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.

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.

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 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.

Chronic atrophic gastritis(CAG)is a chronic gastric disorder characterized by recurrent damage to the gastric mucosal epithelium,resulting in the reduction of intrinsic glands,with or without concurrent intestinal metaplasia.The"hyperactive stomach qi"theory,derived from Huang Di Nei Jing Su Wen Ji Zhu,proposes that the core pathogenesis of CAG lies in excessive stomach qi activity,grounded in the physiological principle of"strong yang qi in earth and weak yin qi in earth".This theory synthesizes the clinical manifestations and pathological progression of CAG,asserting that its development often involves intertwined pathological factors such as stagnation,dryness-heat,phlegm-dampness and stasis-toxicity.A triphasic therapeutic framework is proposed:the spleen qi deficiency phase,marked by impaired spleen transport function and dysregulated qi-fluid distribution,requiring spleen fortification and qi-fluid regulation;the hyperactive stomach qi phase,characterized by intensified stomach qi activity coupled with dryness-damp stagnation,necessitating stagnation resolution,dampness elimination and yin nourishment;the decline and disorder of middle qi phase,characterized by the deficiency of the middle qi,with phlegm,blood stasis and toxins forming the terminal stage.Treatment should focus on reinforcing the middle and restoring balance,detoxifying and dissipating accumulation.By exploring CAG pathogenesis and treatment through the lens of"hyperactive stomach qi",this study aimed to provide novel theoretical insights and therapeutic strategies for TCM in the prevention and treatment of CAG.

Objective To develop the value of machine learning(ML)models based on contrast-transthoracic echocardiography(cTTE)and transesophageal echocardiography(TEE)combined with clinical and laboratory indicators for predicting patent foramen ovale-associated stroke(PFO-AS).Methods Totally 313 patients with PFO diagnosed with cTTE and TEE were retrospectively enrolled.Among them,65 cases were found complicated with ischemic stroke and confirmed as PFO-AS(PFO-AS group),and the rest 248 cases without ischemic stroke were classified as non-PFO-AS group.The patients were divided into training set(n=219,including 48 cases of PFO-AS and 171 cases of non-PFO-AS)and test set(n=94,including 17 cases of PFO-AS and 77 cases of non-PFO-AS)at the ratio of 7∶3.Univariable and multivariable logistic regression(LR)were used to analyze clinical and laboratory indicators as well as cTTE and TEE parameters in training set to screen independent predictive factors of PFO-AS.ML models,including LR,K-nearest neighbor(KNN),support vector machine(SVM),random forest(RF),decision tree(DT),back propagation neural network(BPNN)and gradient boosting machine(GBM)were constructed,and the predictive efficacy of the models for predicting PFO-AS was evaluated,then the optimal model was selected.Results Patient's age＞49-69 years,with smoking history,plasma albumin≥43.8 g/L,significant right-to-left shunt at rest shown on cTTE and complicated atrial septal aneurysm shown on TEE were all independent predictors of PFO-AS,which were used to construct ML models.The area under the curve(AUC)of LR,KNN,SVM,RF,DT,BPNN and GBM models in training set was 0.779-0.853,while in test set was 0.730-0.877.RF model had relatively high and comparable sensitivity,specificity and AUC in both training and test sets,also higher precision and smaller Brier score in test set,hence was regarded as the optimal ML model.Conclusion RF model based on cTTE and TEE combined with clinical and laboratory indicators could be used to effectively predict PFO-AS.

Purpose To construct and validate a risk prediction model for in-stent restenosis(ISR)nomogram in patients with superficial femoral artery stent implantation.Materials and Methods 150 cases of superficial femoral artery stent implantation patients who were hospitalized in Department of Cardiovascular Surgery of the Second Affiliated Hospital of Army Medical University from February 2016 to November 2022 were retrospectively analyzed.Risk factors for ISR in patients with superficial femoral artery stent implantation were screened using univariate analysis,least absolute shrinkage and selection operator and multifactorial Logistic regression analysis.Nomograms were produced,Bootstrap method was used for internal validation,consistency index was used for model differentiation assessment,and calibration graphs were used for calibration assessment.Results Fifty-five patients(36.7%)with ISR one year after superficial femoral artery stenting were identified.Univariate analysis,least absolute shrinkage and selection operator and Logistic regression showed a history of stroke(OR=9.152,95%CI 2.957-28.322),chronic kidney disease(OR=14.639,95%CI 2.378-90.115),fibrinogen concentration(OR=8.422,95%CI 3.139-22.594),pre-procedural occlusion(OR=3.604,95%CI 1.446-8.981)and calcified plaque(OR=5.167,95%CI 2.044-13.059)were the best predictors of the occurrence of ISR one year post-procedure in patients with stenting of superficial femoral artery.The consistency index of the prediction model was 0.876(95%CI 0.812-0.939),with specificity and sensitivity of 93.6%and 70.9%,respectively;a Brie score of 0.124,and a consistency index after internal validation of the model of 0.859,respectively.Calibration plots showed that the ideal probability curves and the actual probability curves overlapped with each other well.Conclusion The Nomogram risk prediction model of superficial femoral artery stent restenosis constructed in this study has good differentiation and calibration,and is of good value for clinical prediction of ISR in patients with superficial femoral artery stent implantation.

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.