Objective: To develop QingNangTCM, a specialized large language model (LLM) tailored for expert-level traditional Chinese medicine (TCM) question-answering and clinical reasoning, addressing the scarcity of domain-specific corpora and specialized alignment. Methods: We constructed QnTCM_Dataset, a corpus of 100 000 entries, by integrating data from ShenNong_TCM_Dataset and SymMap v2.0, and synthesizing additional samples via retrieval-augmented generation (RAG) and persona-driven generation. The dataset comprehensively covers diagnostic inquiries, prescriptions, and herbal knowledge. Utilizing P-Tuning v2, we fine-tuned the GLM-4-9B-Chat backbone to develop QingNangTCM. A multi-dimensional evaluation framework, assessing accuracy, coverage, consistency, safety, professionalism, and fluency, was established using metrics such as bilingual evaluation understudy (BLEU), recall-oriented understudy for gisting evaluation (ROUGE), metric for evaluation of translation with explicit ordering (METEOR), and LLM-as-a-Judge with expert review. Qualitative analysis was conducted across four simulated clinical scenarios: symptom analysis, disease treatment, herb inquiry, and failure cases. Baseline models included GLM-4-9B-Chat, DeepSeek-V2, HuatuoGPT-II (7B), and GLM-4-9B-Chat (freeze-tuning). Results: QingNangTCM achieved the highest scores in BLEU-1/2/3/4 (0.425/0.298/0.137/0.064), ROUGE-1/2 (0.368/0.157), and METEOR (0.218), demonstrating a balanced and superior normalized performance profile of 0.900 across the dimensions of accuracy, coverage, and consistency. Although its ROUGE-L score (0.299) was lower than that of HuatuoGPT-II (7B) (0.351), it significantly outperformed domain-specific models in expert-validated win rates for professionalism (86%) and safety (73%). Qualitative analysis confirmed that the model strictly adheres to the “symptom-syndrome-pathogenesis-treatment” reasoning chain, though occasional misclassifications and hallucinations persisted when dealing with rare medicinal materials and uncommon syndromes. Conclusion Combining domain-specific corpus construction with parameter-efficient prompt tuning enhances the reasoning behavior and domain adaptation of LLMs for TCM-related tasks. This work provides a technical framework for the digital organization and intelligent utilization of TCM knowledge, with potential value for supporting diagnostic reasoning and medical education.

Objective To investigate the factors influencing the efficacy of intravesical Bacille Calmette-Guérin(BCG)instillation after transurethral resection of bladder tumor(TURBT)in patients with intermediate-and high-risk non-muscle invasive bladder cancer(NMIBC),and to construct a prediction model for recurrence after BCG treatment.Methods A retrospective cohort study was conducted on the subjected patients diagnosed with intermediate-and high-risk NMIBC undergoing TURBT followed by standard BCG instillation.The 110 patients treated in Department of Urology of Army Medical Center of PLA from January 2018 to December 2023 were assigned into a training set,while the 52 patients treated at Department of Urology of General Hospital of Central Theater Command from January 2015 to December 2020 were into an external validation set.A total of 17 variables were included and analyzed.Univariate and multivariate Cox regression analyses were performed to identify factors associated with recurrence after BCG instillation,and nomograms were plotted to predict 1-year,3-year,and 5-year recurrence-free survival(RFS).Calibration curve,decision curve analysis(DCA),and receiver operating characteristic(ROC)curve analysis were conducted for internal and external validation to evaluate the predictive performance and clinical utility of the model.Results In the training set,26 patients(23.64%)experienced recurrence during the follow-up period,with