Objective: To investigate the status and associated factors of sports supplement usage among grade 9 students, so as to provide a scientific basis for targeted supervision and health education regarding sports supplement usage among junior high school students. Methods: From June to September 2025, a stratified cluster random sampling method was used to select 2 261 grade 9 students from 10 provinces (autonomous regions, municipalities) in China. A questionnaire survey was conducted on their sports supplement usage and related factors. The Chi square test was used to compare the usage rates of sports supplements among different groups of students, and binary Logistic regression analysis was employed to investigate the related factors of sports supplement usage among grade 9 students. Results: Totally 59.7% of the grade 9 students used sports supplements. The usage rate (62.5%) was higher among boys than girls (56.3%), higher among students from rural areas and towns/counties (66.5%, 66.2%) than those from urban districts (52.9%), higher among boarding students (65.2%) than non resident students (54.3%), higher among students whose parents occupations were businessmen and workers (fathers: 65.0%, 63.7%; mothers: 63.6%, 61.1%) than those whose parents were farmers and civil servants (fathers: 57.5%, 54.1%; mothers: 58.8%, 55.7%), higher among students with a monthly family income of 5 000- 10 000 yuan (66.3%) than those in other income groups, and higher among students in the high score zone for the entrance physical examination to senior high school (67.7%) than those in the medium and low score zones ( 56.3% , 56.5%) ( χ 2=8.99, 42.21, 27.98, 20.55, 8.20, 22.74, 24.70, respectively, all P <0.05). Binary Logistic regression analysis showed that boys ( OR =1.26), those from rural areas ( OR =1.59), boarding students ( OR =1.36), those with a monthly family income of 5 000- 10 000 yuan ( OR =1.41), and those in the high score zone for entrance physical examination to senior high school ( OR =1.34) were more likely to use sports supplements during the entrance physical examination to senior high school; the probability of sports supplement usage was lower among students whose fathers were civil servants ( OR =0.74) (all P <0.05). Conclusions The usage of sports supplements is relatively common among grade 9 students. Intervention measures should be targeted at specific populations to reduce the risk of misuse.

Renal Fanconi syndrome (FS) is a rare renal manifestation of primary Sjögren's syndrome (pSS). This study aims to analyze the clinical and prognostic characteristics of patients with pSS-associated renal FS (pSS-FS) and provide insights for clinical management.

ObjectiveTo investigate the associations of multiple glycolipid metabolic indicators and their cumulative abnormality burden with incident colorectal cancer risk in a general population-based prospective cohort, and to examine the mediating role of glycolipid metabolic abnormalities in the relationships between smoking, alcohol consumption, physical activity, and colorectal cancer incidence. MethodsA total of 17 897 eligible participants recruited from the Taizhou Cohort between 2011 and 2014 were included. Cox proportional hazards regression models were used to assess the associations of conventional and derived glycolipid indicators, as well as a glycolipid abnormality index constructed from total cholesterol, triglycerides, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, fasting plasma glucose, and insulin, with incident colorectal cancer risk. Restricted cubic spline models were applied to evaluate dose-response relationships for major indicators. Receiver operating characteristic curves were generated to compare predictive performance across indicators. Mediation analyses were conducted to assess the mediating effects of the glycolipid abnormality index on the associations of smoking, alcohol consumption, and physical activity with colorectal cancer incidence. ResultsDuring a median follow-up of 11.2 years, 102 incident colorectal cancer cases were identified, with an incidence density of 51.2 per 100 000 person-years. After adjustment for potential confounders, Cox proportional hazards regression analyses showed that decreased high-density lipoprotein cholesterol was associated with a 1.74-fold higher risk of colorectal cancer (HR=1.74, 95%CI: 1.03‒2.95), elevated low-density lipoprotein cholesterol was associated with a 1.78-fold higher risk (HR=1.78, 95%CI: 1.05‒3.02), and abnormal fasting plasma glucose was associated with a 1.86-fold higher risk (HR=1.86, 95%CI: 1.15‒3.02). Triglycerides and fasting plasma glucose showed an approximately linear increasing association with colorectal cancer risk in multivariable restricted cubic spline models. The glycolipid abnormality index showed a clear gradient association with colorectal cancer risk; participants with three or more abnormal indicators had a 3.08-fold higher risk than those without abnormalities (HR=3.08, 95%CI: 1.60‒5.92). The area under the curve was 0.795, higher than that of individual glycolipid indicators and other combined indices, and gender-stratified analyses showed generally consistent patterns. Glycolipid metabolic abnormalities partially mediated the associations of smoking, alcohol consumption, and physical activity with colorectal cancer incidence, with mediation proportions of 8.33%, 9.45%, and 9.04%, respectively. ConclusionGlycolipid metabolic abnormalities are associated with an increased risk of incident colorectal cancer. The glycolipid abnormality index shows an increasing relationship with colorectal cancer risk and demonstrates better discrimination, and it partially mediates the associations of smoking, alcohol consumption, and physical activity with colorectal cancer incidence in the overall population.

