BACKGROUND:Rapid and accurate segmentation of brain tissue in medical images is of great significance for three-dimensional biomechanical modeling and diagnosis of craniocerebral injuries.Currently,artificial intelligence(AI)-based baseline models exhibit excellent generalization capabilities on large-scale datasets.However,due to the specificity and complexity of craniocerebral tissues,these models have certain limitations in their application to craniocerebral tissue segmentation.Additionally,the scarcity of craniocerebral tissue samples makes it difficult for baseline models to achieve precise segmentation results through fine-tuning.OBJECTIVE:To construct a craniocerebral tissue segmentation model based on texture feature retrieval enhancement to improve segmentation accuracy under a small number of samples.METHODS:Segment Anything in Medical Images(MedSAM)model was selected as the basic framework,and texture features were combined with deep learning to build a brain tissue segmentation model based on texture feature retrieval enhancement(DP-MedSAM).Dice Coefficient and mean intersection over union(MIoU)were selected to evaluate the efficiency of image segmentation results.In comparison with the original MedSAM model,the ablation experiment systematically evaluated the influence of key components on the model performance.The sensitivities of MedSAM,the Segment Anything Model(SAM)for medical image segmentation(SAM-Med2D)and DP-MedSAM in the mandible,left optic nerve,and left parotid gland were compared.RESULTS AND CONCLUSION:(1)By verifying the impact of the number of point prompts on segmentation results on the HaN-Seg dataset,the experimental results indicated that the optimal Dice score was achieved with the addition of three points.(2)DP-MedSAM demonstrated performance improvements compared with MedSAM and SAM-Med2D on two datasets(HaN and Public Domain Database for Computational Anatomy).Especially on the Public Domain Database for Computational Anatomy dataset,in terms of the MIoU metric,DP-MedSAM outperformed MedSAM by 6.59％and SAM-Med2D by 37.35％;in terms of the Dice metric,DP-MedSAM outperformed MedSAM and SAM-Med2D by 4.34％and 25.32％,respectively.(3)The ablation experiment results showed that removing the texture feature extraction module in the DP-MedSAM model,relying solely on original image features,led to a significant decrease in results on the test set.Furthermore,removing the vector cache database and its retrieval enhancement function from the model,which deprived the ability of the model to perform similarity retrieval using an external knowledge base,further reduced model performance.(4)Under conditions of limited data resources,the DP-MedSAM model outperformed the other two models in all evaluation metrics.The DP-MedSAM model performed excellently when processing simple and moderately difficult samples,demonstrating a clear advantage over the other two models and indicating good generalization ability.Processing the fine structures of difficult samples placed higher demands on the model's segmentation capabilities.Although the performance of the DP-MedSAM model declined slightly,it still outperformed the other two models.(5)This study proposes an innovative craniocerebral tissue segmentation model,DP-MedSAM,which improves the baseline model's performance in capturing local details and global structural information in medical images by introducing target region texture feature extraction.Through vector similarity retrieval technology,DP-MedSAM can retrieve the feature vector most similar to the current target region from a pre-constructed vector database,providing more precise guiding information for the segmentation process.

