[摘 要] 目的：系统评价CD19 CAR-T细胞免疫疗法对比标准治疗在复发/难治性弥漫性大B细胞淋巴瘤（R/R DLBCL）患者中疗效与安全性，通过探索性分析探索不同CAR-T细胞产品对疗效的潜在影响，为临床治疗决策提供循证参考。方法：计算机检索中国知网、万方数据库、维普期刊数据库、中国生物医学文献数据库、PubMed、Embase和Cochrane Library数据库，搜集CD19 CAR-T细胞疗法对比标准治疗用于R/R DLBCL的随机对照试验（RCT），检索时限均为建库至2025年10月25日。由2位研究者独立进行文献筛选、数据提取和质量评价，采用R4.2.2软件进行Meta分析。结果：共纳入2项Ⅲ期RCT研究（ZUMA-7、TRANSFORM），各结局指标均根据异质性检验结果选择固定效应模型合并数据。疗效方面，CD19 CAR-T细胞疗法可显著改善患者无事件生存期（HR=0.455，95%CI：0.363~0.570，P < 0.001）和降低死亡风险（HR = 0.738，95%CI：0.575~0.947，P = 0.017）；同时可显著提高完全缓解率（RR = 1.879，95%CI：1.574~2.242，P < 0.001）。按产品类型的探索性分析显示，liso-cel和axi-cel均优于标准治疗（liso-cel：HR = 0.380，95%CI：0.260~0.540，P < 0.001；axi-cel：HR = 0.510，95%CI：0.380~0.680，P < 0.001），但该分析为不同试验间的对比，证据等级有限。安全性结局显示：CAR-T细胞疗法的免疫效应细胞相关神经毒性综合征（ICANS）发生率显著升高（RR = 22.387，95%CI：4.353~115.132，P < 0.001）；≥3级细胞因子释放综合征（CRS）发生率数值升高（RR = 8.181，95%CI：0.935~71.574，P = 0.058），差异无统计学意义。纳入研究的偏倚风险整体为低；敏感性分析证实结果稳健。结论：基于2项RCT的Meta分析结果，CD19 CAR-T细胞（liso-cel/axi-cel）可作为R/R DLBCL二线治疗的选择之一，其疗效优于标准治疗，且特征性不良反应（CRS/ICANS）经规范管理后可控，可根据患者基线状态个体化选择CAR-T细胞产品。本研究证据基础薄弱，上述结论尚需更多高质量、大样本RCT验证。

