OBJECTIVE To investigate the ameliorative effects of Coptidis Rhizoma-Ophiopogonis Radix herb pair （HLMD） in rats with type 2 diabetes mellitus （T2DM） and its potential mechanisms.METHODS Male Wistar rats were randomly divided into a control group （6 rats） and a model induction group （30 rats）. The T2DM rat model was established by feeding a high-sugar and high-fat diet combined with intraperitoneal injection of streptozotocin. The successfully modeled rats were subsequently divided into the model group， the metformin group （200 mg/kg）， and the low-， medium-， and high-dose HLMD groups （100， 200 and 400 mg/kg， respectively）， with six rats in each group. Rats in each group were administered the corresponding drug solution or normal saline by oral gavage once daily for six consecutive weeks. Body weight， fasting blood glucose （FBG）， the area under the curve （AUC） of the oral glucose tolerance test， serum lipid profiles [total cholesterol （TC）， triglyceride （TG）， low-density lipoprotein cholesterol （LDL-C）， and high-density lipoprotein cholesterol （HDL-C）]， and inflammatory cytokine [interleukin-6 （IL-6）， IL-1β， tumor necrosis factor-α （TNF-α）， and IL-10] levels were measured. Histopathological changes in liver and pancreatic tissues， as well as hepatic lipid accumulation， were evaluated. Changes in gut microbiota composition were analyzed in the control， model， and high-dose HLMD groups. Network pharmacology and molecular docking analyses were performed to identify potential therapeutic targets of HLMD against T2DM， followed by experimental validation.RESULTS Compared with the model group， the body weight generally recovered in rats in all HLMD dose groups， while FBG levels consistently decreased. Histopathological abnormalities， including disrupted pancreatic islet architecture， disorganized hepatocyte arrangement， hepatic cord necrosis， and lipid accumulation， were alleviated to varying degrees. AUC （except for the low-dose group）， levels of TC， TG， LDL-C， IL-6（except for the low-dose group）， IL-1β and TNF-α， Simpson and Shannon indices， as well as the relative abundances of Firmicutes， Proteobacteria， and Clostridium were significantly reduced； meanwhile， the levels of HDL-C（except for the low-dose group） and IL-10 （except for the low-dose group）， as well as the relative abundances of Bacteroidota and Ligilactobacillus were significantly increased （ P ＜0.05）. Network pharmacology and molecular docking analyses suggested that the active constituents of HLMD， including quercetin， adenosine， oleanolic acid， stigmasterol， and ophiopogonin D′， may exert antidiabetic effects by targeting key proteins such as protein kinase B1（Akt1）， TNF， IL-6， P53 and IL-1β. Experimental validation further demonstrated that HLMD significantly downregulated the protein expressions of P53 （except for the low-dose group）， IL-6 （except for the low-dose group）， IL-1β and TNF-α in the liver tissues of T2DM rats （ P ＜0.05）.CONCLUSIONS HLMD alleviates glucose and lipid metabolism disorders， attenuates inflammatory responses， and ameliorates hepatic and pancreatic tissue damage in T2DM rats. These beneficial effects may be associated with the modulation of gut microbiota composition and the downregulation of inflammation-related targets， including P53， IL-6， IL-1β and TNF-α.

