Objective To investigate the clinical effect of protective technique for intercostal nerve in the application of esophageal cancer surgery.Methods Seventy cases of esophageal and cardia cancer patients with thoracic operation were randomly divided into observation group and control group,and each group was 35 cases.Patients in the observation group were treated with improved protective technique of intercostal nerve during operation,and patients in the control group using the traditional methods of operation for treatment.All patients were treated with intramuscular injection of morphine analgesia drugs after operation.Results The pain score of every time point analgesia patients in the observation group (VAS score) was significant difference from that in the control group (t =3.041,2.854,1.827,2.156,1.965,2.706,P＜ 0.05).The incidence of adverse reactions of analgesia,the incidence of painless patients of postoperative 30 d and 60 d,morphine dosage,chest tube indwelling time and the average hospitalization time in observation group were 0,42.86％,68.57％,(10.6±5.2) mg (4.0±1.6) d,(17.6±4.5) d,and the control group were 17.14％,20.0％,34.29％,(22.8±7.6) mg,(4.8±1.8) d,(20.4±5.8) d (x2=4.56,4.24,8.24,t=7.838,1.965,2.257,P＜0.05).Conclusion The improvement protective technique for intercostal nerve in esophageal cancer surgery,not only can reduce thoracotomy acute postoperative pain,but also reduce the happening of the CPP.Meanwhile,the analgesic effect is good.It is proved with a simple operation,less adverse reaction,conducive to disease recovery after esophageal cancer surgery,indicating that it has clinical popularization value.

OBJECTIVE： To establish the method for content determination of the related substance in fudosteine raw material and its preparations. METHODS： Fudosteine or its preparations produced by 8 domestic enterprises were taken as samples. HPLC method （external standard） was used to determine the contents of impurities A， B and C. The separation was performed on MGⅡ C18 column with mobile phase consisted of 0.12％ sodium hexane sulfonate solution （pH 2.0） at flow rate of 1.0 mL/min. The detection wavelength was set at 210 nm， column temperature was 35 ℃ and sample size was 20 μL. The contents of impurities E， F， G were determined by HPLC method （principal component self-contrast method with correction factor）. The separation was performed on Altech Altima C18 column with mobile phase consisted of 0.05 mol/L phosphate buffer-acetonitrile- water （gradient elution） at the flow rate of 0.5 mL/min. The detection wavelength was set at 200 nm， and the column temperature was 30 ℃. The sample size was 20 μL. RESULTS： The linear ranges of impurity A， B， C， E， F and G were 0.446-22.291， 0.202-20.158， 0.101-12.082， 0.111 0-11.100， 0.210 4-10.520， 0.221 6-11.080 μg/mL， respectively. The limits of detection were 5.57， 1.01， 1.99， 2.22， 4.21， 4.43 ng， respectively. The limits of quantitation were 11.14， 2.02， 3.98， 4.45， 8.42， 8.85 ng， respectively. The relative correction factors of impurities E， F and G were 0.91， 1.42 and 1.73， respectively； their relative retention time were 0.88， 1.95 and 3.08. RSDs of precision （n＝6） and stability [impurity A （4 h，n＝3）， other impurities （24 h，n＝7）] tests were all lower than 2.0％. The average recoveries were 98.0％， 97.3％， 102.4％， 99.4％， 98.9％， 96.4％， respectively； RSDs were 1.4％， 1.5％， 1.1％， 0.9％， 1.2％， 0.5％ （n＝9）， respectively. Total contents of substances in fudosteine raw material or its preparation produced by 8 enterprises were all lower than 1.1％. CONCLUSIONS： Established method is sensitive and specific. The method can be used for the quantitative study on related substances in fudosteine raw material and its preparations．

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.