Osteoarthritis (OA), a highly prevalent degenerative joint disease worldwide, is defined by articular cartilage degradation, abnormal bone remodeling, and persistent chronic inflammation. It severely compromises patients’ quality of life, and currently, there is no radical cure. Abnormal mechanical stress is widely regarded as a core driver of OA pathogenesis, and the exploration of mechanical signal perception and transduction mechanisms has become crucial for deciphering OA’s pathophysiological processes. Piezo1, a key mechanosensitive cation channel belonging to the Piezo protein family, has recently gained significant attention due to its pivotal role in mediating cellular responses to mechanical stimuli in joint tissues. This review systematically examines Piezo1’s expression patterns, regulatory mechanisms, and pathological functions in OA, with a particular focus on its dual roles in modulating chondrocyte homeostasis and bone metabolism disorders, while also delving into the underlying molecular signaling pathways and potential therapeutic implications. Piezo1, consisting of approximately 2 500 amino acids and forming a unique trimeric propeller-like structure, is widely expressed in chondrocytes, osteocytes, mesenchymal stem cells, and synovial cells. It exhibits permeability to cations such as Ca²⁺, K⁺, and Na⁺, and directly responds to membrane tension changes induced by mechanical stimuli like fluid shear stress and mechanical overload. In OA patients and animal models, Piezo1 expression is significantly upregulated, especially in cartilage regions subjected to abnormal mechanical stress (e.g., human temporomandibular joint cartilage). This overexpression is closely associated with aggravated cartilage degeneration, increased chondrocyte apoptosis, accelerated cellular senescence, and intensified inflammatory responses. Mechanical overload and pro-inflammatory cytokines (e.g., IL-1β) are key inducers of Piezo1 upregulation: IL-1β activates the PI3K/AKT/mTOR signaling pathway to enhance Piezo1 expression, forming a pathogenic positive feedback loop that inhibits chondrocyte autophagy, promotes apoptosis, and further accelerates joint degeneration. Mechanistically, Piezo1 mediates OA progression through multiple interconnected pathways. When activated by mechanical stress, Piezo1 triggers excessive Ca²⁺ influx, leading to endoplasmic reticulum stress (ERS) and mitochondrial dysfunction, which directly induce chondrocyte apoptosis. This process involves the activation of downstream signaling cascades such as cGAS-STING and YAP-MMP13/ADAMTS5. YAP, a transcriptional regulator, upregulates the expression of matrix metalloproteinase 13 (MMP13) and aggrecanase (ADAMTS5), thereby accelerating cartilage matrix degradation. Additionally, Piezo1-driven Ca²⁺ overload promotes the accumulation of reactive oxygen species (ROS) and upregulates senescence markers (p16 and p21), accelerating chondrocyte senescence via the p38MAPK and NF-κB pathways. Senescent chondrocytes secrete senescence-associated secretory phenotype (SASP) factors (e.g., IL-6, IL-1β), further amplifying joint inflammation. In terms of bone metabolism, Piezo1 maintains joint homeostasis by promoting the differentiation of fibrocartilage stem cells into chondrocytes and balancing bone formation and resorption through regulating the FoxC1/YAP axis and RANKL/OPG ratio. Therapeutically, targeting Piezo1 shows promising potential. Preclinical studies have demonstrated that Piezo1 inhibitors (e.g., GsMTx4) can reduce joint damage and alleviate pain in OA mice. Simultaneously, siRNA-mediated co-silencing of Piezo1 and TRPV4 (another mechanosensitive channel) decreases intracellular Ca²⁺ concentration, inhibits chondrocyte apoptosis, and promotes cartilage repair. Conditional knockout of Piezo1 using Gdf5-Cre transgenic mice alleviates cartilage degeneration in post-traumatic OA models by downregulating MMP13 and ADAMTS5 expression. Despite existing challenges, such as off-target effects of inhibitors, inefficient local drug delivery, and interindividual genetic variability, strategies like developing selective Piezo1 antagonists, optimizing targeted nanocarriers, and combining Piezo1-targeted therapy with physical therapy provide viable avenues for clinical translation. The authors propose that Piezo1 serves as a critical therapeutic target for OA, and future research should focus on deciphering its context-dependent regulatory networks, developing tissue-specific intervention strategies, and validating their efficacy and safety in clinical trials to address the unmet medical needs of OA patients.

Objective: To examine the prevalence of e-cigarette use and associated factors among primary and secondary school students in Pudong New Area, Shanghai, and to explore the pathways linking harm perception of e-cigarettes, interpersonal social influence, and attitudes toward e-cigarette use with experimentation behavior, in order to provide scientific basis for further optimizing the prevention and control strategies of e-cigarette among adolescents. Methods: From September to October 2025, a multi stage cluster random sampling method was used to select 5 144 primary and secondary school students aged 8-19 years from 47 primary and secondary schools in Pudong New Area, Shanghai, to conduct an anonymous questionnaire survey. The questionnaire collected information on e-cigarette use, harm perception of e-cigarette, interpersonal social influence, and attitudes toward e-cigarette use. The Chi-square test was applied to analyze the differences in cigarette and e-cigarette use behavior among primary and secondary school students with different demographic characteristics. Structural equation modeling (SEM) was constructed using Mplus 8.3 software, and the bootstrap method was utilized to test the mediating effects. Results: The reported rates of cigarette experimentation, e-cigarette experimentation, and current e-cigarette use among primary and secondary school students were 1.85%, 2.10%, and 0.70%, respectively. Higher reporting rates of e-cigarette experimentation were observed among boys (2.82%), secondary school students aged 16-19 (3.39%), senior high school students (3.05%), and those with weekly pocket money >100 yuan ( 6.11% ) ( χ 2=11.67, 8.61, 8.00, 54.18, all P <0.05). Structural pathway analysis results demonstrated that e-cigarette harm perception positively predicted interpersonal social influence ( β =0.61) and e-cigarette use attitude ( β = 0.53 ), and interpersonal social influence ( β =0.65) and e-cigarette use attitude ( β =0.25) further promoted e-cigarette experimentation behavior among primary and secondary school students (all P <0.01), with interpersonal social influence playing a major mediating role (indirect effect=0.40). Conclusions E-cigarette experimentation behavior among primary and secondary school students in Pudong New Area is jointly influenced by multiple psychosocial factors. Intervention strategies should strengthen interpersonal social factors such as family and peers on the basis of health education, thereby constructing a comprehensive prevention and control strategy for e-cigarette use among primary and secondary school students.