Objective To systematically analyze the disease burden, long-term trends, and age-sex distribution of major valvular heart disease (VHD) subtypes—rheumatic heart disease (RHD), non-rheumatic valvular disease (NRVD), and non-rheumatic calcific aortic valve disease (CAVD)—in global, Chinese, and US populations from 1990 to 2021, providing evidence for public health strategies and clinical resource allocation. Methods Based on publicly available data from the Global Burden of Disease (GBD) Study 2021, we extracted incidence, mortality, and disability-adjusted life years (DALYs) for VHD from 1990 to 2021. Age-standardized rates (ASRs) were calculated using the GBD 2021 global standard population, and the estimated annual percentage change (EAPC) with its 95% uncertainty interval (UI) was computed for the period. Data from the Agency for Healthcare Research and Quality (AHRQ), the European Society of Cardiology (ESC)/Eurostat surveys, and Chinese national registries were used for trend triangulation and contextual background. Results From 1990 to 2021, the ASR and disease burden of RHD significantly decreased globally and in China (EAPC for DALYs in China: −4.8%, 95%UI: −5.0% to −4.6%). In contrast, the burden of NRVD and CAVD steadily increased in aging populations like those in China and the US, with a higher burden observed in older adults and males. In 2021, the incidence of NRVD and CAVD peaked in individuals aged ≥65 years, with rates being significantly higher in men than in women. RHD burden was concentrated in low socio-demographic index (SDI) regions, whereas NRVD/CAVD burden was strongly associated with high-SDI regions. Conclusion The global VHD epidemiological landscape is transitioning from an RHD-dominant to an NRVD/CAVD-dominant pattern. China faces a dual challenge of a residual RHD burden and a rapidly growing burden of degenerative valvular diseases. Developing tailored screening, prevention, and treatment strategies for different disease subtypes and populations is crucial.

Objective: To explore the relationship between social jetlag and depressive symptoms in junior high school students, as well as the potential gender differences, so as to provide a reference for developing effective interventions for depressive symptoms and promoting adolescents mental health. Methods: In October 2024, a total of 3 516 students from grades 7 to 9 were recruited from 4 junior high schools in Chongqing Municipality using a combination of cluster sampling and convenience sampling. A questionnaire survey was conducted using the Center for Epidemiologic Studies Depression Scale (CES-D) and the Munich Chronotype Questionnaire (MCTQ). Statistical analyses included the χ 2 test, binary Logistic regression analysis, and stratified Logistic regression analysis. Results: The detection rate of depressive symptoms among the junior high school students was 34.3%. The number of students with social jetlag >2 h was 714 (20.3%), >1-2 h was 1 455(41.4%), and ≤1 h was 1 347(38.3%). Results from the binary Logistic regression analysis showed that compared to the group with social jetlag ≤1 h, the risk of depressive symptoms in the group with social jetlag >2 h was higher ( OR=1.59, 95%CI=1.28-1.98, P <0.01). Gender stratified analysis revealed that among females, the risk of depressive symptoms was higher in the groups with social jetlag of >1-2 h and >2 h compared to the ≤1 h group ( OR = 1.34 and 2.05, 95% CI =1.03-1.75 and 1.48-2.83, both P <0.05). However, among males, the associations were not statistically significant ( OR =1.11 and 1.29, 95% CI =0.86-1.43 and 0.95-1.77, both P >0.05). Conclusions Social jetlag is positively associated with depressive symptoms in junior high school students, demonstrating a threshold effect and gender differences. The findings suggest that reducing social jetlag may decrease the risk of depressive symptoms in adolescents, and targeted intervention measures should be developed considering different gender characteristics.

