OBJECTIVE To compare the antihypertensive efficacy of sacubitril/valsartan versus benazepril in patients with perimenopausal hypertension， as well as their impacts on ventricular remodeling and inflammatory fibrosis. METHODS A total of 206 perimenopausal hypertensive patients in our hospital from January 1， 2023 to December 30， 2024 were retrospectively included.These patients were enrolled and divided into benazepril group （105 cases） and sacubitril/valsartan group （101 cases）. Benazepril group received Benazepril hydrochloride tablet， and sacubitril/valsartan group received Sacubitril valsartan sodium tablet. All patients were treated for 6 months. The blood pressure（systolic blood pressure and diastolic blood pressure） and blood pressure control status before and after treatment， echocardiographic indicators （left ventricular ejection fraction， left ventricular mass index， relative wall thickness， and early-diastolic peak transmitral flow velocity/early-diastolic peak velocity of the mitral annulus）， inflammatory fibrosis related indicators（high-sensitivity C-reactive protein，ratio of monocytes to lymphocytes，and ratio of neutrophils to lymphocytes）， as well as the occurrence of adverse reactions（hypotension，hyperkalemia，and angioedema） were observed in both groups before and after treatment. RESULTS The blood pressure control rate was significantly higher in the sacubitril/valsartan group than in benazepril group （ P ＜0.05）. After treatment， the blood pressure， echocardiographic indicators（except for left ventricular ejection fraction） ，and inflammatory fibrosis related indicators were significantly lower than those before treatment within the same group， and the sacubitril/valsartan group were significantly lower than the benazepril group （ P ＜0.05）. There were no statistically significant differences in the incidence of hypotension， hyperkalemia， angioedema， and overall adverse drug reactions between the two groups （ P ＞0.05）. CONCLUSIONS Compared with benazepril， sacubitril/valsartan provides superior blood-pressure control， reverses ventricular remodeling， attenuates inflammatory fibrosis in perimenopausal hypertensive patients， while maintaining a similar safety profile.

ObjectiveTo analyze the risk factors for long-term cardiovascular events in patients undergoing long-term peritoneal dialysis （PD）， and to construct and validate a visual nomogram prediction model based on multiple parameters. MethodsA prospective cohort study was conducted， consecutively enrolling 248 maintenance PD patients （dialysis duration ≥ 3 months）. Demographic characteristics， clinical indicators， laboratory parameters， and echocardiographic indices （including left ventricular ejection fraction ［LVEF］， ratio of early diastolic mitral inflow velocity to early diastolic mitral annular velocity （E/e’）， etc.） were collected. The composite endpoint was defined as the occurrence of cardiovascular events or cardiovascular death， with non-cardiovascular death as the competing risk and loss to follow-up or the end of follow-up as censoring events. Fine-Gray competing risks model was used to screen independent predictors， based on which a nomogram model was constructed. Internal validation was performed using the Bootstrap method （1 000 resamplings）， and the concordance index （C-index） and time-dependent receiver operating characteristic （time-dependent ROC） curve were calculated to evaluate the model performance. ResultsWith a median follow-up of 29 months （interquartile range： 24–35 months）， 88 patients （35.48%） reached the composite endpoint， including 80 cases of cardiovascular events and 8 cases of cardiovascular death， and 4 patients died of non-cardiovascular causes. Multivariate Fine-Gray analysis revealed that age， diabetes mellitus， hemoglobin （HGB） level and E/e' ratio were independent influencing factors of the composite endpoint. Specifically， each 1-year increase in age was associated with a 3.0% increase in the risk of the composite endpoint （HR=1.030， P=0.006）； patients with diabetes mellitus had a 167.9% higher risk compared with non-diabetic patients （HR=2.679， P=0.007）； each 1g/L increase in HGB level contributed to a 1.5% reduction in the risk （HR=0.985， P=0.003）； and each 0.1 increase in E/e' ratio led to a 7.2% increase in the risk （HR=1.072， P=0.045）. The nomogram model had a C-index of 0.76 （95% CI： 0.698–0.820）， and the AUC of the time-dependent ROC curve reached 0.849 at 23 months of follow-up. ConclusionIncreased age， complicated with diabetes mellitus， decreased HGB， and elevated E/e' ratio are independent risk factors of long-term occurrence of cardiovascular events and cardiovascular death in patients undergoing long-term PD. The nomogram model constructed based on the above variables has good predictive value and clinical applicability， which can provide a reference for cardiovascular risk stratification and individualized intervention in long-term PD patients.

