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.

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

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.

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

Median arcuate ligament compression syndrome (MALS) presents with atypical clinical manifestations. MALS is rarely reported in pediatric patients but is recognized as an independent risk factor for postoperative hepatic artery thrombosis in liver transplant recipients. We report a case of a pediatric liver transplant recipient with hepatolenticular degeneration, cirrhosis, and acute liver failure. Despite undergoing artificial liver support therapy, the patient showed no significant improvement in liver function and subsequently underwent liver transplantation. Intraoperatively, weak arterial pulsation and the absence of a pulsatile waveform in the hepatic artery anastomosis raised suspicion of MALS. The condition was successfully managed by releasing and transecting the median arcuate ligament, along with ligation of the splenic and left gastric arteries. To optimize transplantation outcomes, meticulous preoperative imaging assessment, particularly focusing on characteristic findings in CT angiography, is essential. Additionally, individualized surgical planning and intraoperative adjustments based on ultrasound monitoring and arterial pulsation assessments are critical for ensuring successful transplantation.

Objective:To explore clinical characteristics of multiple sclerosis(MS)patients in Suzhou,and to analyze main factors affecting their prognosis.Methods:General data,clinical symptoms,cerebrospinal fluid and imaging examinations of 51 MS patients admitted to Department of Neurology of the Second Hospital of Soochow University from July 31,2009 to July 31,2021 were retrospectively analyzed,and main factors affecting their prognosis were discussed.Results:Average age of onset of 51 MS patients was(43.3±15.6)years old,female accounted for 56.9%,male/female=1/1.3.Adult onset MS(AOMS)accounted for 62.8%,male/female=1/1.7;late onset MS(LOMS)accounted for 37.2%,male/female=1/0.9.Relapsing remitting MS(RRMS)accounted for 76.5%,and chronic onset accounted for 60.8%.Average annual recurrence rate was 8.8%.The first symptoms were numbness and weakness of limbs.Dizziness and numbness were more common in patients without recurrence after diagnosis of MS,and limb weak-ness and numbness were more common in patients with recurrence.Among lesions of MRI,62.7%(32/51)of periventricular involve-ment,52.9%(27/51)of spinal cord involvement,51.0%(26/51)of infratentorial involvement.Proportion of subtentorial and spinal cord(cervical,thoracic)involved were significantly higher in patients with recurrent MS than without recurrence.Values of albumin,IgG,IgA and IgM in cerebrospinal fluid increased with increase of recurrence times.EDSS score of male was higher than female,and LOMS score was higher than AOMS.MS patients without relapse had a low EDSS score,and median EDSS score at current follow-up was 0(0,1.00)score.MS score with relapse was relatively high,and median EDSS score at current follow-up was 2.75(0.25,7.25)score.Conclusion:MS patients with chronic onset are more common,with a high proportion of LOMS,and proportion of males increases with increasing age of onset.High EDSS score at first onset,cervical,thoracic and subtentorial lesions,increased values of cerebrospinal fluid albumin,IgG,IgA,IgM,age at first onset(50+years old),male associate with poor MS prognosis.

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.