a median RFS of 32.00(18.00～50.50)months.Univariate Cox regression analysis suggested that platelet count,eosinophil to lymphocyte ratio(ELR),neutrophil to lymphocyte ratio(NLR),platelet to lymphocyte ratio(PLR),systemic immune inflammation(SII)index,and neutrophil-monocyte to lymphocyte ratio(NMLR),pathological T1 stage(pT1)tumor and hemoglobin,albumin,lymphocyte,and platelet(HALP)score were potential factors influencing recurrence after BCG instillation.Multivariate Cox regression analysis identified high HALP score(HR=0.185,95%CI:0.046～0.736,P=0.017)as an independent protective factor,while high ELR(HR=3.599,95%CI:1.505～8.608,P=0.004)and pT1 stage(HR=3.240,95%CI:1.191～8.818,P=0.021)were independent risk factors for recurrence.Based on this,a nomogram prediction model was constructed.The calibration curves demonstrated good agreement between predicted and actual 1-,3-,and 5-year recurrence risks.Decision curve analysis indicated clinical utility across a wide threshold probability range.In the training set,the model showed strong predictive performance for 1-(AUC=0.842),3-(AUC=0.847),and 5-year(AUC=0.887)recurrence risks,which was further validated in the external cohort.Conclusion Higher HALP score prior to BCG instillation therapy is a protective factor against tumor recurrence,while higher ELR and pT1 stage are risk factors.Our nomogram prediction model based on HALP score,ELR and pathological T stage,can identify individuals at high risk of recurrence after BCG instillation therapy.

Objective To investigate risk factors for residual lesions after initial transurethral resection of bladder tumors(TURBT)and risk factors for tumor recurrence after second TURBT in patients with non-muscle-invasive bladder cancer(NMIBC)in order to provide reference for clinical management.Methods A case-control study design was adopted to include 120 NMIBC patients who underwent initial TURBT and then second surgery within 2～8 weeks in our department from January 2017 to January 2025.Based on the presence of residual lesions after the initial TURBT or not,the patients were divided into a residual lesion group(n=34)and a non-residual lesion group(n=86).Chi-square test and multivariate logistic regression analysis were performed to identify potential risk factors for residual lesions following the initial TURBT.Univariate and multivariate Cox regression models were used to analyze potential risk factors for tumor recurrence after the second TURBT.Results The residual lesion rate after initial TURBT was 28.33％.Chi-square test analysis revealed that tumor stage T1(Chi-square=5.756,P=0.016)and broad tumor base(Chi-square=4.331,P=0.037)were factors influencing residual lesions after initial TURBT.Multivariate logistic regression analysis identified tumor stage T1(OR=3.047,95％CI:1.128～8.226,P=0.028)as an independent risk factor for residual lesions after initial TURBT.The tumor recurrence rate after second TURBT was 17.5％.Multivariate Cox regression analysis identified tumor stage T1(OR=4.258,95％CI:1.248～14.532,P=0.021),intravesical chemotherapy instillation after second TURBT(OR=3.539,95％CI:1.284～9.752,P=0.015),history of urinary system tumors(OR=3.002,95％CI:1.145～7.873,P=0.025)and high platelet-to-lymphocyte(PLR)ratio(OR=2.798,95％CI:1.115～7.023,P=0.028)as independent risk factors for tumor recurrence after second TURBT.Conclusion Tumor stage T1 and broad tumor base are risk factors for residual lesions after initial TURBT,while tumor stage T1,intravesical chemotherapy instillation after second TURBT,history of urinary system tumors and high PLR ratio are risk factors for tumor recurrence after second TURBT.Comprehensive analysis on above 4 indicators can effectively assess the risk of tumor recurrence in NMIBC patients following second TURBT,and timely early medical intervention is beneficial for improving patient outcomes.