Objective: To analyze the association of participation in non-sports extracurricular tutoring classes with the prevalence of screening myopia, axial length (AL) and axial length to corneal radius ratio (AL/CR) among primary school students, so as to provide evidences for formulating myopia prevention and control policies. Methods: In December 2024, combination of convenience and cluster sampling method was used to select 2 273 students from two primary schools in Hefei City, Anhui Province. Ophthalmic examinations and questionnaire surveys were conducted to obtain information on myopia, AL, AL/CR and participation in various types of extracurricular tutoring. A binary Logistic regression model was used to analyze the association between non-sports tutoring and screening myopia, and multiple linear regression models were used to examine the associations between non-sports tutoring and AL and AL/CR. Results: Among the surveyed students, the participation rate in non-sports extracurricular tutoring classes was 64.9% , and the overall prevalence of screening myopia was 39.1%. The average AL and AL/CR were (23.60± 1.01 ) mm and (3.00±0.12), respectively. Univariate analysis showed that students who attended non-sports, music, or academic tutoring classes for ≥2 h per week had higher risks of screening myopia and greater AL/CR values than non-participants (screening myopia: OR =1.38, 1.82, 1.55; AL/CR: β =0.01, 0.03, 0.03; all P <0.05). After adjusting for sex, grade, and participation in sports tutoring, multivariate analysis indicated that participation in non-sports and musical instrument tutoring classes for ≥2 h per week remained significantly associated with higher risks of screening myopia ( OR =1.26, 1.49, both P <0.05). Multiple linear regression showed that participation in musical instrument tutoring for ≥2 h per week was positively correlated with AL ( β=0.14, P < 0.05). Conclusions Participation in non-sports extracurricular tutoring is common among primary school students. Attending non-sports tutoring classes for ≥2 h per week increases the risk of screening myopia.

Objective:To detect the expression levels of long non-coding RNAs(lncRNA)in elderly patients with pulmonary tuberculosis(PTB)and those with non-tuberculous lung diseases(non-TB), and to assess the performance of these lncRNA in the differential diagnosis of PTB.Methods:A total of 300 elderly patients with suspected PTB were recruited from Beijing Chest Hospital between January 2024 and September 2024, and were further divided into the PTB group and the non-TB lung disease group based on the results of mycobacterium tuberculosis(MTB)pathogenicity testing.Peripheral blood mononuclear cells were isolated using a lymphocyte separation solution, and RNA was extracted using the TRIzol method.Nine lncRNAs, previously identified as differentially expressed in PTB through our group's microarray analysis, were selected and detected by real-time fluorescence quantitative polymerase chain reaction to evaluate the expression levels of these lncRNAs between the PTB and non-TB lung disease groups.The overall patients were randomly divided into training and validation sets in a 7∶3 ratio.Lasso regression was employed to select the characteristic variables, and a random forest algorithm was then used to construct the lncRNA diagnostic portfolio.Receiver operating characteristic(ROC)curves were generated to evaluate the diagnostic performance of individual lncRNAs and the combined panel in differentiating elderly patients with PTB from those with other non-TB lung diseases.Results:A total of 201 cases were included, with 105 confirmed elderly patients diagnosed with PTB(52.2%)and 96 elderly patients suffering from non-TB lung disease(47.8%).Compared to the elderly patients with non-TB lung disease, the expression levels of ENST00000417346.1, ENST00000620744.1, lncRNA PWP1, ENST00000583184.1, lncRNA ABHD17B, ENST00000607464.1, ENST00000516057.1, and NR_003000 were significantly downregulated in the PTB patients, whereas the expression level of lncRNA BCL2L10 was significantly upregulated in the PTB patients.ROC analysis revealed that the area under the curve(AUC)for each lncRNA ranged from 0.659 to 0.848.The diagnostic panel, which included NR_003000, ENST00000607464.1, ENST00000583184.1, and ENST00000620744.1 as determined by Lasso analysis, exhibited AUC values of 0.917 and 0.906 in the training and validation sets, respectively.The performance of this panel was superior to that of each individual lncRNA.Conclusions:The random forest model, which incorporates NR_003000, ENST00000607464.1, ENST00000583184.1, and ENST00000620744.1, demonstrates potential in differentiating between PTB and non-TB lung diseases.

