ObjectiveFrom the perspective of cardiovascular-kidney-metabolic （CKM） syndrome， this study aims to construct and validate a diagnostic machine learning model integrating traditional Chinese and Western medicine for severe coronary artery stenosis in patients with CKM， thereby providing clinical decision-making support for patients with borderline lesions. MethodsBased on a retrospective study design， a total of 535 hospitalized patients from two independent campuses of Jiangsu Province Hospital of Traditional Chinese Medicine： the main campus （from January 2024 to August 2024） and Zidong branch （from September 2024 to December 2024） were screened. Data from the main campus were randomly divided into the training dataset （376 cases） and the internal validation dataset （95 cases） at a 4∶1 ratio， while data from Zidong branch served as the external validation dataset （64 cases）. Risk factors were analyzed and screened through literature review， expert interviews， and the least absolute shrinkage and selection operator （LASSO） algorithm. Nine machine learning algorithms were utilized to construct diagnostic models. Comparative analyses of common evaluation metrics， calibration curves， and decision curves were conducted to select the model through internal and external validation. The Shapley additive explanations （SHAP） method and two cases were utilized to help understand the operational logic of the best model. Finally， the best model was applied to patients with borderline lesions to calculate diagnostic efficacy. ResultsNine risk factors were screened by LASSO regression， such as phlegm， hematoma， stagnation， deficiency， hypertension duration， gender， arterial stiffness index （ASI）， uric acid to high-density lipoprotein cholesterol ratio （UHR）， and glycosylated hemoglobin （HbA1c）. After comparison from multiple dimensions， the light gradient boosting machine （LightGBM） was the best model， achieving area under the curve （AUC） of 0.918 （95% confidence interval （CI）： 0.890-0.945） in the training dataset， 0.885 （95%CI： 0.820-0.951） in the internal validation dataset， and 0.897 （95%CI： 0.818-0.975） in the external validation dataset. Calibration curves indicated good consistency in the predicted probabilities， while decision curve analysis showed clinical benefit when threshold probabilities were less than 90%. SHAP importance rankings were stagnation， deficiency， hematoma， HbA1c， gender， phlegm， hypertension duration， ASI， and UHR. When applied to the patients with borderline lesions， the diagnostic model achieved an AUC of 0.783 （95%CI： 0.637-0.930）， with 73% of patients with actual severe stenosis getting benefit. ConclusionGuided by clinical value， the diagnostic model integrating traditional Chinese and Western medicine established in this study demonstrates favorable performance， providing a basis for clinical diagnosis， treatment， and decision-making in patients with CKM.

ObjectiveFrom the perspective of cardiovascular-kidney-metabolic （CKM） syndrome， this study aims to construct and validate a diagnostic machine learning model integrating traditional Chinese and Western medicine for severe coronary artery stenosis in patients with CKM， thereby providing clinical decision-making support for patients with borderline lesions. MethodsBased on a retrospective study design， a total of 535 hospitalized patients from two independent campuses of Jiangsu Province Hospital of Traditional Chinese Medicine： the main campus （from January 2024 to August 2024） and Zidong branch （from September 2024 to December 2024） were screened. Data from the main campus were randomly divided into the training dataset （376 cases） and the internal validation dataset （95 cases） at a 4∶1 ratio， while data from Zidong branch served as the external validation dataset （64 cases）. Risk factors were analyzed and screened through literature review， expert interviews， and the least absolute shrinkage and selection operator （LASSO） algorithm. Nine machine learning algorithms were utilized to construct diagnostic models. Comparative analyses of common evaluation metrics， calibration curves， and decision curves were conducted to select the model through internal and external validation. The Shapley additive explanations （SHAP） method and two cases were utilized to help understand the operational logic of the best model. Finally， the best model was applied to patients with borderline lesions to calculate diagnostic efficacy. ResultsNine risk factors were screened by LASSO regression， such as phlegm， hematoma， stagnation， deficiency， hypertension duration， gender， arterial stiffness index （ASI）， uric acid to high-density lipoprotein cholesterol ratio （UHR）， and glycosylated hemoglobin （HbA1c）. After comparison from multiple dimensions， the light gradient boosting machine （LightGBM） was the best model， achieving area under the curve （AUC） of 0.918 （95% confidence interval （CI）： 0.890-0.945） in the training dataset， 0.885 （95%CI： 0.820-0.951） in the internal validation dataset， and 0.897 （95%CI： 0.818-0.975） in the external validation dataset. Calibration curves indicated good consistency in the predicted probabilities， while decision curve analysis showed clinical benefit when threshold probabilities were less than 90%. SHAP importance rankings were stagnation， deficiency， hematoma， HbA1c， gender， phlegm， hypertension duration， ASI， and UHR. When applied to the patients with borderline lesions， the diagnostic model achieved an AUC of 0.783 （95%CI： 0.637-0.930）， with 73% of patients with actual severe stenosis getting benefit. ConclusionGuided by clinical value， the diagnostic model integrating traditional Chinese and Western medicine established in this study demonstrates favorable performance， providing a basis for clinical diagnosis， treatment， and decision-making in patients with CKM.