OBJECTIVE To investigate the efficacy and safety of ropivacaine combined with oxycodone for the analgesia of iliac fascia nerve block in patients undergoing hip replacement. METHODS Sixty-six patients who underwent hip replacement at the Central Hospital of Enshi Tujia and Miao Autonomous Prefecture from October 2023 to April 2024 were selected and randomly divided into observation group and control group， with 33 cases in each group. Before induction of anesthesia， ultrasound-guided iliac fascial nerve block was performed. Patients in the observation group were treated with 0.33% ropivacaine+0.1 mg/kg oxycodone injection mixture 30 mL， and patients in the control group were treated with 0.33% ropivacaine injection 30 mL. The time of first postoperative rescue analgesia， 24 h postoperative analgesic drug consumption， sensory block and motor block effective and maintenance time， satisfaction degree， numerical rating scale （NRS） pain score， Ramsay sedation score， muscle strength score， heart rate （HR）， mean arterial pressure （MAP）， oxygen saturation（SpO2）， sleep score， anxiety score， and the occurrence of adverse reactions in the two groups were all recorded. RESULTS Compared with the control group， the first rescue analgesia time after operation was significantly prolonged in the observation group， and 24 h postoperative analgesic drug consumption after operation decreased； the effective time of sensory block was significantly shortened， and the maintenance time of sensory block was significantly prolonged， and the satisfaction score was higher； the NRS pain score after iliac fascia nerve block was lower， HR and MAP were lower， and the anxiety score and sleep score 24 and 48 h after operation were lower （P＜0.05）. In terms of safety， patients in both groups had adverse reactions after operation， such as hypertension， nausea， vomiting， and dizziness， but there was no significant difference in the incidence of adverse reactions between the two groups （P＞0.05）. CONCLUSIONS Oxycodone combined with ropivacaine shows good efficacy and safety for iliac fascial nerve block analgesia in patients undergoing hip replacement， can significantly prolong the analgesic time of ropivacaine， reduce postoperative analgesic drug consumption， improve the sleep quality of patients， and promote the rapid recovery of patients.

Objective To investigate the relationship between the expression of serum angiopoietin-like protein 8(ANGPTL8)and Kruppel-like factor 2(KLF2)and the degree of coronary artery disease and the occurrence of major adverse cardiac events(MACE)in patients with acute myocardial infarction(AMI).Methods A total of 106 patients with AMI who were hospitalized in our hospital from January 2021 to June 2023 were selected as the research objects.According to the degree of coronary artery disease,the patients were grouped into mild group(52 cases)and severe group(54 cases).According to the occurrence of MACE,the patients were grouped into MACE group(18 cases)and non-MACE group(88 cases).General patient data were collected.Serum ANGPTL8 and KLF2 levels were detected by enzyme-linked immunosorbent assay(ELISA).Spearman correlation analysis was applied to analyze the correlation between serum ANGPTL8 and KLF2 levels and Gensini score in AMI patients.Multivariate logistic regression was applied to analyze the influencing factors of coronary artery disease degree in patients with AMI.Receiver operating characteristic(ROC)curves were drawn to analyze the value of serum ANGPTL8 and KLF2 levels in predicting the occurrence of MACE in AMI patients.Results The proportions of patients with history of hypertension and hyperlipidemia,systolic blood pressure,diastolic blood pressure,levels of triacylglycerol(TG),N-terminal pro-B-type natriuretic peptide(NT-proBNP),cardiac troponin I(cTnI),Gensini score,and level of serum ANGPTL8 in the severe group were higher than those in the mild group(P<0.05).The level of high-density lipoprotein cholesterol(HDL-C)and serum KLF2 in the severe group were lower than those in the mild group(P<0.05).The number of lesions in the mild group and the severe group was statistically obvious(P<0.05).Serum ANGPTL8 level in AMI patients was positively correlated with Gensini score(r=0.638,P<0.05),and serum KLF2 level was negatively correlated with Gensini score(r=-0.612,P<0.05).History of hypertension,hyperlipidemia,cTnI and ANGPTL8 were risk factors for the progression of severe coronary artery disease in patients with AMI(P<0.05),while HDL-C and KLF2 were protective factors(P<0.05).The serum level of ANGPTL8 in AMI patients in the MACE group was higher than that in the non-MACE group(P<0.05),and the serum KLF2 level in the MACE group was lower than that in the non-MACE group(P<0.05).The area under the curve of serum ANG-PTL8 and KLF2 levels and their combination in predicting the occurrence of MACE in AMI patients was 0.740(95%CI:0.646～0.820),0.799(95%CI:0.710～0.870),and 0.806(95%CI:0.717～0.876),respectively.Conclusion The expressions of serum ANGPTL8 and KLF2 are closely related to the degree of coronary artery disease in patients with AMI,and have certain predictive value for the occurrence of MACE.