Objective:To assess common errors in head and neck CT angiography (CTA) image reports using four types of large language models (LLM), namely GPT-4, DeepSeek, ERNIE Bot and SparkDesk, and to assess the feasibility of using existing LLMs to support quality control of radiology reports in Chinese.Methods:The study was a cross-sectional study. Totally 1 000 head and neck CTA image reports were randomly selected using the simple random sampling method from Xuanwu Hospital, Capital Medical University in 2023, including 500 primary reports and 500 finalized reports. Two radiologists collaboratively identified six types of errors in the reports: description errors, writing errors, left-right confusion errors, diagnostic omissions, logical sequence errors, and other errors. The overall quality of the reports was assessed using a 5-point Likert scale. Subsequently, GPT-4, DeepSeek, ERNIE Bot and SparkDesk models were employed to detect the same six types of errors in the imaging reports and to provide overall scoring. The results from manual review were considered the gold standard for calculating F1 score to evaluate model performance. Intra-class correlation coefficients ( ICC) were used to assess the consistency between manual scores and the overall scores from the four LLMs. Results:In the primary imaging reports, the proportions of manually detected errors were as follows: descriptive errors 2.6% (13/500), writing errors 0.6% (3/500), left-right confusion errors 0, diagnostic omissions 6.4% (32/500), logical sequence errors 5.2% (26/500), and other errors 0. In the finalized imaging reports, the proportions of errors across the six categories were 0.2% (1/500), 0, 0, 0, 0, and 0.2% (1/500), respectively. For error detection in the primary imaging reports, the F1 scores of GPT-4 for the six error types were 0.992, 0.997, 0.997, 0.967, 0.980, and 0.992, respectively. DeepSeek achieved F1 scores of 0.980, 0.955, 0.981, 0.920, 0.995, and 0.960; ERNIE Bot scored 0.982, 0.990, 1.000, 0.956, 0.976, and 0.999; and SparkDesk achieved 0.985, 0.995, 1.000, 0.961, 0.982, and 1.000. In the detection of errors in finalized imaging reports, GPT-4′s F1 scores were 0.994, 0.995, 0.998, 0.973, 0.989, and 0.993; DeepSeek scored 0.968, 0.965, 0.985, 0.971, 0.991, and 0.983; ERNIE Bot achieved 0.996, 0.992, 1.000, 0.983, 0.999, and 0.997; and SparkDesk achieved 0.999, 0.999, 1.000, 1.000, 1.000, and 0.999. The consistency between GPT-4, DeepSeek, and SparkDesk models and human ratings was moderate, with ICC values of 0.514, 0.560, and 0.515 respectively (all P0.001); in contrast, the overall score of ERNIE Bot showed poor consistency with human ratings, with an ICC of 0.221 ( P0.001). Conclusion:LLMs demonstrate high accuracy in detecting errors in head and neck CTA imaging reports. The overall scoring of report quality shows moderate consistency with manual assessments, indicating a certain feasibility for automated quality control in reporting.

Objective To explore the effectiveness and practical pathways of integrating narrative education into higher vocational nursing courses for cultivating students' humanistic care ability.Methods A total of 156 nursing students from a higher vocational college were randomly divided into an observation group(narrative education intervention)and a control group(traditional teaching).Quantitative evaluations were conducted using the Caring Ability Inventory(CAI)and a self-developed Humanistic Care Quality Scale,supplemented by semi-structured interviews for qualitative thematic analysis.Results The ob-servation group demonstrated significantly higher total caring ability scores(180.1±10.9 vs 145.2±14.3),humanistic care quality(97.5±3.8 vs 77.7±5.9),and course satisfaction(9.5±0.6 vs 7.0±1.3)compared to the control group(P＜0.01).Qualitative findings revealed that narrative education enhanced professional beliefs and practical skills through empathy training,reflective writing,and team collaboration.Conclusion Narrative education systematically improves the humanistic care ability and professional of higher vocational nursing students,providing empirical support for nursing education reform.