BackgroundNon-suicidal self-injury （NSSI） behavior among adolescents has become a global public health concern. Anxiety and depression are considered key factors influencing NSSI behavior， while social support may play a protective role in alleviating emotional and behavioral issues. However， existing research has primarily focused on the direct impact of individual factors on NSSI behavior， with insufficient exploration of the combined effects of anxiety， depression and social support. ObjectiveTo investigate the direct effect of anxiety on NSSI， the mediating role of depression and the moderating role of social support in relationship between anxiety and NSSI behavior， thus to provide references for the prevention and intervention of NSSI behavior among adolescents. MethodsIn February 2022， a total of 40 820 students in grades 7 to 12 across 10 middle schools in a district of Chengdu were selected as participants， and they were assessed using Generalized Anxiety Disorder Scale-7 item （GAD-7）， Patient's Health Questionnaire Depression Scale-9 item （PHQ-9）， Social Support Scale for Urban Students （SSSUS） and Adolescent Self-Harm Scale （ASHS）. Pearson correlation analysis was conducted to examine the correlations between scale scores among adolescents with NSSI behaviors. Mediation and moderation analyses were performed using Process 3.5 in SPSS， and the significance was tested with bootstrapping. The interaction was visualized by using simple slope analysis. ResultsAmong 34 534 （84.60%） valid respondents， 542 adolescents （1.57%） reported engaging in NSSI behavior. Significant differences in gender， GAD-7 scores， PHQ-9 scores， and SSSUS scores were observed between NSSI behavior group and non-NSSI group （χ²/t=62.889， 71.120， 94.365， -41.464， P<0.01）.Adolesents with NSSI showed positive correlations between GAD-7 scores and both ASHS and PHQ-9 scores （r=0.158， 0.166， P<0.01）. PHQ-9 scores were positively correlated with ASHS scores （r=0.364， P<0.01）， but negatively correlated with SSSUS scores （r=-0.290， P<0.01）. SSSUS scores were negatively correlated with ASHS scores （r=-0.247， P<0.01）. Depression partially mediated the relationship between anxiety and NSSI behavior， with an effect size of 0.544 （95% CI： 0.162~0.944）， accounting for 35.79% of the total effect. Social support moderated the relationship between depression and NSSI bahavior， with an effect value of -0.082 （95% CI： -0.135~-0.029）. ConclusionAnxiety not only directly influences NSSI bahavior among adolescents， also indirectly exacerbates it through depression， while social support mitigates the impact of depression on NSSI behavior. ［Funded by Youth Project of National Natural Science Foundation of China （number， 82401812）； Project of Health Commission of Sichuan Province （number， 24LCYJPT18）］

Objective:To explore the latent-classes of HIV high risk behaviors among college students, and the association between experiences of intimate partner violence (IPV) and HIV high risk behaviors, to provide evidence for reducing the HIV high risk behaviors among them.Methods:A cross-sectional study was conducted from October to December 2019 among university students from six higher education institutions in Zhuhai City, using a multi-stage cluster sampling method, with an estimated sample size of 1 318. The study included participants who self-reported being in a romantic relationship and having sexual experience within the past year. Data on sociodemographic characteristics, IPV experiences, and HIV high risk behaviors were collected. Latent-class analysis was performed on HIV high risk behaviors, and chi-squared tests and multivariable logistic regression were used to analyze the associations between IPV experiences and different latent classes of HIV high risk behaviors.Results:The effective response rate for the survey was 95.4% (12 235/12 821). 