Objective:To summarize the best evidence for the management of lower urinary tract dysfunction (LUTD) in total hysterectomy patients, so as to provide evidence-based basis for clinical practice.Methods:According to the "6S" pyramid model, literature related to the management of LUTD in total hysterectomy patients was successively searched from guide websites, evidence-based websites, professional websites and comprehensive databases. The search deadline was from the establishment of the databases to March 31, 2023. Two researchers evaluated the quality of the included literature, extracted evidence and recommended the level of evidence.Results:A total of 14 articles were included, including one clinical decision, two evidence summaries, three guidelines, one expert consensus and seven systematic evaluations. A total of 25 pieces of evidence were summarized from four aspects, such as symptom assessment, urinary tract management, symptom intervention and health education.Conclusions:Medical staff should manage lower urinary tract dysfunction in patients undergoing total hysterectomy based on evidence-based evidence to prevent or reduce the occurrence of lower urinary tract dysfunction in patients.

Objective:To construct and validate a nomogram model for predicting the risk of 28-day mortality in sepsis patients.Methods:A retrospective cohort study was conducted. 281 sepsis patients admitted to the department of intensive care unit (ICU) of the 940th Hospital of the Joint Logistics Support Force of PLA from January 2017 to December 2022 were selected as the research subjects. The patients were divided into a training set (197 cases) and a validation set (84 cases) according to a 7∶3 ratio. The general information, clinical treatment measures and laboratory examination results within 24 hours after admission to ICU were collected. Patients were divided into survival group and death group based on 28-day outcomes. The differences in various data were compared between the two groups. The optimal predictive variables were selected using Lasso regression, and univariate and multivariate Logistic regression analyses were performed to identify factors influencing the mortality of sepsis patients and to establish a nomogram model. Receiver operator characteristic curve (ROC curve), calibration curve, decision curve analysis (DCA), and clinical impact curve (CIC) were used to evaluate the nomogram model.Results:Out of 281 cases of sepsis, 82 cases died with a mortality of 29.18%. The number of patients who died in the training and validation sets was 54 and 28, with a mortality of 27.41% and 33.33% respectively. Lasso regression, univariate and multivariate Logistic regression analysis screened for 5 independent predictors associated with 28-day mortality. There were use of vasoactive drugs [odds ratio ( OR) = 5.924, 95% confidence interval (95% CI) was 1.244-44.571, P = 0.043], acute physiology and chronic health evaluation Ⅱ (APACHEⅡ: OR = 1.051, 95% CI was 1.000-1.107, P = 0.050), combined with multiple organ dysfunction syndrome (MODS: OR = 17.298, 95% CI was 5.517-76.985, P < 0.001), neutrophil count (NEU: OR = 0.934, 95% CI was 0.879-0.988, P = 0.022) and oxygenation index (PaO 2/FiO 2: OR = 0.994, 95% CI was 0.988-0.998, P = 0.017). A nomogram model was constructed using the independent predictive factors mentioned above, ROC curve analysis showed that the AUC of the nomogram model was 0.899 (95% CI was 0.856-0.943) and 0.909 (95% CI was 0.845-0.972) for the training and validation sets respectively. The C-index was 0.900 and 0.920 for the training and validation sets respectively, with good discrimination. The Hosmer-Lemeshoe tests both showed P > 0.05, indicating good calibration. Both DCA and CIC plots demonstrate the model's good clinical utility. Conclusions:The use of vasoactive, APACHEⅡ score, comorbid MODS, NEU and PaO 2/FiO 2 are independent risk factors for 28-day mortality in patients with sepsis. The nomogram model based on these 5 indicators has a good predictive ability for the occurrence of mortality in sepsis patients.