[摘 要] 目的：探讨lncRNA DLEU2通过EZH2/H3K27me3途径调控IKKα介导甲状腺癌（TC）放射性碘抵抗的作用机制。方法：利用TCGA数据库分析TC中DLEU2的表达及其与EZH2的相关性。构建放射性碘抵抗的TPC-1细胞（RR-TPC-1细胞）模型及裸鼠移植瘤模型，通过敲低或过表达DLEU2（si-DLEU2/OE-DLEU2）、抑制EZH2（UNC1999）、过表达IKKα（OE-IKKα）进行干预，采用qPCR、WB、RIP、ChIP、CCK-8、流式细胞术、TUNEL染色及体内成瘤实验检测基因与蛋白表达、表观修饰、细胞增殖、凋亡及肿瘤生长。结果：TCGA分析显示，DLEU2在TC组织中显著上调（P < 0.001），与患者不良预后相关（P = 0.008 4），且与EZH2表达呈正相关（r = 0.390, P < 0.001）；RIP证实EZH2与DLEU2存在相互作用/结合（P < 0.05）。体外实验表明，敲低DLEU2可显著下调RR-TPC-1细胞中EZH2、IKKα表达及H3K27me3修饰水平，抑制NF-κB通路活化（P < 0.05或P < 0.01），抑制细胞增殖、促进凋亡（均P < 0.05）。联合敲低DLEU2与抑制EZH2进一步增强上述效应，而过表达IKKα则可部分逆转上述效应（P < 0.05或P < 0.01）。体内实验进一步证实，敲低DLEU2联合抑制EZH2可显著抑制移植瘤生长，增加肿瘤细胞凋亡（均P < 0.01）；IKKα过表达则部分逆转上述抗肿瘤效应（P < 0.05或P < 0.01）。结论：lncRNA DLEU2通过招募EZH2催化H3K27me3修饰，间接激活IKKα/NF-κB信号并形成正反馈环路，介导TPC-1细胞131I抵抗。

Objective To investigate the prevalence of thyroid nodules in the physical examination population in Mianyang region and analyze its association with metabolic syndrome. Methods A retrospective study was conducted on 9 978 individuals who underwent health examinations at our hospital from January 2024 to May 2025. Thyroid examinations were performed using color Doppler ultrasound to analyze the prevalence of thyroid nodules in this population. Clinical data of all subjects were collected, and logistic regression analysis was employed to assess the association between metabolic syndrome and the risk of thyroid nodule development. Results The prevalence of thyroid nodules in the physical examination population of Mianyang region was 17.98% (1 794/9 978). The logistic regression results showed that after adjusting for gender, age, BMI, occupation, consumption of non-iodized salt, staying up late, daily sleep duration, anxiety, and depression, metabolic syndrome (OR=6.593, 95% CI: 3.961-10.975) was associated with thyroid nodules (P<0.05). Conclusion The prevalence of thyroid nodules among the physical examination population in the Mianyang area is 17.98%, and metabolic syndrome remains associated with the risk of thyroid nodules after effectively controlling for confounding factors.