Objective:To comprehensively retrieve and evaluate the best available evidence on perioperative fluid management in patients undergoing hepatectomy for liver cancer, and to provide references for clinical practice.Methods:A systematic search was conducted across multiple platforms, including UpToDate, BMJ Best Practice, Guidelines International Network, National Institute for Health and Care Excellence, PubMed, Web of Science, China National Knowledge Infrastructure, and Wanfang Data, for clinical decisions, guidelines, expert consensus, systematic reviews, evidence summaries, and randomized controlled trials related to perioperative fluid management in patients undergoing hepatectomy for liver cancer. The search period was from database inception to March 13, 2024. The 2014 version of the Joanna Briggs Institute evidence pre-grading and recommendation grading system was used to classify the evidence.Results:A total of 14 articles were included: 3 clinical decision articles, 3 guidelines, 1 evidence summary, 5 expert consensuses, 1 systematic review, and 1 randomized controlled trial. A total of 27 best evidence items were extracted, covering seven aspects: principles of fluid therapy, preoperative fluid management, intraoperative fluid management, postoperative fluid management, fluid type selection, assessment and monitoring, and training and education.Conclusions:This study summarizes the best evidence on perioperative fluid management for hepatectomy in liver cancer patients and provides an evidence-based reference for clinical nursing practice. Healthcare professionals should apply the evidence in a personalized manner based on the actual clinical context to promote scientific fluid management in the perioperative period.

Objective To explore the effectiveness and practical pathways of integrating narrative education into higher vocational nursing courses for cultivating students' humanistic care ability.Methods A total of 156 nursing students from a higher vocational college were randomly divided into an observation group(narrative education intervention)and a control group(traditional teaching).Quantitative evaluations were conducted using the Caring Ability Inventory(CAI)and a self-developed Humanistic Care Quality Scale,supplemented by semi-structured interviews for qualitative thematic analysis.Results The ob-servation group demonstrated significantly higher total caring ability scores(180.1±10.9 vs 145.2±14.3),humanistic care quality(97.5±3.8 vs 77.7±5.9),and course satisfaction(9.5±0.6 vs 7.0±1.3)compared to the control group(P＜0.01).Qualitative findings revealed that narrative education enhanced professional beliefs and practical skills through empathy training,reflective writing,and team collaboration.Conclusion Narrative education systematically improves the humanistic care ability and professional of higher vocational nursing students,providing empirical support for nursing education reform.

Objective To explore the roles of the mechanoreceptor Yes-associated protein 1(YAP1)and piezo type mechanosensitive ion channel component 2(PIEZO2)in mechanotransduction in mouse bladder urothelial cells.Methods Mouse bladder urothelial cells were subjected to mechanical stretching using the FX-6000T cell stretching system and treated with the YAP1-specific inhibitor verteporfin(VP).The expressions of PIEZO2,YAP1 and connective tissue growth factor(CTGF)at the mRNA and protein levels,as well as changes in cellular adenosine triphosphatase(ATP)concentration,were detected using reverse transcription quantitative PCR(RT-qPCR)and Western blotting(WB).Results After stretching stimulation,under the fluorescence microscope,it was observed that the diameter length of the stretched cells were longer than that before stretching,and the difference was statistically significant(P＜0.05).The expressions of YAP1,PIEZO2 and CTGF at the mRNA and protein levels were increased in the stretched group compared to those of the non-stretched group(P＜0.05).VP effectively reduced the expressions of YAP1,PIEZO2 and CTGF at the mRNA and protein levels after stretching stimulation(P＜0.05).Stretching stimulation significantly increased the intracellular ATP concentration,while VP was able to inhibit the increase in ATP concentration,with a statistically significant difference(P＜0.000 1).Conclusion Stretching stimulation increased the expressions of YAP1 and PIEZO2 in bladder urothelial cells and promoted the release of ATP;verteporfin inhibited the increase in YAP1 activity and the overexpression of PIEZO2 caused by stretching,thereby reducing the release of ATP.It is suggested that mouse bladder urothelial cells may primarily sense mechanical signals through the YAP1-PIEZO2-ATP pathway.