Objective:To develop and validate a prediction model for medication adherence among patients receiving allergen sublingual immunotherapy (SLIT).Methods:A prospective cross-sectional study was conducted, and a total of 288 patients who received SLIT treatment at an allergy center in the First Affiliated Hospital with Nanjing Medical University (Jiangsu Province Hospital) from December 2023 to July 2024 were assigned to the modeling group. Additionally, 122 patients from August to October 2024 were assigned to the validation group. Data of patients′ general information, medication beliefs, anxiety levels, social support, disease perception, and medication adherence were collected. Single-factor analysis and LASSO regression were utilized to identify potential predictors, and a prediction model for medication adherence was constructed using multifactorial logistic regression. A nomogram was then developed based on the model. The model′s discriminatory ability was evaluated using receiver operating characteristic curve (ROC), the area under curve (AUC), sensitivity, and specificity. The model was then validated in the validation cohort.Results:Single-factor analysis and LASSO regression identified a total of nine predictive factors. Logistic regression revealed that medical belief tendency [ OR (95% CI) =2.420 (1.116-5.248), P=0.025], the somatic control dimension in self-rating anxiety scales [ OR (95% CI)=1.404 (1.241-1.589), P<0.001], the subjective support dimension in social support assessment [ OR (95% CI)=0.784 (0.725-0.847), P<0.001], and the cognitive dimension in illness perception [ OR (95% CI)=0.725 (0.647-0.813), P<0.001] were independent predictors of medication adherence in patients undergoing SLIT. The AUC value of the model was 0.899 (95% CI=0.863-0.934) in the modeling group and 0.882 (95% CI=0.820-0.944) in the validation group, indicating good discriminatory ability. The optimal cutoff value of the model was 0.493, with a sensitivity of 81.1% and specificity of 85.7% in the modeling group, and a sensitivity of 87.3% and specificity of 82.5% in the validation group. Conclusion:The medication adherence prediction model developed in this study for patients undergoing SLIT exhibits good predictive performance and provides valuable guidance for early intervention by clinical healthcare professionals.

Objective:To identify factors associated with depressive symptoms in maintenance hemodialysis patients and to examine the relationship between these symptoms and mortality.Methods:Between January and December 2019, patients who received maintenance hemodialysis in the Blood Purification Center of the Second Affiliated Hospital of Nanjing Medical University were enrolled in a prospective cohort study. Depressive symptoms were assessed using the internationally validated patient health questionnaire-8 (PHQ-8). Sleep quality and anxiety were measured with the Pittsburgh sleep quality index (PSQI) and the generalized anxiety disorder-7 (GAD-7) scale, respectively. Follow-up continued until December 31, 2022, with all-cause mortality as the primary outcome. Ordinal logistic regression was used to identify independent predictors of depression severity. Cox proportional hazards models evaluated the association between depressive symptoms and mortality.Results:A total of 532 maintenance hemodialysis (MHD) patients completed the study. Among them, 177 (33.3%) exhibited depressive symptoms. Compared with patients without depression, those with mild or moderate-to-severe depression were older [median age: 58 (50, 66) vs. 60 (55, 65) vs. 55 (46, 64)], more likely to smoke [35.9% (51/142) vs. 40.0% (14/35) vs.26.2% (93/355)], had poorer sleep quality [PSQI: 9 (6, 13) vs. 12 (9, 17) vs. 5 (3, 9)], and higher anxiety levels [GAD-7: 1 (0, 3) vs. 3 (1, 6) vs. 0 (0, 1)], the differences among the three groups were statistically significant (all P<0.05). Ordinal logistic regression identified smoking status, history of diabetes or cardiovascular disease, hemoglobin level, PSQI score, and GAD-7 score as independent predictors of depression severity ( OR=1.60, 1.80, 1.81, 0.98, 3.67, 8.67; all P<0.05). After a median follow-up of 40 (35, 44) months, 109 patients died, including 66 (60.6%) from cardio-cerebrovascular causes and 24 (22.0%) from infections. Kaplan-Meier analysis revealed significantly lower cumulative survival in the depression group compared to the non-depression group ( P<0.001). Cox regression analysis demonstrated that depressive symptoms remained independently associated with all-cause mortality after adjusting for confounders ( HR=1.06, 95% CI 1.00-1.13, P=0.048), with an even stronger association observed for patients with PHQ-8 scores≥2.9 ( HR=1.10, 95% CI 1.03-1.16, P=0.005). However, the associations between depression and cardio-cerebrovascular mortality ( P=0.111) or infection-related mortality ( P=0.509) were not statistically significant. Conclusions:Depressive symptoms are prevalent among maintenance hemodialysis patients and are independently associated with increased all-cause mortality. Smoking, comorbid diabetes or cardiovascular disease, low hemoglobin level, poor sleep quality, and anxiety are risk factors contributing to depression. Maintenance hemodialysis patients with PHQ-8 scores≥3 should be considered at heightened risk for mortality.

