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.

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.

Salvia miltiorrhiza (S. miltiorrhiza) represents a crucial component of traditional Chinese medicine, demonstrating effects on blood circulation activation and stasis removal, and has been widely utilized in asthma treatment. This study isolated a novel phenolic acid (S1) from S. miltiorrhiza and investigated its anti-asthmatic activity and underlying mechanisms for the first time. An allergic asthma (AA) model was established using ovalbumin (OVA). The mechanism of S1's effects on AA was investigated using multi-factor joint analysis, flow cytometry, and co-culture systems to facilitate clinical asthma treatment. S1 (10 or 20 mg·kg^-1) was administered daily to mice with OVA-induced AA (OVA-AA) during days 21-25. The study examined airway responsiveness, lung damage, inflammation, and levels of immunoglobulin E (IgE), PGD2, interleukins (IL-4, 5, 10, 13, 17A), tumor necrosis factor α (TNF-α), GM-CSF, CXCL1, CCL11, and mMCP-1. Additionally, mast cell (MC) activation and degranulation were explored, along with T helper type 17 (Th17)/Treg immune cells and TLR4 pathway biomarkers. The antagonistic activity of that specific antagonist of TLR4 (TAK-242) (1 µmol·L^-1), a specific TLR4 blocker, against S1 (10 µmol·L^-1) was examined in co-cultured 16HBE cells and bone marrow-derived cells (BMDCs) or splenic lymphocytes (SLs) induced with LPS (1 µg·mL^-1) to elucidate the TLR4 pathway's mediating role. S1 demonstrated reduced airway responsiveness, lung damage, and inflammation, with downregulation of IgE, PGD2, interleukins, TNF-α, GM-CSF, CXCL1, CCL11, and mMCP-1. It also impeded MC activation and degranulation, upregulated IL-10, and influenced Th17/Treg immune cell transformation following OVA challenge. Furthermore, S1 inhibited the TLR4 pathway in OVA-AA mice, and TLR4 antagonism enhanced S1's positive effects. Analysis using an OVA-AA mouse model demonstrated that S1 alleviates AA clinical symptoms, restores lung function, and inhibits airway response. S1's therapeutic effects occur through regulation of Th17/Treg immune cells and inflammation, attributable at least partially to the TLR4 pathway. This study provides molecular justification for S1 in AA treatment.

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 mediating role of balanced time perspective in the relationship between social jetlag and depressive symptom among junior high school students, so as to privide a basis for improving their mental health. Methods: In October 2024, a method of combining convenient sampling with cluster sampling was used to select 3 438 junior high school students from two districts(Yubei District and Kaizhou District) in Chongqing. The Center for Epidemiological Studies Depression Scale (CES-D), Munich Chronotype Questionnaire (MCTQ), and Balanced Time Perspective Inventory (BTPI) were administered for the survey. Statistical analyses included t-test, ANOVA, and Spearman correlation analysis, and conduct a mediation effect analysis using the Process program. Results: The mean score of depressive symptom among junior high school students was 15.00(9.00, 23.00 ), social jetlag was 1.18(0.72, 1.83) hours, and balanced time perspective score was -0.18(-2.82, 2.93). Social jetlag among junior high school students was positively correlated with depressive symptom score ( r =0.17), while balanced time perspective was negatively correlated with social jetlag ( r =-0.15) and depressive symptom score ( r =-0.68) (all P <0.01). Mediation analysis showed that balanced time perspective partially mediated the relationship between social jetlag and depressive symptom ( β=0.68, P <0.01) among junior high school students, accounting for 53.97% of the total effect. Conclusions Social jetlag of junior high school students influences depressive symptoms by affecting balanced time perspective. Efforts should be made to improve adolescents balanced time perspective for reducing the impact of social jetlag on depressive symptom.

Objective: To explore the potential subgroups of emotion regulation strategies among junior high school students and their moderating role in the relationship between parental psychological control and depressive symptom, so as to provide basis for improving mental health among junior high school students. Methods: In October 2024, a cluster sampling method was used to select 3 389 junior high school students from 4 secondary schools across 2 districts in Chongqing. Surveys were conducted by using Center for Epidemiological Survey-Depression Scale (CES-D), Chinese Parental Psychological Control Scale (CPPCS), and Emotion Regulation Questionnaire (ERQ). Latent profile analysis was applied to identify subgroups based on emotion regulation strategies, and multiple linear regression model was used to test the moderating effect of emotion regulation strategies on the association between parental psychological control and depressive symptom among junior high school students. Results: According to the two dimensions of emotion regulation strategies-cognitive reappraisal and expressive suppression, three distinct subgroups were identified:the moderate cognitive reappraisal-moderate expressive suppression subgroup (moderate-moderate subgroup, 64.2%), the low cognitive reappraisal-moderate expressive suppression subgroup (low-moderate subgroup, 9.6%), and the high cognitive reappraisal low expressive suppression subgroup (high-low subgroup, 26.1%). Hierarchical regression results indicated that both parental psychological control ( β =0.70) and deficits in cognitive reappraisal (low moderate subgroup, β =5.38) were associated with an increased risk of depressive symptoms, whereas the high-low subgroup appeared to mitigate depressive symptom ( β =-3.47) (all P < 0.01). Compared to the moderate-moderate subgroup, the low-moderate subgroup showed a statistically significant positive moderating effect on the relationship between parental psychological control and depressive symptom ( β =0.14, P <0.05), while the negative moderating effect of the high-low subgroup was not statistically significant ( β =-0.07, P >0.05). Conclusions The negative impact of parental psychological control on depressive symptom in junior high school students varies depending on the use of different emotion regulation strategies. For a majority of students, enhancing cognitive reappraisal training may help alleviate the adverse effects of parental psychological control and promote better mental health.