Objective:To analyze the epidemiological characteristics and infection sources of cholera in China from 2005 to 2024.Methods:A total of 2 066 cholera cases were included in the study, which were obtained from the China Disease Control and Prevention Information System (CDPCIS) of China CDC. The information on cholera clusters was downloaded from the National Public Health Emergency Event Surveillance System (PHEESS) of China CDC. A total of 128 cholera clusters were included and analyzed in this study. The epidemiological characteristics and infection sources of cholera were analyzed. The Jointpoint model was applied to analyze the incidence trend, and annual percentage change (APC) was also quantified.Results:From 2005 to 2024, a total of 2 066 cholera cases were reported, with an average of 103 cases reported annually. Specifically, the incidence showed a marked downward trend from 2004 to 2015 ( APC=-26.78%, P=0.006). During 2015-2024, the disease remained at low endemic levels, with an average of 18 reported cases annually ( APC=-2.68%, P=0.807). Cholera peak season was from May to October. A total of 24 provinces reported cholera cases, which were mainly distributed in Zhejiang, Fujian, Beijing, Jiangsu, Anhui, Guangdong, and Hainan provinces, accounting for 78.03% of the total cases. Pathogen surveillance indicated an alternating prevalence of Vibrio cholerae serogroups O1 and O139 among laboratory-confirmed cases between 2005 and 2024. There was a disparity in the dominant serogroup of Vibrio cholerae by region. The results from 128 cholera clusters indicated that cholera outbreaks frequently occurred in rural banquets (64.84%), followed by regular restaurants (13.28%). Among these, 63 clusters (49.22%) with identified infection sources indicated that foodborne transmission (95.24%) was the primary mode of cholera transmission, which mainly through seafood and aquatic products, such as soft-shelled turtles, shrimp and shellfish. The characteristics of cholera clusters caused by Vibrio cholerae serogroups O1 and O139 showed statistically significant differences in scale, attack rate, place of residence, setting, and infection source ( P<0.05). Conclusion:Cholera incidence has remained consistently low since 2015 in China, mainly in sporadic cases. Rural gatherings (e.g., wedding banquets) are the main settings for cholera clusters. The main infection sources are predominantly caused by cross-contamination due to improper processing practices of aquatic products, such as soft-shelled turtles.

Objective To develop and validate risk prediction models utilizing five machine learning algorithms for assessing postoperative pulmonary infection(PPI)risk in lung cancer patients undergoing grade Ⅳ thoracoscopic surgery.Methods A retrospective cohort study included 2,380 lung cancer patients who underwent grade Ⅳ thoracoscopic surgery at a tertiary hospital in Shanghai(January 2022 to June 2024).Patients were stratified into training(n=1,665)and validation(n=715)cohorts.Five machine learning algorithms—Logistic regression(LR),artificial neural network(ANN),support vector machine(S VM),random forest(RF),and extreme gradient boosting(XGB)—were employed to construct predictive models.A nomogram was developed for clinical utility.Results Among 2,380 patients,226(9.5％)developed PPI.The Least Absolute Shrinkage and Se-lection Operator(LASSO)regression identified eight predictive variables:daily cigarette consumption,diabetes history,preoperative diffusing capacity,maximal tumor diameter,24-hour postoperative chest drainage volume,perioperative oral nutritional supplementation(ONS),postoperative urinary cathe-terization,and intraoperative pleural adhesion severity.All models demonstrated robust discrimina-tion,with area under the curve(AUC)values ranging from 0.862 to 0.947.The XGB model a-chieved superior performance(AUC=0.947,95％CI,0.937 to 0.962),followed closely by the LR model(AUC=0.926,95％CI,0.918 to 0.933).Conclusion Machine learning-based algo-rithms models effectively stratify PPI risk in lung cancer patients following grade Ⅳ thoracoscopic surgery.The derived nomogram provides a practical tool for perioperative risk management by healthcare providers.