Objective To study anti-aging mechanism of skin with water extract of Codonopsis Radix by applying computational biology and animal experiments.Methods A total of 50 SPF 8-week-old C57BL/6 mice were selected and then randomly divided into five groups.D-galactose-induced aging mice model was constructed,and different doses of water extract of Codonopsis Radix were used for intervention.Hematoxylin-eosin staining(HE)and Masson staining were used to observe the pathological changes of mouse skin tissue.The content of hydroxyproline(HYP),superoxide dismutase(SOD),and malondialdehyde(MDA)in mouse skin were measured by biochemical detection.Transmission electron microscopy was used to observe the ultrastructural changes of mouse skin tissue.The chip data of skin aging from GEO database was obtained to screen skin-aging differential genes.TCMSP and UniProt databases were used to search for active ingredients and targets of Codonopsis Radix.The intersection of targets for Codonopsis Radix-skin aging was obtained by integrating the above data.A protein interaction network of all core gene proteins for Codonopsis Radix intervention in skin aging was constructed through the STRING database.Then,quantitative real-time PCR(RT-qPCR)and Western Blotting were used to verify target genes expression and pathway-related protein expression after the intervention of Codonopsis Radix in the aging model.Results Compared with the normal control group,the skin tissue structure of mice in senile model group were damaged significantly,the damage of skin tissue structure was improved significantly after the intervention of Codonopsis Radix.Compared with the normal control group,the content of HYP and SOD in the skin tissue of mice in senile model group were significantly reduced(P＜0.05),while MDA was significantly increased(P＜0.05).After the intervention of Codonopsis Radix,the content of HYP and SOD were increased,while MDA was decreased(P＜0.05)compared with senile model group.It was found that matrix metalloproteinase 9(MMP9)was the core target for the intervention of Codonopsis Radix on skin aging in computational biology.Experiments have shown that the expression of MMP9 was significantly increased in the skin of aging model mice compared to normal control group(P＜0.05).After the intervention of Codonopsis Radix,the expression of MMP9 is significantly reduced(P＜0.05),the expression of the key protein including inhibitory subunit of NF-kappa B alpha(IκBα)、IκB kinase-alpha(IKKα)、nuclear factor kappa-B(NF-κB)P65 of NF-κB signaling pathway were significantly changed(P＜0.05).Conclusion Codonopsis Radix water extract can effectively alleviate skin aging in aging model mice by inhibiting the protein expression of IκBα、IKKα、NF-κB P65 of NF-κB signaling pathway,reducing the expression of downstream gene MMP9,and ultimately alleviate skin collagen damage and resist skin aging.

Objective To systematically review the risk prediction models for intradialytic hypotension in maintenance hemodialysis patients,with a view to provide references for clinical practice.Methods PubMed,Embase,Web of Science,Cochrane Library,CINAHL,CNKI,VIP,Wanfang and CBM were searched from inception to May 29,2023.2 reviewers independently screened the literature,extracted information and assessed methodological quality using the Prediction Model Risk of Bias Assessment Tool.Results A total of 20 studies and 25 models were included with the sample size of 68～9 292 cases and the incidence of outcome events of 2.1～51％.Baseline systolic blood pressure,age,ultrafiltration rate,diabetes and dialysis duration were the top 5 predictors of repeated reporting of the models.20 models reported the area under the curve of ranging from 0.649 to 0.969,and 5 models reported calibration metrics.There were 9 internal validations and 4 combined internal and external validation models.The overall applicability of the 20 studies was good,but all had a high risk of bias,mainly in data analysis.Conclusion Research on risk prediction models for intradialytic hypotension in maintenance hemodialysis patients is still in the developmental stage.Future studies should improve the research design and reporting process,and validation studies of existing models should be carried out to further evaluate the effectiveness and feasibility in clinical practice.