ObjectiveTo explore the expression levels of long non-coding RNA （lncRNA） H19 and microRNA-22-3p （miR-22-3p） in the serum of patients with acute myocardial infarction （AMI） and their diagnostic value for AMI. MethodsA total of 176 AMI patients were selected as the experimental group and were divided into ST-segment elevation myocardial infarction （STEMI） group （n=95） and non-ST-segment elevation myocardial infarction （NSTEMI） group （n=81） based on their medical history and electrocardiogram. Meanwhile， 156 patients with negative angiography during the same period were selected as the control group （CON group）. The relative expression levels of lncRNA H19 and miR-22-3p in the serum of the two groups were detected by real-time fluorescence quantitative polymerase chain reaction. The diagnostic value of miR-22-3p and lncRNA H19 in AMI was evaluated by receiver operating characteristic curve （ROC） analysis. The levels of serum cardiac troponin I （cTnI）， creatine kinase （CK）， creatine kinase MB isoenzyme （CK-MB）， and high-sensitivity C-reactive protein （CRP） were detected in all study subjects. Spearman correlation analyses were used to evaluate the correlations between lncRNA H19 and miR-22-3p and cTnI， CK， CK-MB and CRP. ResultsCompared with the control group， the expression level of lncRNA H19 was upregulated and the expression level of miR-22-3p was downregulated in the AMI group （all P<0.01）； compared with the NSTEMI group， the expression level of lncRNA H19 was upregulated and the expression level of miR-22-3p was downregulated in the STEMI group （P<0.01）. The areas under the ROC curves （AUC） for the diagnosis of AMI by miR-22-3p， lncRNA H19 and their combination were 0.555， 0.977， and 0.983， respectively. lncRNA H19 was positively correlated with cTnI， CK， CK-MB and CRP （P<0.05）， and negatively correlated with left ventricular ejection fraction （LVEF） （P<0.001）； miR-22-3p was negatively correlated with cTnI， CK and CRP， and positively correlated with LVEF （P<0.05）. ConclusionThe expression level of lncRNA H19 is elevated and the expression level of miR-22-3p decreased in AMI patients， and their levels may have potential value as an auxiliary biomarker for AMI diagnosis and cardiac function assessment.

ObjectiveTo observe the effect of Yifei Jianpi prescription on the of signal transducer and activator of transcription protein 1 （STAT1）/interferon regulatory factor 3 （IRF3） signaling pathway in a pneumonia model induced by lipopolysaccharide （LPS） and to explore the mechanism of Yifei Jianpi prescription in improving lung immune and inflammatory responses. MethodsSixty male SPF SD rats were used in this study. Ten rats were randomly assigned to the normal control group， and the remaining 50 were instilled with LPS in the trachea to establish a pneumonia model. After successful modeling， the rats were randomly divided into the model group， dexamethasone group （0.5 mg·kg^-1）， and Yifei Jianpi prescription high-dose （12 mg·kg^-1）， medium-dose （6 mg·kg^-1）， and low-dose （3 mg·kg^-1） groups， with 10 rats in each group. Treatment was administered once daily， and the normal control and model groups received the same volume of normal saline. After 14 days， flow cytometry was used to detect the classification of whole blood lymphocytes. Enzyme-linked immunosorbent assay （ELISA） was used to measure serum levels of immunoglobulin G （IgG）， immunoglobulin A （IgA）， immunoglobulin M （IgM）， and the content of tumor necrosis factor-α （TNF-α）， interleukin-8 （IL-8）， interleukin-6 （IL-6）， and interleukin-10 （IL-10） in alveolar lavage fluid （BALF）. Hematoxylin-eosin （HE） staining was used to observe lung tissue pathology and score the damage. Thymus weight， spleen weight， and wet-to-dry weight ratio （W/D） were recorded. Real-time fluorescence quantitative polymerase chain reaction （Real-time PCR） was used to detect the mRNA expression of STAT1， IRF3， IL-6， and interferon-alpha （IFN-α） in lung tissues， while Western blot was performed to assess the protein expression of STAT1， IRF3， IL-6， and IFN-α. ResultsCompared with the normal control group， the model group showed significantly increased proportion of B lymphocytes in peripheral blood， decreased proportions of NK cells and CD4⁺/CD8⁺ （P<0.05， P<0.01）， decreased serum levels of IgG and IgA， significantly increased IgM levels （P<0.01）， significantly elevated content of TNF-α， IL-6， and IL-8 in BALF， and significantly decreased IL-10 levels （P<0.01）. Lung tissue damage was evident， with significant increases in thymus and spleen weights and a higher W/D ratio （P<0.01）. The mRNA and protein expression of STAT1， IRF3， IFN-α， and IL-6 in lung tissues was significantly upregulated （P<0.05，P<0.01）. Compared with the model group， the Yifei Jianpi prescription groups showed significantly reduced proportions of B lymphocytes in peripheral blood， increased proportions of NK cells and CD4⁺/CD8⁺ ratios （P<0.05， P<0.01）， significantly increased serum levels of IgG and IgA， significantly decreased IgM levels （P<0.05， P<0.01）， significantly reduced levels of TNF-α， IL-6， and IL-8 in BALF， and significantly increased IL-10 levels （P<0.01）. Lung tissue damage was alleviated， thymus and spleen weights were significantly reduced， and the W/D ratio was markedly decreased （P<0.01）. The mRNA and protein expression of STAT1， IRF3， IFN-α， and IL-6 in lung tissues was significantly downregulated （P<0.05， P<0.01）. ConclusionYifei Jianpi prescription can alleviate lung tissue damage and improve immune and inflammatory responses in LPS-induced pneumonia rats. The mechanism may be related to the inhibition of STAT1/IRF3 signaling pathway activation.