BACKGROUND:Rapid and accurate segmentation of brain tissue in medical images is of great significance for three-dimensional biomechanical modeling and diagnosis of craniocerebral injuries.Currently,artificial intelligence(AI)-based baseline models exhibit excellent generalization capabilities on large-scale datasets.However,due to the specificity and complexity of craniocerebral tissues,these models have certain limitations in their application to craniocerebral tissue segmentation.Additionally,the scarcity of craniocerebral tissue samples makes it difficult for baseline models to achieve precise segmentation results through fine-tuning.OBJECTIVE:To construct a craniocerebral tissue segmentation model based on texture feature retrieval enhancement to improve segmentation accuracy under a small number of samples.METHODS:Segment Anything in Medical Images(MedSAM)model was selected as the basic framework,and texture features were combined with deep learning to build a brain tissue segmentation model based on texture feature retrieval enhancement(DP-MedSAM).Dice Coefficient and mean intersection over union(MIoU)were selected to evaluate the efficiency of image segmentation results.In comparison with the original MedSAM model,the ablation experiment systematically evaluated the influence of key components on the model performance.The sensitivities of MedSAM,the Segment Anything Model(SAM)for medical image segmentation(SAM-Med2D)and DP-MedSAM in the mandible,left optic nerve,and left parotid gland were compared.RESULTS AND CONCLUSION:(1)By verifying the impact of the number of point prompts on segmentation results on the HaN-Seg dataset,the experimental results indicated that the optimal Dice score was achieved with the addition of three points.(2)DP-MedSAM demonstrated performance improvements compared with MedSAM and SAM-Med2D on two datasets(HaN and Public Domain Database for Computational Anatomy).Especially on the Public Domain Database for Computational Anatomy dataset,in terms of the MIoU metric,DP-MedSAM outperformed MedSAM by 6.59％and SAM-Med2D by 37.35％;in terms of the Dice metric,DP-MedSAM outperformed MedSAM and SAM-Med2D by 4.34％and 25.32％,respectively.(3)The ablation experiment results showed that removing the texture feature extraction module in the DP-MedSAM model,relying solely on original image features,led to a significant decrease in results on the test set.Furthermore,removing the vector cache database and its retrieval enhancement function from the model,which deprived the ability of the model to perform similarity retrieval using an external knowledge base,further reduced model performance.(4)Under conditions of limited data resources,the DP-MedSAM model outperformed the other two models in all evaluation metrics.The DP-MedSAM model performed excellently when processing simple and moderately difficult samples,demonstrating a clear advantage over the other two models and indicating good generalization ability.Processing the fine structures of difficult samples placed higher demands on the model's segmentation capabilities.Although the performance of the DP-MedSAM model declined slightly,it still outperformed the other two models.(5)This study proposes an innovative craniocerebral tissue segmentation model,DP-MedSAM,which improves the baseline model's performance in capturing local details and global structural information in medical images by introducing target region texture feature extraction.Through vector similarity retrieval technology,DP-MedSAM can retrieve the feature vector most similar to the current target region from a pre-constructed vector database,providing more precise guiding information for the segmentation process.

Objective To analyze the global pattern, long-term trends, and regional and population heterogeneity in the burden of 375 diseases and injuries and 88 risk factors from 1990 to 2023 using data from the Global Burden of Disease Study 2023 (GBD 2023). Methods Publicly available GBD 2023 estimates were extracted for incidence, deaths, years of life lost (YLLs), years lived with disability (YLDs), disability-adjusted life years (DALYs), and age-standardized rates at the global level and by socio-demographic index (SDI), GBD region, age group, and sex. Descriptive analysis, ranking comparison, stratified analysis, and trend analysis were performed, and 95% uncertainty interval was used to assess the stability of estimates. Results In 2023, non-communicable diseases were the leading contributor to global DALYs, accounting for 64.39% of DALYs across the three major disease and injury groups (communicable, maternal, neonatal, and nutritional diseases, non-communicable diseases, and injuries), and also had the highest age-standardized DALYs rate. From 1990 to 2023, age-standardized DALYs rates declined for three major disease and injury groups, with estimated annual percentage changes of −2.69%, −0.78%, and −1.47%, respectively. Cardiovascular diseases ranked first by both DALYs and deaths in 2023, whereas respiratory infections and tuberculosis ranked first by incidence. Non-communicable diseases were the leading cause of disease burden across all SDI strata. As the SDI level declined, the burden of communicable, maternal, neonatal, and nutritional diseases, as well as injuries, increased markedly. Behavioral and metabolic risks remained the main contributors to attributable deaths and DALYs. Conclusion From 1990 to 2023, the global burden of disease continues to shift toward non-communicable diseases; however, some regions still face a double burden of infectious, maternal and child, injury-related, and chronic diseases. Integrating deaths, DALYs, YLLs, YLDs, and risk-attributable burden helps identify priority areas for prevention and resource allocation across regions and populations.