BACKGROUND: G protein-coupled receptor kinase 2 (GRK2) could participate in the regulation of diverse cells via interacting with non-G-protein-coupled receptors. In the present work, we explored how paroxetine, a GRK2 inhibitor, modulates the differentiation and activation of immune cells in rheumatoid arthritis (RA). METHODS: The blood samples of healthy individuals and RA patients were collected between July 2021 and March 2022 from the First Affiliated Hospital of Anhui Medical University. C57BL/6 mice were used to induce the collagen-induced arthritis (CIA) model. Flow cytometry analysis was used to characterize the differentiation and function of dendritic cells (DCs)/T cells. Co-immunoprecipitation was used to explore the specific molecular mechanism. RESULTS: In patients with RA, high expression of GRK2 in peripheral blood lymphocytes, accompanied by the increases of phosphatidylinositol 3 kinase (PI3K), protein kinase B (AKT), and mammalian target of rapamycin (mTOR). In animal model, a decrease in regulatory T cells (T regs ), an increase in the cluster of differentiation 8 positive (CD8 + ) T cells, and maturation of DCs were observed. Paroxetine, when used in vitro and in CIA mice, restrained the maturation of DCs and the differentiation of CD8 + T cells, and induced the proportion of T regs . Paroxetine inhibited the secretion of pro-inflammatory cytokines, the expression of C-C motif chemokine receptor 7 in DCs and T cells. Simultaneously, paroxetine upregulated the expression of programmed death ligand 1, and anti-inflammatory cytokines. Additionally, paroxetine inhibited the PI3K-AKT-mTOR metabolic pathway in both DCs and T cells. This was associated with a reduction in mitochondrial membrane potential and changes in the utilization of glucose and lipids, particularly in DCs. Paroxetine reversed PI3K-AKT pathway activation induced by 740 Y-P (a PI3K agonist) through inhibiting the interaction between GRK2 and PI3K in DCs and T cells. CONCLUSION Paroxetine exerts an immunosuppressive effect by targeting GRK2, which subsequently inhibits the metabolism-related PI3K-AKT-mTOR pathway of DCs and T cells in RA.
G-Protein-Coupled Receptor Kinase 2/metabolism* ; Arthritis, Rheumatoid/immunology* ; Animals ; Dendritic Cells/metabolism* ; Paroxetine/therapeutic use* ; Proto-Oncogene Proteins c-akt/metabolism* ; Mice ; Humans ; Mice, Inbred C57BL ; Signal Transduction/drug effects* ; Male ; Phosphatidylinositol 3-Kinases/metabolism* ; Lymphocyte Activation/drug effects* ; Female ; T-Lymphocytes/metabolism* ; Middle Aged

Ovarian cancer （OC） is a common gynecological malignant tumor in clinical practice. In the early stage，it is often asymptomatic，while in the late stage，it mainly presents with non-specific symptoms such as abdominal distension，poor appetite，and dull abdominal pain. Some patients may also have cachexia such as weight loss and anemia. Early diagnosis is difficult，and the mortality rate ranks first among gynecological malignant tumors，making OC a major challenge in clinical treatment. The classic Chinese medicine formula Guizhi Fulingwan comes from the Jingui Yaolue and has the effects of promoting blood circulation，removing blood stasis，and reducing abdominal lumps. In recent years，it has been widely used to treat OC with good results. This article summarized the clinical application of Guizhi Fulingwan in the treatment of OC from two aspects：The analysis of its basic prescriptions and clinical research. In terms of basic prescriptions，the formula has the ability to promote blood circulation，remove blood stasis，and reduce abdominal lumps. It can exert therapeutic effects considering both water and blood aspects and reduce abdominal lumps， with characteristics of simultaneous Yang warming and heat clearing and parallel supplementation and elimination. Through the methods of "circulation" and "supplementation"， it strengthens the body，dispels evil，and eliminates underlying symptoms. In clinical studies，Guizhi Fulingwan can be applied to various stages of patients with OC，which not only promotes the recovery of the body after OC surgery but also can be combined with chemotherapy and immunotherapy to synergistically treat advanced OC and enhance treatment efficacy. In addition，the formula can also alleviate various adverse reactions caused by chemotherapy，with high safety，improve patients' quality of life，prolong survival，and optimize tumor control effects. Based on the above analysis，this article elaborated on the current clinical research status of Guizhi Fulingwan combined with Western medicine in the treatment of OC and proposed suggestions and improvements to address the shortcomings in current clinical research，so as to provide reference for the clinical application of this formula in the treatment of OC and the construction of a combined traditional Chinese and Western medicine treatment model.