OBJECTIVE T o understand the resistance capability of berberine combined with levofloxacin in vitro to methicillin-resistant Staphylococcus aureus(MRSA).METHODS Totally 100 strains of MRSA were isolated from Shanghai Eighth People's Hospital from Jul.2023 to Jun.2024,the minimum inhibitory concentrations(MICs)of berberine and levofloxacin against the strains were determined by microbroth dilution method.The anti-bacterial effect of the combined use of the two drugs on MRSA was observed.The effects of the single and com-bined use of berberine and levofloxacin on sterilization of MRSA standard strain USA 300-LAC and their influence on cellular morphology were observed.The influence of berberine on mRNA levels of drug resistance genes and ef-flux pump-related genes in the standard strain USA 300-LAC and clinical strains was determined by means of real-time fluorescent quantitative PCR.RESULTS Among the 100 clinical isolates of MRSA,most of them were isola-ted from respiratory tract specimens of respiratory medicine department and geriatrics department,and 75.00％of the strains were resistant to levofloxacin.The MICs of both berberine and levofloxacin against the standard strain USA300-LAC were 128 μg/ml;the MICs against the clinical MRSA isolates were 64 to 1024 μg/ml and 0.125 to 256 μg/ml,respectively.Berberine combined with levofloxacin showed additive effect on 17(44.74％)strains of MRSA.The combined use of the two drugs showed the most remarkable in vitro antibacterial effect.The cellular structures of the strains were unclear and the contents of bacterial bodies increased when berberine and levofloxacin were used in combination.Berberine showed various degree of inhibitory effect on gyrA,gyrB,grlA and nor A genes.CONCLUSIONS Berberine is capable of resisting the MRSA and shows synergistic or additive effect with levofloxacin.The combined use of the two drugs may reduce the MIC against levofloxacin,destroy the morpholog-ical structure,and inhibit the expression of gyrA,gyrB,grlA and nor A genes.

Objective:To investigate the value of 3.0T magnetic resonance imaging(MRI)in the assessment for clinically pathologic features of prostate cancer(PCa).Methods:The case data of 102 PCa patients who were confirmed by histology in The Second Affiliated Hospital of Bengbu Medical University from April 2020 to July 2024 were retrospectively analyzed.51 PCa patients of them were included in cancer group,and 51 patients with prostatic hyperplasia were included in hyperplasia group.Multi-parametric MRI was implemented in all patients.Imaging performance was scored by using Prostate Imaging Reporting and Data System version 2.1(PI-RADS v2.1).The distribution of scores between the two groups was compared and analyzed.The correlations between the PI-RADS v2.1 scores and PCa clinically pathological features which included Gleason scores,serum prostate specific antigen(PSA)level,tumor staging and aggressiveness were analyzed and explored by using Spearman correlation analysis.The diagnostic efficacy of the 3.0T MRI technique for PCa was analyzed by using the receiver operating characteristic(ROC)curve.Results:The PI-RADS v2.1 score of cancer group was 4(4,5)scores,which was significantly higher than 2(3,4)scores of hyperplasia group,and the difference of that between two groups was significant(U=369.5,P<0.05).The rate of high scores(4,5 scores)of cancer group was more than that of hyperplasia group in the distribution of scores,and the difference of that between two groups was significant(x2=51.220,P<0.05).In patients of cancer group,PI-RADS v2.1 score showed significant correlation with serum PSA level,Gleason score,tumor staging and aggressiveness(P<0.05),respectively,and they were significant(r=0.815,0.723,0.693,P<0.05).ROC curve analysis showed that the area under curve(AUC)value of PI-RADS v2.1 score was 0.858(95%CI:0.783-0.932)in diagnosing PCa,which can effectively distinguish PCa and prostate hyperplasia.It had higher diagnostic efficacy,which was higher than that of serum PSA.Conclusions:3.0T MRI has favorable accuracy in diagnosing PCa,and it can be used as a noninvasive test method to diagnose and assess pathological features of tumor.

