,and lung cancer.In recent years,research has found that NPS may be superior to TNMstaging in predicting the prognosis of breast cancer patients.It is an independent predictor of overall sur-vival(OS)and progression-free survival(PFS)in breast cancer patients.This suggests that NPS has great potential for applica-tion in predicting the prognosis of breast cancer.