A valve-free continuous flow method and instrument were established,with only a multi-channel pump for delivering the sample and reagent,and without any injection or solenoid valves and sample loop for selecting and adding the sample or reagent.Nitrate was reduced to nitrite with Cu-Cd reductant column,and then detected with spectrophotometric detector.The proposed method was suitable for determination of nitrate at normal level in most of estuary and coastal seawaters.With the optimum parameters,the linear range and detection limit were 5-180 μmol/L and 0.27 μmol/L,respectively.The samples of 10 and 80 μmol/L nitrate were continually measured for 11 times,and the relative standard deviations were 1.4％ and 1.3％,respectively.The recovery of real samples at different salinity ranged between 99.4％ and 106.1％.There was no significant difference in the analytical results between the proposed method and the flow injection analysis (FIA).In comparison with FIA,the method and instrument were less cost and easy to operate,and was suitable to be applied in general laboratories and field for continuous monitoring.The method was successfully used to measure the nitrate in seawater samples in Xiamen's Western Harbor and monitor nitrate in Jiulongjiang estuary.

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

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.