Background: Gestational diabetes mellitus (GDM) is a metabolic disorder posing significant risks to maternal and infant health, with a lack of effective early screening markers. Therefore, identifying early screening biomarkers for GDM with higher sensitivity and specificity is urgently needed. Methods: High-throughput sequencing was employed to screen for key circular RNAs (circRNAs), which were then evaluated using reverse transcription quantitative polymerase chain reaction. Logistic regression analysis was conducted to examine the relationship between clinical characteristics, circRNA expression, and adverse pregnancy outcomes. The diagnostic accuracy of circRNAs for early and mid-pregnancy GDM was assessed using receiver operating characteristic curves. Pearson correlation analysis was utilized to explore the relationship between circRNA levels and oral glucose tolerance test results. A predictive model for early GDM was established using logistic regression. Results: Significant alterations in circRNA expression profiles were detected in GDM patients, with hsa_circ_0031560 and hsa_ circ_0000793 notably upregulated during the first and second trimesters. These circRNAs were associated with adverse pregnancy outcomes and effectively differentiated GDM patients, with second trimester cohorts achieving an area under the curve (AUC) of 0.836. In first trimester cohorts, these circRNAs identified potential GDM patients with AUCs of 0.832 and 0.765, respectively. The early GDM prediction model achieved an AUC of 0.904, validated in two independent cohorts. Conclusion Hsa_circ_0031560, hsa_circ_0000793, and the developed model serve as biomarkers for early prediction or midterm diagnosis of GDM, offering clinical tools for early GDM screening.

Background: Gestational diabetes mellitus (GDM) is a metabolic disorder posing significant risks to maternal and infant health, with a lack of effective early screening markers. Therefore, identifying early screening biomarkers for GDM with higher sensitivity and specificity is urgently needed. Methods: High-throughput sequencing was employed to screen for key circular RNAs (circRNAs), which were then evaluated using reverse transcription quantitative polymerase chain reaction. Logistic regression analysis was conducted to examine the relationship between clinical characteristics, circRNA expression, and adverse pregnancy outcomes. The diagnostic accuracy of circRNAs for early and mid-pregnancy GDM was assessed using receiver operating characteristic curves. Pearson correlation analysis was utilized to explore the relationship between circRNA levels and oral glucose tolerance test results. A predictive model for early GDM was established using logistic regression. Results: Significant alterations in circRNA expression profiles were detected in GDM patients, with hsa_circ_0031560 and hsa_ circ_0000793 notably upregulated during the first and second trimesters. These circRNAs were associated with adverse pregnancy outcomes and effectively differentiated GDM patients, with second trimester cohorts achieving an area under the curve (AUC) of 0.836. In first trimester cohorts, these circRNAs identified potential GDM patients with AUCs of 0.832 and 0.765, respectively. The early GDM prediction model achieved an AUC of 0.904, validated in two independent cohorts. Conclusion Hsa_circ_0031560, hsa_circ_0000793, and the developed model serve as biomarkers for early prediction or midterm diagnosis of GDM, offering clinical tools for early GDM screening.

Background: Gestational diabetes mellitus (GDM) is a metabolic disorder posing significant risks to maternal and infant health, with a lack of effective early screening markers. Therefore, identifying early screening biomarkers for GDM with higher sensitivity and specificity is urgently needed. Methods: High-throughput sequencing was employed to screen for key circular RNAs (circRNAs), which were then evaluated using reverse transcription quantitative polymerase chain reaction. Logistic regression analysis was conducted to examine the relationship between clinical characteristics, circRNA expression, and adverse pregnancy outcomes. The diagnostic accuracy of circRNAs for early and mid-pregnancy GDM was assessed using receiver operating characteristic curves. Pearson correlation analysis was utilized to explore the relationship between circRNA levels and oral glucose tolerance test results. A predictive model for early GDM was established using logistic regression. Results: Significant alterations in circRNA expression profiles were detected in GDM patients, with hsa_circ_0031560 and hsa_ circ_0000793 notably upregulated during the first and second trimesters. These circRNAs were associated with adverse pregnancy outcomes and effectively differentiated GDM patients, with second trimester cohorts achieving an area under the curve (AUC) of 0.836. In first trimester cohorts, these circRNAs identified potential GDM patients with AUCs of 0.832 and 0.765, respectively. The early GDM prediction model achieved an AUC of 0.904, validated in two independent cohorts. Conclusion Hsa_circ_0031560, hsa_circ_0000793, and the developed model serve as biomarkers for early prediction or midterm diagnosis of GDM, offering clinical tools for early GDM screening.