1 382 college students from Zhuhai were included as participants in the study, with 19.4% (268/1 382) self-reporting having experienced IPV. Latent-class analysis of HIV high risk behaviors classified the participants into three latent groups: low-risk group (78.1%, 1 079/1 382), multiple sexual partners/alcohol use before sex group (15.8%, 219/1 382), and high-risk group (6.1%, 84/1 382). Multivariable logistic regression analysis showed that students who had experienced psychological violence were more likely to be in the group that had multiple sexual partners/alcohol use before sex (a OR=2.51, 95% CI:1.48-4.27). Those who had experienced IPV (a OR=5.74, 95% CI:3.45-9.55), physical violence (a OR=9.26, 95% CI: 5.24-16.35), sexual violence (a OR=8.46, 95% CI:4.93-14.52), or psychological violence (a OR=15.99, 95% CI:8.64-29.57) were more likely to be in the high-risk group. Students who experienced two (a OR=9.37, 95% CI:3.55-24.71) or three types of IPV (a OR=50.09, 95% CI: 21.06-119.14) were more likely to be in the high-risk group compared to those with no IPV experiences. Conclusions:HIV high risk behaviors among college students in Zhuhai exhibited heterogeneity across different latent groups, and these groups have different associations with IPV experiences. Universities should tailor targeted HIV/AIDS education and prevention strategies based on the characteristics of each latent group to reduce HIV high risk behaviors among college students.

Ovarian cancer is the most lethal malignancy affecting the female reproductive system. Pharmacological inhibitors targeting CDK4/6 have demonstrated promising efficacy across various cancer types. However, their clinical benefits in ovarian cancer patients fall short of expectations, with only a subset of patients experiencing these advantageous effects. This study aims to provide further clinical and biological evidence for antineoplastic effects of a CDK4/6 inhibitor (TQB4616) in ovarian cancer and explore underlying mechanisms involved. Patient-derived ovarian cancer organoid models were established to evaluate the effectiveness of TQB3616. Potential key genes related to TQB3616 sensitivity were identified through RNA-seq analysis, and TRIM4 was selected as a candidate gene for further investigation. Subsequently, co-immunoprecipitation and GST pull-down assays confirmed that TRIM4 binds to hnRNPDL and promotes its ubiquitination through RING and B-box domains. RIP assay demonstrated that hnRNPDL binded to CDKN2C isoform 2 and suppressed its expression by alternative splicing. Finally, in vivo studies confirmed that the addition of siTRIM4 significantly improved the effectiveness of TQB3616. Overall, our findings suggest that TRIM4 modulates ubiquitin-mediated degradation of hnRNPDL and weakens sensitivity to CDK4/6 inhibitors in ovarian cancer treatment. TRIM4 may serve as a valuable biomarker for predicting sensitivity to CDK4/6 inhibitors in ovarian cancer.
