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.

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.

As a common pathological state in clinical practice， Chong Mai Wei Bing （冲脉为病） is typically manifested as rebellious qi and a sense of urgency. It often involves various diseases caused by the disorder of qi circulation. From the perspectives of theoretical foundation， pathological characteristics， and clinical differentiation and treatment， this paper elaborates on the characteristics of Chong Mai （冲脉） as the cause of disease， including three main manifestations： upward qi surge， upward yin fire， and upward water-qi. Among these， the upward qi surge is further categorized into four aspects： Chong Qi （冲气） counterflow， counterflow of stomach qi， counterflow of kidney qi， and counterflow of liver qi. Three major treatment methods are proposed： pacifying the Chong Mai and reversing the counterflow， consolidating Chong Mai to subdue fire， and warming Chong Mai to resolve qi and promote water flow. This paper summarizes its practical application in clinical diagnosis and treatment， aiming to deepen the understanding of the functional and pathological mechanisms of Chong Mai， and to provide insights and methods for the traditional Chinese medicine diagnosis and treatment of various diseases.

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.

In recent years, with the continuous development and increasing maturity of interventional techniques, interventional treatment for congenital heart disease (CHD) has been progressively disseminated to county- and city-level hospitals in China. Concurrently, the standardized management of adult CHD (particularly patent foramen ovale) and the lifelong management of complex CHD are gaining increasing clinical attention, while the emergence of new techniques and products continuously advances the discipline. This article aims to review the new progress made in the field of interventional treatment for congenital heart disease in China during 2024. It specifically reviews and analyzes the following key aspects: (1) annual statistics on interventional closure procedures for CHD; (2) recent insights into patent foramen ovale closure; (3) advances in transcatheter pulmonary valve replacement; (4) interventional treatment and lifelong management strategies for complex CHD; (5) new interventional techniques for acquired heart disease; and (6) the application of artificial intelligence in CHD management. Through the synthesis and discussion of these topics, this article seeks to provide a detailed analysis of the current landscape of interventional treatment for CHD in China and project its future development trends.

OBJECTIVE: To explore the clinical and genetic characteristics of a patient with Beck-Fahrner syndrome attributed to a TET3 gene variants. METHODS: A case of Beck-Fahrner syndrome (proband) who was treated at the Children's Hospital of Nanjing Medical University in December 2021 was selected as the study subject. Clinical data of the family were collected. Peripheral blood samples of the proband and his parents were collected, and genomic DNA was extracted for whole exome sequencing (WES). Candidate variants were verified in the family by Sanger sequencing. According to the "Classification Criteria and Guidelines for Genetic Variations" formulated by the American College of Medical Genetics and Genomics (hereinafter referred to as "ACMG guidelines"), the pathogenicity of the TET3 gene variant sites was rated. This study was approved by the Medical Ethics Committee of the Children's Hospital of Nanjing Medical University (Ethics No.: 202402022-1). RESULTS: The proband was a male, with a age of 9 months at the time of consultation. His clinical manifestations included decreased muscle tone, global developmental delay, long face, and open mouth. WES revealed that he has harbored a c.2811_c.2812insAGAC (p.T938fs*27) (NM_001287491) truncation variant in exon 7 of the TET3 gene. Sanger sequencing showed that neither of his parents has harbored the same variant. According to the ACMG guidelines, the variant was rated as pathogenic (PVS1+PS2+PM2_Supporting). CONCLUSION The TET3 gene c.2811_c.2812insAGAC variant probably underlay the pathogenesis of Beck-Fahrner syndrome in the proband. Above discovery has enriched the mutational spectrum of the TET3 gene and provided a reference for the diagnosis and treatment of this disease.
Humans ; Male ; Frameshift Mutation ; Infant ; Dioxygenases/genetics* ; Exome Sequencing ; Female ; Proto-Oncogene Proteins/genetics* ; Pedigree

OBJECTIVES: To investigate the effect of avitinib for suppressing NLRP3 inflammasome activation and alleviating septic shock and explore the underlying mechanism. METHODS: Mouse bone marrow-derived macrophages (BMDM), human monocytic leukemia cell line THP-1, and peripheral blood mononuclear cells (PBMC) isolated from healthy volunteers were pre-treated with avitinib, followed by activation of the canonical NLRP3 inflammasome using agonists including nigericin, monosodium urate (MSU) crystals, or adenosine triphosphate (ATP). Non-canonical NLRP3 inflammasome activation was induced via intracellular transfection of lipopolysaccharide (LPS). Western blotting was used to detect the secretory protein markers of NLRP3 inflammasome activation and assess pyroptosis, and the levels of inflammatory cytokines in cell culture supernatant were determined with ELISA. In a mouse model of LPS-induced septic shock, the effect of avitinib treatment on the levels of inflammatory cytokines in serum and peritoneal lavage fluid were examined with ELISA, and survival curves of the mice were plotted using the Kaplan-Meier method. RESULTS: Avitinib significantly inhibited NLRP3 inflammasome activation in multiple cell types, and dose-dependently reduced IL-1β secretion and caspase-1 cleavage while suppressing GSDMD-mediated pyroptosis without obviously affecting IL-6 or TNF-α levels. In the mouse models of LPS-induced septic shock, avitinib significantly lowered IL-1β levels in serum and peritoneal fluid and extended survival time of the mice. CONCLUSIONS Avitinib suppresses NLRP3 inflammasome activation and alleviates septic shock in mice.
Animals ; Shock, Septic/metabolism* ; Mice ; NLR Family, Pyrin Domain-Containing 3 Protein ; Inflammasomes/drug effects* ; Humans ; Macrophages/metabolism* ; Interleukin-1beta/metabolism* ; Lipopolysaccharides

Objective: To screen and identify the key active molecules, signaling pathways, and therapeutic targets of Shuxuening (SXN) injection for treating liver cirrhosis (LC) and to evaluate its therapeutic potential using a mouse model. Methods: Target genes of SXN and LC were retrieved from public databases, and enrichment analysis was performed. A protein–protein interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING), and hub genes were identified using Molecular Complex Detection (MCODE). LC was induced in rats and mice via intraperitoneal injections of diethylnitrosamine and carbon tetrachloride (CCl4) for 12 weeks. Starting at week 7, SXN was administered intraperitoneally to the mice in the treatment group. Serum and liver tissues of the mice were collected for the detection of indicators, pathological staining, and expression analysis of hub targets using quantitative real-time polymerase chain reaction (qRT-PCR). Results: We identified 368 overlapping genes (OLGs) between SXN and LC targets. These OLGs were subsequently used to build a PPI network and to screen for hub genes. Enrichment analysis showed that these genes were associated with cancer-related pathways, including phosphoinositide-3-kinase/Akt and mitogen-activated protein kinase signaling and various cellular processes, such as responses to chemicals and metabolic regulation. In vivo experiments demonstrated that SXN treatment significantly improved liver function and pathology in CCl4-induced LC mice by reducing inflammation and collagen deposition. Furthermore, qRT-PCR demonstrated that SXN regulated the expression of MAPK8, AR and CASP3 in the livers of LC mice. Conclusion This study highlighted the therapeutic effects of SXN in alleviating LC using both bioinformatics and experimental methods. The observed effect was associated with modulation of hub gene expression, particularly MAPK8, and CASP3.

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.