Background: Gestational diabetes mellitus (GDM) is a metabolic disorder posing significant risks to maternal and infant health, with a lack of effective early screening markers. Therefore, identifying early screening biomarkers for GDM with higher sensitivity and specificity is urgently needed. Methods: High-throughput sequencing was employed to screen for key circular RNAs (circRNAs), which were then evaluated using reverse transcription quantitative polymerase chain reaction. Logistic regression analysis was conducted to examine the relationship between clinical characteristics, circRNA expression, and adverse pregnancy outcomes. The diagnostic accuracy of circRNAs for early and mid-pregnancy GDM was assessed using receiver operating characteristic curves. Pearson correlation analysis was utilized to explore the relationship between circRNA levels and oral glucose tolerance test results. A predictive model for early GDM was established using logistic regression. Results: Significant alterations in circRNA expression profiles were detected in GDM patients, with hsa_circ_0031560 and hsa_ circ_0000793 notably upregulated during the first and second trimesters. These circRNAs were associated with adverse pregnancy outcomes and effectively differentiated GDM patients, with second trimester cohorts achieving an area under the curve (AUC) of 0.836. In first trimester cohorts, these circRNAs identified potential GDM patients with AUCs of 0.832 and 0.765, respectively. The early GDM prediction model achieved an AUC of 0.904, validated in two independent cohorts. Conclusion Hsa_circ_0031560, hsa_circ_0000793, and the developed model serve as biomarkers for early prediction or midterm diagnosis of GDM, offering clinical tools for early GDM screening.

Hearing loss is the most prevalent disabling disease. Cochlear implantation（CI） serves as the primary intervention for severe to profound hearing loss. This consensus systematically explores the value of genetic diagnosis in the pre-operative assessment and efficacy prognosis for CI. Drawing upon domestic and international research and clinical experience, it proposes an evidence-based medicine three-tiered prognostic classification system（Favorable, Marginal, Poor）. The consensus focuses on common hereditary non-syndromic hearing loss（such as that caused by mutations in genes like GJB2, SLC26A4, OTOF, LOXHD1） and syndromic hereditary hearing loss（such as Jervell & Lange-Nielsen syndrome and Waardenburg syndrome）, which are closely associated with congenital hearing loss, analyzing the impact of their pathological mechanisms on CI outcomes. The consensus provides recommendations based on multiple round of expert discussion and voting. It emphasizes that genetic diagnosis can optimize patient selection, predict prognosis, guide post-operative rehabilitation, offer stratified management strategies for patients with different genotypes, and advance the application of precision medicine in the field of CI.
Humans ; Cochlear Implantation ; Prognosis ; Hearing Loss/surgery* ; Consensus ; Connexin 26 ; Mutation ; Sulfate Transporters ; Connexins/genetics*