Humans ; Female ; Ovarian Neoplasms/pathology* ; Animals ; Tripartite Motif Proteins/genetics* ; Mice ; Cyclin-Dependent Kinase 4/antagonists & inhibitors* ; Cell Line, Tumor ; Cyclin-Dependent Kinase 6/antagonists & inhibitors* ; Protein Kinase Inhibitors/pharmacology* ; Ubiquitin/metabolism* ; Xenograft Model Antitumor Assays ; Ubiquitination ; Antineoplastic Agents/pharmacology*

Objective To establish a classification tree model for non-alcoholic fatty liver disease(NAFLD)among aircrews,screen for influencing factors of NAFLD,so as to provide scientific basis for prevention and intervention decisions for NAFLD.Methods Aircrews who underwent recuperation at a sanatorium from January 2019 to December 2023 were selected as the research objects.Their annual physical examination data were collected and the NAFLD detection rate was calculated.Age,body mass index(BMI),blood pressure,waist circumference,blood routine,biochemistry indexes,and thyroid function were incorporated,and a NAFLD risk model was constructed using classification regression tree method.The predictive performance of the NAFLD classification tree model was evaluated through model misclassification matrix,risk statistics,and receiver operating characteristic curve.Results A total of 4088 aircrews were included in the study,and NAFLD was detected in 380 persons(380/4088,9.30%).The NAFLD model consisted of three layers,and five explanatory variables affecting the onset of NAFLD were extracted,including BMI,triglycerides(TG),high-density lipoprotein cholesterol(HDL-C),alanine aminotransferase(ALT),and total bilirubin(TBIL).BMI was located at the top of the classification tree and was the most important risk factor for NAFLD in aircrews.The area under the curve(AUC)of the model was 0.853.The predictive accuracy of NAFLD was 90.9%,indicating that the model has good accuracy and fitting effect.Conclusion In this study,the detection rate of NAFLD in aircrews was 9.30%.BMI,TG,HDL-C,ALT,and TBIL are risk factors for the onset of NAFLD.NAFLD is mainly related to weight gain and lipid metabolism disorders caused by unhealthy lifestyles.

Objective:To investigate the value of a deep learning radiomics (DLR) nomogram model based on intra-tumoral and peri-tumoral MRI features for differentiating IgG4-related ophthalmic disease (IgG4-ROD) from orbital mucosa-associated lymphoid tissue (MALT) lymphoma.Methods:This was a case-control study. The clinical and imaging data of 233 patients pathologically confirmed with either IgG4-ROD or orbital MALT lymphoma were retrospective collected between January 2020 and December 2024 from the Second Hospital of Jilin University (Center 1) and Beijing Tongren Hospital, Capital Medical University (Center 2). Patients from Center 1 ( n=158) were used as the training cohort, while those from Center 2 ( n=75) served as the validation cohort. Among the cases, 102 were IgG4-ROD (70 in training, 32 in validation) and 131 were orbital MALT lymphoma (88 in training, 43 in validation). Univariate and multivariate logistic regression analyses were used to identify independent clinical imaging predictors and build a clinical imaging model. Based on T 1WI, T 2WI, and diffusion weighted images, intra-tumoral regions were manually delineated, a 2 mm peri-tumoral margin was automatically generated, and both regions were combined as a single region of interest for radiomics feature extraction. Deep learning features were extracted using a ResNet-50 backbone, and after feature selection and dimensionality reduction, a DLR model was constructed. The clinical imaging features and DLR features were integrated to build a combined nomogram model. Model performance in differentiating IgG4-ROD from orbital MALT lymphoma was assessed using receiver operating characteristic curves, calibration curves, and decision curve analysis. The area under the curve (AUC) were compared using the DeLong test. Results:Bilateral orbital involvement ( OR=1.983, 95% CI 1.166-2.843, P=0.046) and extraocular muscle involvement ( OR=1.246, 95% CI 1.079-1.764, P=0.015) were identified as independent predictors for distinguishing IgG4-ROD from orbital MALT lymphoma and were used to construct the clinical model. Fourteen features (9 radiomics and 5 deep learning features) were selected for the DLR model, and a nomogram was developed. In the training set, the AUCs for the clinical model, DLR model, and nomogram were 0.762 (95% CI 0.712-0.812), 0.865 (95% CI 0.822-0.908), and 0.943 (95% CI 0.909-0.953), respectively. In the validation set, the AUCs were 0.733 (95% CI 0.675-0.791), 0.823 (95% CI 0.762-0.884), and 0.924 (95% CI 0.902-0.958), respectively. The nomogram showed significantly higher AUCs than those of the clinical and DLR models alone (training set: Z=3.92, 2.87, P0.001, P=0.004; validation set: Z=3.25, 2.46, P=0.001, 0.014). Calibration curves indicated good agreement between predicted and actual IgG4-ROD incidence, and decision curve analysis demonstrated the highest net benefit for the nomogram. Conclusion:A nomogram that incorporates both intra-tumoral and peri-tumoral DLR features and clinical imaging characteristics demonstrates excellent performance in distinguishing IgG4-ROD from orbital MALT lymphoma.