Objective:To explore the independent risk factor for in-hospital mortality of patients with multiple trauma, and to construct a prediction model of risk of death and validate its efficacy.Methods:A retrospective cohort study was performed to analyze the clinical data of 1 028 patients with multiple trauma admitted to Affiliated Hospital of Jiangsu University from January 2011 to December 2021. There were 765 males and 263 females, aged 18-91 years[(53.8±12.4)years]. The injury severity score (ISS) was 16-57 points [(26.3±7.6)points]. There were 153 deaths and 875 survivals. A total of 777 patients were enrolled as the training set from January 2011 to December 2018 for building the prediction model, while another 251 patients were enrolled as validation set from January 2019 to December 2021. According to the outcomes, the training set was divided into the non-survival group (115 patients) and survival group (662 patients). The two groups were compared in terms of the gender, age, underlying disease, injury mechanism, head and neck injury, maxillofacial injury, chest injury, abdominal injury, extremity and pelvis injury, body surface injury, damage control surgery, pre-hospital time, number of injury sites, Glasgow coma score (GCS), ISS, shock index, and laboratory test results within 6 hours on admission, including blood lactate acid, white blood cell counts, neutrophil to lymphocyte ratio (NLR), platelet counts, hemoglobin, activated partial thromboplastin time (APTT), fibrinogen, D-dimer and blood glucose. Univariate analysis and multivariate Logistic regression analysis were performed to determine the independent risk factors for in-hospital mortality in patients with multiple trauma. The R software was used to establish a nomogram prediction model based on the above risk factors. Area under the receiver operating characteristic (ROC) curve (AUC), calibration curve and clinical decision curve analysis (DCA) were plotted in the training set and the validation set, and Hosmer-Lemeshow goodness-of-fit test was performed.Results:Univariate analysis showed that abdominal injury, extremity and pelvis injury, damage control surgery, GCS, ISS, shock index, blood lactic acid, white blood cell counts, NLR, platelet counts, hemoglobin, APTT, fibrinogen, D-dimer and blood glucose were correlated with in-hospital mortality in patients with multiple trauma ( P<0.05 or 0.01). Logistic regression analysis showed that GCS≤8 points ( OR=1.99, 95% CI 1.12,3.53), ISS>25 points ( OR=7.39, 95% CI 3.50, 15.61), shock index>1.0 ( OR=3.43, 95% CI 1.94,6.08), blood lactic acid>2 mmol/L ( OR=9.84, 95% CI 4.97, 19.51), fibrinogen≤1.5 g/L ( OR=2.57, 95% CI 1.39,4.74) and blood glucose>10 mmol/L ( OR=3.49, 95% CI 2.03, 5.99) were significantly correlated with their in-hospital mortality ( P<0.05 or 0.01). The ROC of the nomogram prediction model indicated that AUC of the training set was 0.91 (95% CI 0.87, 0.93) and AUC of the validation set was 0.90 (95% CI 0.84, 0.95). The calibration curve showed that the predicted probability was consistent with the actual situation in both the training set and validation set. DCA showed that the nomogram prediction model presented excellent performance in predicting in-hospital mortality. In Hosmer-Lemeshow goodness-of-fit test, χ2 value of the training set was 9.69 ( P>0.05), with validation set of 9.16 ( P>0.05). Conclusions:GCS≤8 points, ISS>25 points, shock index>1.0, blood lactic acid>2 mmol/L, fibrinogen≤1.5 g/L and blood glucose>10 mmol/L are independent risk factors for in-hospital mortality in patients with multiple trauma. The nomogram prediction model based on these 6 predictive variables shows a good predictive performance, which can help clinicians comprehensively assess the patient′s condition and identify the high-risk population.