Objective To propose a framework for risk assessment and stepwise management of pulmonary nodules based on existing guidelines and key studies, and to clarify the boundaries for triage, workup escalation, and referral in different detection scenarios. Methods A problem-oriented evidence synthesis approach was used, prioritizing Chinese expert consensus, Fleischner Society Guidelines, British Thoracic Society (BTS) Guidelines, and Lung-RADS. This was supplemented by studies on risk models, imaging measurements, liquid biopsy, tissue sampling, and surgical treatments. This paper is not presented as a formal consensus or a validated clinical pathway. Results A three-tiered framework consisting of basic non-invasive assessment, advanced assessment, and invasive diagnosis was established. It specified the applicable populations, boundaries for the use of risk models, imaging review, management of conflicting results, evaluation of histopathological consistency, and post-intervention follow-up. Liquid biopsy, genetic testing, and artificial intelligence were defined as investigational or adjunctive tools. Conclusion This framework can serve as a preliminary reference for multidisciplinary teams in developing local protocols. It cannot replace applicable guidelines, pathological diagnosis, shared decision-making with patients, or real-world validation.

Pituitary metastasis(PM), a rare metastatic complication of malignant tumors most commonly seen in lung and breast cancers, exhibits subtle and nonspecific clinical manifestations that complicate its differentiation from other sellar lesions. Symptom development correlates with anatomical involvement: tumors often initially destroy the posterior pituitary, causing diabetes insipidus; subsequently, pituitary stalk compression may trigger hyperprolactinemia, while progressive anterior pituitary destruction ultimately leads to hormonal deficiencies. Additionally, mass effects from the tumor can result in optic chiasm compression, with specific neurological manifestations dependent on the invasion site. Nonspecific symptoms such as fatigue and headache are frequently observed. Pituitary MRI serves as a critical diagnostic tool for distinguishing sellar pathologies. As a distant metastatic event, PM portends a poor overall prognosis. This study presents two cases of lung adenocarcinoma with PM, supplemented by a comprehensive literature review, to summarize clinical characteristics and diagnostic/therapeutic strategies.

Objective:To explore the symptom management experience and needs of tuberculosis patients undergoing home-based anti-tuberculosis chemotherapy.Methods:This was a descriptive qualitative study. Purposeful sampling was used to select tuberculosis patients receiving home-based chemotherapy and healthcare professionals with tuberculosis experience from Beijing Chest Hospital, Capital Medical University between March and July 2024. Semi-structured interviews focused on the management experience and needs related to chemotherapy symptoms. Data were analyzed using thematic analysis.Results:A total of 13 tuberculosis patients and six healthcare professionals were interviewed. A total of four core themes were identified: multiple concurrent symptoms exacerbate the difficulty of home disease management and cause multidimensional distress; tuberculosis patients have insufficient self-management skills for symptoms; there is a high and diverse demand for symptom management during home chemotherapy; and the out-of-hospital follow-up and monitoring system struggles to address symptom management effectively.Conclusions:Tuberculosis patients undergoing home chemotherapy face significant and challenging symptom management burdens. Future improvements should include enhancing continuity of care outside the hospital, developing specific symptom assessment tools, and establishing an efficient, multi-symptom integrated management strategy combining home and hospital care to improve symptom management experience and outcomes for tuberculosis patients.