Objective:To evaluate cerebrospinal fluid (CSF) flow dynamics and volume changes of pulsatile tinnitus (PT) patients induced by sigmoid sinus wall dehiscence (SSWD) using MRI.Methods:This was a cross-sectional study. Totally 55 SSWD-PT patients, and 35 age- and sex-matched healthy controls were prospectively enrolled at Beijing Tongren Hospital, Capital Medical University from October 2019 to September 2023. The CSF at the midbrain aqueduct level was analyzed based on phase-contrast MRI to obtain the flow dynamics information. Based on T 1-weighted turbo field echo sequence, the CSF was segmented and the volume of CSF was calculated using ITK-SNAP software. The Mann-Whitney U test was used to compare the differences of each parameter between the two groups. Binary logistic regression was used to analyze the parameters with statistically significant differences to obtain the independent influencing factors of SSWD-PT and establish the combined parameters. Receiver operating characteristic curve analysis was used to evaluate the efficacy of diagnosing SSWD-PT. Results:Compared with controls, the SSWD-PT group showed significantly decreased mean flux (MF), mean velocity, peak velocity( P<0.05), and significantly increased regurgitant fraction (RF), CSF volume ( P<0.05). No significant differences were observed in forward flow volume, backward flow volume, and stroke volume ( P>0.05). The logistic regression results showed that MF ( OR=0.497, 95% CI 0.305-0.808, P=0.005) and RF ( OR=1.809, 95% CI 1.040-3.147, P=0.036) were independent influencing factors of SSWD-PT. The area under the curve (AUC) of MF and RF for diagnosing SSWD-PT were 0.641 (95% CI 0.517-0.766) and 0.675 (95% CI 0.564-0.786), respectively. The AUC of the combination of MF and RF was 0.724 (95% CI 0.614-0.833). Conclusions:SSWD-PT patients have abnormal changes in CSF flow dynamics and volume. The MF and RF demonstrate moderate diagnostic value for diagnosing SSWD-PT.

Objective:To explore the diagnostic value of intratumoral area(ITA)and peritumoral area(PTA)ultrasound image-based bioin-formatics models for small breast cancer.Methods:We retrospectively analyzed data of 305 breast lesions from 292 patients with small breast nodules(diameter≤2 cm)who were treated at People's Hospital of Xinjiang Uygur Autonomous Region between January 2021 and January 2025.The lesions were randomly assigned into the training(214 lesions)and validation sets(91 lesions)in a 7:3 ratio.Radiomics fea-tures were extracted from the intertumoral area(ITA)and peritumoral area(PTA)regions at 2,4,6,and 8 mm,followed by feature selection and dimensionality reduction.A Logistic regression(LR)algorithm was used to construct a model.The performance of the models were eval-uated via receiver operating characteristic(ROC)curve analysis,Hosmer-Lemeshow test,and decision curve analysis(DCA).Results:In the training set,the areas under the ROC curves(AUC)for the ITA,2 mm PTA,and 2 mm fusion models were 0.869,0.897,and 0.909,respect-ively.In the test set,these respective AUC values were 0.813,0.825,and 0.840.For breast lesions≤2 cm,<1 cm,and 1-2 cm,the overall ac-curacies of the 2 mm fusion model were 81.0%,82.7%,and 80.1%,respectively,whereas the respective overall accuracies of BI-RADS were 76.4%,81.7%,and 73.6%.Conclusions:ITA and PTA ultrasound imaging-based radiomics models had a high diagnostic value for small breast cancers.The fusion model can effectively improve predictive performance,outperforming the BI-RADS classification in diagnosing small breast lesions of different diameters.Thus,these models have the potential to serve as an auxiliary diagnostic tool in clinical practice.