Objective:To analyze the incidence of adverse treatment outcome of rifampicin-resistant tuberculosis (RR-TB) patients and influencing factors in 9 provinces in China.Methods:The information about the basic characteristics, diagnosis and treatment of RR-TB patients registered from January 1, 2017 to December 31, 2021 in 9 provinces (Beijing, Jilin, Shanghai, Jiangsu, Zhejiang, Hubei, Henan, Yunnan and Guizhou) were collected from the tuberculosis information management sub-system of China Disease Control and Prevention Information System for a descriptive analysis, and the influencing factors of adverse treatment outcome were identified by binary logistic regression analysis.Results:In 18 204 RR-TB patients in this study a total of 6 852 had adverse treatment outcomes (37.64%). Treatment failure occurred in 1 031 patients, 1 272 patients died, 2 284 patients were lost to follow-up, and 2 265 patients were not evaluated. The results of multivariate logistic regression analysis showed that being man (a OR=1.54, 95% CI: 1.43-1.66), age ≥65 years (a OR=2.42, 95% CI: 2.20-2.67), being from other ethnic groups (a OR=1.18, 95% CI: 1.03-1.35), being farmer (a OR=1.22, 95% CI: 1.13-1.32), being retired with honours or being retired (a OR=1.16, 95% CI: 1.00-1.35) and being floating population (a OR=1.23, 95% CI: 1.13-1.34), re-treatment (a OR=1.33, 95% CI: 1.24-1.42), long-term treatment therapy (a OR=3.26, 95% CI: 2.52-4.23), living in central provinces (a OR=2.76, 95% CI: 2.55-2.99), living in western provinces (a OR=2.31, 95% CI: 2.08-2.57) were the influencing factors for adverse treatment outcome of RR-TB. Conclusions:The incidence of adverse outcomes in RR-TB patients in 9 provinces in China was higher from 2017 to 2021. There were many influencing factors associated with adverse treatment outcome in RR-TB patients, especially the area specific factors. The national tuberculosis control program should strengthen the follow-up and treatment management of RR-TB in men, the elderly, floating population, people in central and western provinces and other key groups, actively promote the short-term treatment program and improve the allocation of medical resources to reduce the influencing for adverse treatment outcomes in RR-TB patient.

Objective:To apply artificial intelligence（AI）in classifying ovarian tumors on ultrasound images，and compare the diagnostic results of several sonographers with varying seniority levels.Methods:A total of 645 patients diagnosed with adnexal masses via gynecological ultrasound examination at Qilu Hospital of Shandong University from January 2021 to December 2024 were enrolled. Three deep learning architectures，i.e.，Alexnet，Densenet121，and Resnet50 were developed and used to internally test the classification effectiveness of ovarian tumors，while the optimal model was selected for external testing. Two junior sonographers and two senior sonographers were recruited to independently diagnose ovarian tumors in the external test dataset. Subsequently，the benign and malignant results of the model's predictions were disclosed to each sonographer，and their revised diagnoses on the same external test data in combination with the best AI model were recorded.Results:The optimal model achieved an accuracy of 0.941，sensitivity of 0.936，and specificity of 0.944 on the internal test dataset，and maintained robust performance on the external test dataset with accuracy of 0.891，sensitivity of 0.880，and specificity of 0.907. Compared to junior sonographers，the optimal model demonstrated significantly higher sensitivity in discriminating benign from malignant ovarian tumors（0.880 vs. 0.723，0.602；all P<0.05）. No statistically significant difference was observed in diagnostic accuracy between the optimal model and senior sonographer 1（ P=0.05）. With assistance from the optimal model，junior sonographers achieved significant improvements in both sensitivity and specificity（sensitivity：0.723 vs. 0.843，0.602 vs. 0.819；specificity：0.778 vs. 0.833，0.685 vs. 0.741；all P<0.05）. Conclusions:The optimal model achieves comparable performance to that of senior sonographers in ovarian tumor classification. With model assistance，the diagnostic performance of junior sonographers is significantly improved.