Objective: To explore the independent effects and gender differences of different types of adverse childhood experiences (ACEs) on the co-occurrence of non-suicidal self-injury (NSSI) and suicide attempts (SA), so as to provide a reference for the precise prevention and control of self-harm in junior high school students. Methods: From May to June 2023, a total of 7 360 junior high school students were selected from 12 schools in three districts/counties of Chongqing using a combination of stratified cluster sampling and convenience sampling methods. Information on NSSI, SA, ACEs, and depressive symptom, as well as other related data were collected through the Adolescent Non-suicidal Self-injury Assessment Questionnaire (ANSAQ), suicide related section of the Chinese Adolescent Health related Behavior Questionnaire (Junior High School Version), Childhood Trauma Questionnaire-Short Form ( CTQ- SF), and Center for Epidemiologic Studies-Depression Scale (CES-D). Statistical analyses of the data were performed using the Chi-square test and multiple Logistic regression. Results: The detection rates of NSSI, SA, NSSI+SA and ACEs in junior high school students were 19.2%, 4.6%, 3.5% and 57.9% respectively. After controlling for factors such as gender, grade, family type, self rated family economic status, self rated academic performance, self rated academic pressure, number of close friends, and depressive symptom scores, results from the multiple Logistic regression analysis showed that junior high school students with physical abuse ( OR = 1.98, 95% CI =1.23-3.18), emotional abuse ( OR =2.83, 95% CI =1.92-4.19), sexual abuse ( OR = 1.70, 95% CI =1.07- 2.69 ), physical neglect ( OR =1.67, 95% CI =1.20-2.33) and witnessing domestic violence ( OR =2.10, 95% CI =1.41-2.87) in childhood had higher risks for the occurrence of NSSI+SA (all P <0.05). After stratification by gender, boys with sexual abuse in childhood had a high risk for the occurrence of NSSI+SA ( OR =2.17, 95% CI =1.06-4.43), whereas girls with emotional abuse ( OR =3.69, 95% CI =2.29-5.94), physical neglect ( OR =1.62, 95% CI =1.07-2.45) and witnessing domestic violence ( OR =2.17, 95% CI =1.41-3.34) in childhood had hgih risks for the occurrence of NSSI+SA (all P <0.05). Conclusions Different types of ACEs have different effects on the co-occurrence of self-harm in junior high school students and there are gender differences. When family interventions are conducted for the combined model, emphasis should be placed on aspects of emotional abuse and domestic violence while optimizing the interventions based on gender differences.

Objective: To explore the association between various components of sleep quality and the co-occurrence of non-suicidal self-injury (NSSI) behaviors and depressive symptoms among junior high school students, so as to provide evidence for targeted prevention strategies of NSSI and depression. Methods: From May to June 2024, a total of 5 008 junior high school students from 8 schools in 2 districts/counties of Chongqing were selected through a stratified cluster sampling method. The Pittsburgh Sleep Quality Index (PSQI), the Center for Epidemiological Studies Depression Scale (CES-D), and the Adolescent Non suicidal Self injury Assessment Questionnaire (ANSAQ) were used to assess sleep quality, depressive symptoms, and NSSI, respectively. Data were analyzed by using the Chi-square test, Bonferroni correction, and multivariate Logistic regression. Results: Non-NSSI group and depressive symptoms group accounted for 68.11% among junior high school students, NSSI-only group accounted for 4.71%, only depressive symptoms group accounted for 14.94%, and co-occurrence of NSSI and depressive symptoms group accounted for 12.24%. The prevalence of the co-occurrence group was higher in girls (16.39%) than in boys (7.85%) ( χ 2=84.89, P <0.01). After controlling for gender, grade, and boarding status etc., multivariate Logistic regression analysis revealed that five sleep components, including subjective sleep quality, sleep latency, sleep duration, sleep disturbances, and daytime dysfunction, were significantly and positively associated with the co-occurrence of NSSI and depressive symptoms ( OR =1.30-3.86, all P <0.05). The strength of association between these components and the co-occurrence group, in descending order, was: daytime dysfunction ( OR = 2.52), sleep disturbances ( OR =2.36), subjective sleep quality ( OR =1.76), sleep latency ( OR =1.44), and sleep duration ( OR =1.22) (all P <0.01). Conclusions The co-occurrence of NSSI and depressive symptoms is prevalent among junior high school students, with girls being more significantly affected. Sleep disturbances and daytime dysfunction may represent particularly important risk factors. Targeted and prioritized intervention strategies addressing specific sleep components should be developed and implemented to reduce the co-occurrence of NSSI and depressive symptoms in junior high school students.