Present study was focused on the chemical constituents of the stems and leaves of Salvia yunnanensis C . H. Wright and their anti-angiogeneic activities. The compounds were isolated by column chromatography over silica gel and Sephadex LH-20, and other isolation techniques. Their structures were elucidated on the basis of spectral analysis and chemical evidences. Their anti-angiogeneic activities were evaluated by the chicken chorioallantoic membrane (CAM) neovascularisation model. Seven compounds were separated and identified as ( + ) -spathulenol( 1), 5,7,4'-trihydroxyflavanone(2) , beta-amyrin(3), 3 beta-hydroxy-12-ursene(4), 2alpha,3 beta-dihydroxyursa-12-en-28-oic acid(5), ursolic acid (6) and 3-oxo-12-ursen-28-oic acid (7). Compounds 1, 2, 5 and 6 were obtained from this plant for the first time. Compounds 5 (an oleanane compound) and 6 (an ursane compound) could inhibit angiogenesis significantly in a dose-dependent manner.
Angiogenesis Inhibitors ; chemistry ; pharmacology ; Animals ; Chorioallantoic Membrane ; drug effects ; metabolism ; Dose-Response Relationship, Drug ; Drugs, Chinese Herbal ; chemistry ; pharmacology ; Plant Leaves ; chemistry ; Plant Stems ; chemistry ; Salvia ; chemistry

Objective:To investigate the effect and mechanism of the gastric cancer cells-derived exosome miR-382-5p in-duced by Helicobacter pylori(H.pylori)on the autophagy and polarization of macrophages,providing new clues for further elucidating the carcinogenic mechanism of H.pylori.Methods:Ultracentrifugation and exosome extraction kit were used to extract the exosomes re-leased by the H.pylori stimulated group and the blank control group AGS cells cells,then transmission electron microscopy(TEM),nanoparticle tracking analysis(NTA)and Western blot were employed to identify exosomes.qRT-PCR was used to detect the expres-sion of miR-382-5p in H.pylori induced AGS-derived exosomes.miR-382-5p mimic was transfected into THP-1 macrophages,then the expressions of autophagy markers(LC3Ⅱ,p62,and Beclin-1)were evaluated by Western blot,the number of autophagosomes was detected by immunofluorescence.The expression levels of PTEN protein,downstream proteins PI3K,AKT,mTOR and its phosphory-lated proteins p-PI3K,p-AKT,p-mTOR were detected by Western blot.Flow cytometry was used to detect the expression levels of macrophage phenotypic molecules CD206 and HLA-DR.ELISA was used to detect the secretion of cytokines TNF-α,IL-6,IL-10 and Arginase1 in macrophage supernatants.Results:The extracted exosomes were consistent with exosome morphology and highly ex-pressed the surface marker proteins CD9,CD63 and TSG101.Compared with the blank control group,the expression level of exosom-al miR-382-5p in H.pylori-infected group was significantly increased.miR-382-5p mimic transfection resulted in decreased expression of LC3 Ⅱ and Beclin-1 in macrophages,increased expression of P62 and decreased number of autophagosomes.Moreover,the protein expression level of PTEN was significantly decreased in the miR-382-5p mimic transfection group,while the expression levels of p-PI3K,p-AKT and p-mTOR were significantly increased.miR-382-5p mimic transfection also resulted in increased expression of mac-rophage M2 type marker protein CD206 and decreased expression of M1 type marker protein HLA-DR,as well as increased expres-sions of IL-10 and Arginine1,whereas decreased expression of IL-6 and TNF-α.Pretreatment with the pathway inhibitor BEZ235 par-tially reverses the effects of miR-382-5p on macrophage autophagy and polarization.Conclusion:H.pylori-induced gastric cancer cells-derived exosomal miR-382-5p suppresses macrophage autophagy and induces M2 polarization through down-regulation of PTEN ex-pression and activation of the PI3K/AKT/mTOR signaling pathway.

ObjectiveTo construct a large language model （LLM）-based intelligent pre-consultation system for traditional Chinese medicine （TCM） to improve efficacy of clinical practice. MethodsA TCM large language model was fine-tuned using DeepSpeed ZeRO-3 distributed training strategy based on YAYI 2-30B. A weighted undirected graph network was designed and an agent-based syndrome differentiation model was established based on relationship data extracted from TCM literature and clinical records. An agent collaboration framework was developed to integrate the TCM LLM with the syndrome differentiation model. Model performance was comprehensively evaluated by Loss function， BLEU-4， and ROUGE-L metrics， through which training convergence， text generation quality， and language understanding capability were assessed. Professional knowledge test sets were developed to evaluate system proficiency in TCM physician licensure content， TCM pharmacist licensure content， TCM symptom terminology recognition， and meridian identification. Clinical tests were conducted to compare the system with attending physicians in terms of diagnostic accuracy， consultation rounds， and consultation duration. ResultsAfter 100 000 iterations， the training loss value was gradually stabilized at about 0.7±0.08， indicating that the TCM-LLM has been trained and has good generalization ability. The TCM-LLM scored 0.38 in BLEU-4 and 0.62 in ROUGE-L， suggesting that its natural language processing ability meets the standard. We obtained 2715 symptom terms， 505 relationships between diseases and syndromes， 1011 relationships between diseases and main symptoms， and 1 303 600 relationships among different symptoms， and constructed the Agent of syndrome differentiation model. The accuracy rates in the simulated tests for TCM practitioners， licensed pharmacists of Chinese materia medica， recognition of TCM symptom terminology， and meridian recognition were 94.09%， 78.00%， 87.50%， and 68.80%， respectively. In clinical tests， the syndrome differentiation accuracy of the system reached 88.33%， with fewer consultation rounds and shorter consultation time compared to the attending physicians （P＜0.01）， suggesting that the system has a certain pre- consultation ability. ConclusionThe LLM-based intelligent TCM pre-diagnosis system could simulate diagnostic thinking of TCM physicians to a certain extent. After understanding the patients' natural language， it collects all the patient's symptom through guided questioning， thereby enhancing the diagnostic and treatment efficiency of physicians as well as the consultation experience of the patients.