Objective:To analyze the risk factors for in-hospital malignant ventricular arrhythmia (MVA) in acute myocardial infarction (AMI) and to construct and validate a risk prediction model.Methods:This study was a retrospective cohort study. Patients aged≥18 years who were admitted to Qilu Hospital of Shandong University with a diagnosis of AMI and underwent coronary angiography (CAG) from May 2016 to March 2023 were selected, and the patients' clinical routine test indicators and CAG results were collected. Univariate and bidirectional stepwise logistic regression were used to screen out the risk factors for constructing the best prediction model. The prediction model was constructed by combining the results of multivariate logistic regression. The Hosmer-Lemeshow test and ROC curve, calibration curve, and decision curve were drawn to evaluate the model. The nomogram was drawn to visualize the model, and the Bootstrap self-sampling method was used for internal validation. The ROC curve was drawn to evaluate the predictive performance of each risk factor and prediction model. Finally, a multicollinearity test was performed.Results:Among the 4 205 patients finally included in the study, 115 patients (2.735%) developed MVA during hospitalization. The predictive factors screened out included age (X1), diastolic blood pressure (X2), respiratory rate (X3), blood glucose (X4), serum potassium (X5), logarithmic NT-proBNP (X6), myocardial infarction type (NSTEMI=X7, unclassified=X8), J wave (X9), Killip grade (Ⅱ=X10, Ⅲ=X11, Ⅳ=X12), and the regression equation was ln(p/1-p)=-4.699+0.029×X1-0.012×X2+0.059×X3+0.148×X4-1.175×X5+0.866×X6-1.427×X7-0.475×X8+0.758×X9+0.294×X10+0.902×X11+1.815×X12. The area under the ROC curve (AUC) of the model was 0.855 (95% CI: 0.816-0.894), and the Hosmer-Lemeshow test ( χ2=14.178, P=0.077) and the calibration curve showed that the predicted probability was consistent with the actual probability. The probability threshold of 0% to 65% had a better clinical net benefit. The area under the internal validation ROC curve (AUC) was 0.855, 95% CI: 0.813-0.891. The prediction performance of the nine variables was stronger than that of any single variable. There was no multicollinearity between the variables. Conclusions:Age, diastolic blood pressure, respiratory rate, blood glucose, serum potassium, NT-proBNP, type of AMI, J wave, and Killip class are forecasting indicator for in-hospital MVA in AMI. The risk prediction model based on the above factors has good predictive performance.

Objective:To investigate the value of coronary CTA (CCTA)-based traditional features and radiomics of plaque in the identification of culprit lesions that caused acute myocardial infarction (AMI).Methods:This was a retrospective multicenter study. From July 2016 to November 2023, a total of 344 patients from the Affiliated Hospital of Qingdao University (training cohort, n=184), Shandong Provincial Hospital Affiliated to Shandong First Medical University (validation cohort, n=88) and Qilu Hospital of Shandong University (test cohort, n=72) who received percutaneous coronary intervention (PCI) due to AMI and underwent CCTA within 48 hours of AMI were enrolled. The culprit plaques and non-culprit plaques were identified using a combination of electrocardiogram, CCTA, and angiographic findings. The vessel, plaque location, plaque type, Coronary Artery Disease-Reporting and Data System (CAD-RADS) score, high-risk plaque characteristics, plaque length, plaque volume, and burden were analyzed, and 1 904 radiomics features were extracted for each plaque. The traditional imaging model, the radiomics model, and the combined model were established by using multivariate Logistic regression analysis. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of each model in identifying culprit lesions. The DeLong test was used for the comparison of AUC between every two models. The net reclassification index (NRI) was used to evaluate the incremental value of the combined model to the traditional imaging model and the radiomics model. The decision curve analysis (DCA) was used to assess the clinical net benefit of these models. A correlation heatmap was used to evaluate the correlation between the radiomics score and traditional CCTA factors. The interpretable analysis of the decision process of the combined model was performed by the Shapley Additive exPlanations (SHAP). Results:In the validation cohort and the test cohort, the AUC of the traditional imaging model developed by the vessel, plaque type, positive remodeling and CAD-RADS score was 0.898 (95% CI 0.869-0.922) and 0.881 (95% CI 0.848-0.910), respectively. The radiomics model developed by six radiomics features was 0.863 (95% CI 0.831-0.891) and 0.863 (95% CI 0.827-0.864), respectively. The AUC of the combined model was 0.930 (95% CI 0.905-0.950)and 0.919 (95% CI 0.889-0.942), respectively. In the validation cohort and the test cohort, the AUC of the combined model was higher than that of the traditional imaging model ( Z=4.013, 4.272, P<0.001) and that of the radiomics model ( Z=4.819, 3.784, P<0.001), respectively. In the validation cohort, the combined model yielded an NRI of 20.43% (95% CI 10.43%-30.44%, P<0.001) and 20.21% (95% CI 9.62%-30.80%, P<0.001) for identifying culprit lesions compared with the traditional imaging model and the radiomics model, respectively. In the test cohort, the combined model yielded an NRI of 28.05% (95% CI 16.72%-39.38%, P<0.001) and 23.57% (95% CI 13.58%-33.56%, P<0.001) for identifying culprit lesions compared with the traditional imaging model and the radiomics model, respectively. DCA showed the combined model had the highest clinical net benefit. The correlation heatmap showed the radiomics score was not correlated or only weakly correlated with traditional CCTA factors. SHAP indicated the radiomics and CAD-RADS score contributed significantly to the model. Conclusion:The CCTA-based traditional features and radiomics of plaque have favorable performance for the identification of culprit plaques in patients with AMI.