Objective To investigate the feasibility and accuracy of convolutional neural networks for automatically delineating the pancreatic cancer gross target volume(GTV)in pancreatic enhanced CT.Methods The localizable enhanced CT images of 114 patients with pancreatic cancer were retrospectively selected,in which the GTV was manually delineated using AccuContour.The imaging data were then import to AccuLearning and randomly divided as the training set,validation set and test set at a ratio of 8:1:1.Flex and Segresnet were used to train the automatic segmentation model,with each network structure trained continuously 3 times using fixed training parameters.The model was evaluated in terms of Dice similarity coefficient(DSC),95%Hausdorff distance(HD95),average symmetric surface distance(ASSD)and relative volume difference(RVD).Results In the model training phase,Flex-3 test results in Flex group were the worst,with a minimum DSC of 0.14%and an average DSC of 56.30%,while Flex-1 performed well,achieving a minimum DSC of 47.90%and an average DSC of 67.35%.Meanwhile,Segresnet-2 in Segresnet group had the worst test results,with a minimum DSC of 0.00%and an average DSC of 42.46%,while Segresnet-3 test results were better,with a minimum DSC of 42.65%and an average DSC of 63.28%.In the fixed testing phase,the best results among all were as follows:average DSC and RVD values of 63.88%and 29.41%in Segresnet-3 group,average ASSD value of 4.43 mm in Segresnet-2 group,and average HD95 value of 12.87 mm in Segresnet-1 group.Conclusion Both Flex and Segresnet architectures of convolutional neural network can be used for the automatic pancreatic tumor GTV segmentation training,with Segresnet performing better in comprehensive evaluation.

Objective To predict the invasion degree of subsolid nodule(SSN)lung adenocarcinoma using a combined model incorporating artificial intelligence(AI)quantitative parameters and imaging signs,and to validate the predictive efficacy of this model.Methods A total of 281 SSN lung adenocarcinoma CT images in 243 patients were retrospectively collected and randomly divided into training set(224 cases)and validation set(57 cases)in an 8∶2 ratio,with atypical adenomatous hyperplasia(A AH)+adenocarcinoma in situ(AIS)+minimally invasive adenocarcinoma(MIA)(191 cases)as the non-invasive adenocarcinoma(I AC)group and I AC(90 cases)as the IAC group.Multivariate logistic regression analysis was performed based on the AI quantitative parameters and CT signs in the training set to obtain independent predictors of IAC.A combined model and nomogram were then constructed and validated.The diagnostic efficacy and clinical applicability of the model were evaluated by the receiver operating characteristic(ROC)curve,calibration curve,and clinical decision curve analysis(DCA).Results Multivariate logistic regression analysis of the training set showed nodule type,spicule sign,vascular abnormality,long diameter＞11.5 mm,median CT value＞—426.25 HU,and mass＞391.5 mg were independent predictors of IAC(P＜0.05).The area under the curve(AUC)of the training set model,based on these independent predictive factors,was 0.915[95％confidence interval(CI)0.875-0.954],and the AUC of the validation set model was 0.903(95％CI 0.824-0.982),indicating both the training set and validation set models had high efficacy in distinguishing IAC.The nomogram model,which quantified these independent factors,demonstrated enhanced predictive power for IAC.The calibration curve indicated good fit of the prediction model,and the clinical DCA showed the model had good clinical applicability.Conclusion The model combining AI quantitative parameters and imaging signs has a higher ability to predict the risk of IAC,compared to a single indicator.It helps clinicians in determining the appropriate surgical timing,formulating surgical methods,and reducing overtreatment.

Objective:It aims to systematically evaluate the current status of knowledge,attitude,and practice(KAP)regarding universal commercial medical insurance among residents of the sample province from the demand-side perspective.Methods:Utilizing a quota sampling method,face-to-face surveys were conducted via the Questionnaire Star platform to collect demographic characteristics and KAP data of the participants.Comparisons of differences among different groups were made using t-tests,analysis of variance,and chi-square tests.Furthermore,multiple linear regression and structural equation modeling were utilized to analyze the influencing factors of KAP,as well as the pathways among these three factors.Results:Out of the 415 valid questionnaires collected,there were notable differences in KAP among respondents with diverse demographic backgrounds.Regression analysis revealed that education level,frequency of health check-ups,and engagement in other commercial health insurances significantly influenced knowledge;education level,involvement in other commercial health insurances,and self-assessed health status were pivotal in shaping attitudes;whereas age,education level,frequency of health check-ups,and participation in other commercial health insurances were critical in affecting practice.The path analysis results indicate that knowledge of universal commercial medical insurance has a significant direct association with attitude(β=0.379,P＜0.001)and practice(β=0.323,P＜0.001).It also influences practice through attitude as a mediator(β=0.016,P＜0.001),but the direct effect of attitude on practice is not significant(β=0.04,P=0.403).Conclusion:While residents in the sample province exhibit a positive attitude towards universal commercial medical insurance,there is a need to enhance their level of knowledge and engagement in practice.It is recommended to strengthen targeted educational and promotional measures to promote the healthy and sustainable development of universal insurance.