Objective:To analyze the characteristics of transabdominal bowel ultrasound (TBUS) in immune checkpoint inhibitor-related colitis (IRC) and their correlation with endoscopic manifestations.Methods:A cross-sectional study was conducted. Clinical data from 10 patients with IRC treated at Peking Union Medical College Hospital from January 2022 to January 2024 were collected. The ulcerative colitis endoscopic index of severity (UCEIS) and Limberg classification were used to assess the severity of colonoscopy and TBUS examinations, respectively. Kendall's tau-b method was applied for correlation analysis between UCEIS scores and Limberg classification.Results:All the 10 patients were male with a median age of 65 years (59-74 years). The majority had lung cancer (8 patients) and all were in advanced stages, with 6 patients in stage Ⅲ and 4 in stage Ⅳ. They all received anti-programmed death 1 (PD-1) /anti-programmed death ligand 1 (PD-L1) combined with chemotherapy, among whom 2 patients were combined with anti-angiogenic drug treatment. The median time from the first immunotherapy to the onset of IRC was 1.50 (0.25-12.00) months; the median time from IRC treatment to clinical symptom relief to G1 was 2.45 (0.50-8.00) weeks. Nine patients were in the active phase, mainly G3 (8 patients) ; 1 was in the remission phase after treatment. TBUS showed that among the 9 active IRC patients, the entire colon was mainly involved (7 patients), with combined small intestine involvement (3 patients) ; the main manifestations were thickening of the bowel wall, with the thickest bowel wall being 7.0 (5.0-8.0) mm, mainly located in the sigmoid colon (3 patients) and descending colon (3 patients) ; increased bowel wall blood flow signals (Limberg classification 2-4) occurred in 7 patients; 3 active patients had perienteric fat wrapping, and 2 had blurred bowel wall stratification. The Kendall's tau-b correlation coefficient r between the entire colon UCEIS scores and Limberg classification was 0.891 ( P = 0.003), and the Kendall's tau-b correlation coefficient r between the colon segment UCEIS scores and Limberg classification was 0.690 ( P < 0.001) . Conclusion:During the active phase, the left colon of IRC is more severe in TBUS, which mainly manifests as the thickening bowel wall and increased blood flow signals, and the TBUS has good correlation with colonoscopy evaluation.

Objective:To assess the effect of transversus abdominis plane block (TAPB) with liposomal bupivacaine and general anesthesia on postoperative delirium (POD) in elderly patients with prior novel coronavirus pneumonia (COVID-19).Methods:In this randomized double-blind controlled study, 416 patients of either sex, aged 65-90 yr, weighing 50-90 kg, of American Society of Anesthesiologists Physical Status classification Ⅰ-Ⅲ, diagnosed as having COVID-19 within 6 months prior to surgery, who underwent laparoscopic colorectal cancer surgery under combination of elective TAPB and combined intravenous-inhalational general anaesthesia at Qingdao Municipal Hospital from June 2023 to December 2024, were selected. The patients were divided into liposomal bupivacaine group ( n=208) and bupivacaine hydrochloride group ( n=208) using the random number table method. After induction of anaesthesia, bilateral TAPB was performed with liposomal bupivacaine injectio 266 mg (40 ml) in liposomal bupivacaine group and with 0.5% bupivacaine hydrochloride 40 ml in bupivacaine hydrochloride group. The primary outcome measure was the occurrence of POD within 7 days after surgery. Secondary outcome measures included severity of POD, pain scores at 24, 48 and 72 h after operation, the rate of postoperative rescue analgesia and consumption of morphine, duration of post-anesthesia care unit stay, and length of hospital stay. The occurrence of complications such as death, reoperation, atelectasis and pneumonia was recorded at 30 days after surgery. Results:Compared with bupivacaine hydrochloride group, the incidence of POD was significantly decreased (21.5% [43/200]versus 12.0% [24/200]), pain scores at 24, 48 and 72 h after operation were decreased, the rate of postoperative rescue analgesia and consumption of morphine were decreased, and the duration of post-anesthesia care unit stay and length of hospital stay were shortened in liposomal bupivacaine group ( P<0.05). There was no significant difference in the severity of POD and the case fatality rate and related complications within 30 days after surgery between the two groups ( P>0.05). Conclusions:Liposomal bupivacaine TAPB combined with general anesthesia can reduce the development of POD in elderly patients with prior COVID-19.