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:This study investigated the expression of C-X-C motif chemokine receptor 3 (CXCR3) on CD45RO? T cells in the peripheral blood of patients with Alzheimer′s disease (AD) and its association with clinical features.Methods:A total of 41 AD patients and 30 age-and sex-matched healthy controls (HCs) were recruited from the Department of Neurology at the Medical Division of PLA General Hospital between September 2022 and March 2024. Flow cytometry was used to quantify CXCR3 expression on CD45RO? T cell subsets in peripheral blood. Dementia severity in AD patients was assessed using the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA). Spearman correlation analysis examined the relationship between CD45RO?CXCR3? T cell levels and cognitive function in the AD group. Receiver operating characteristic (ROC) curve analysis determined the predictive utility of CD45RO?CXCR3? T cells for AD, quantified by the area under the curve (AUC).Results:Compared to healthy controls, AD patients exhibited significantly elevated levels of CD8?CD45RO?CXCR3? T cells [17.8% (7.2%, 40.3%) vs. 8.2% (5.1%, 12.3%), Z=-2.59, P<0.05]. However, no significant differences were observed for CD4?CD45RO?CXCR3? T cells, CD4?CD45RO?CXCR3? T cells, or CD8?CD45RO?CXCR3? T cells ( P>0.05). Spearman correlation analysis revealed a negative correlation between CD8?CD45RO?CXCR3? T cell levels and cognitive scores (MMSE: r=-0.72, P<0.05; MoCA: r=-0.70, P<0.05). ROC analysis demonstrated an AUC of 0.81 for CD8?CD45RO?CXCR3? T cells in predicting AD, with a sensitivity of 59.0% and specificity of 93.3%. Conclusions:CXCR3 expression is significantly upregulated on CD8?CD45RO? T cells in AD patients, and its levels correlate with cognitive impairment severity. These findings suggest that CD8?CD45RO?CXCR3? T cells may serve as a potential biomarker for AD diagnosis and progression monitoring.

Objective To analyze the formulas and drugs related to sweating syndrome in Chinese Medical Canon through data mining,and explore the medication patterns of sweating syndrome treatment.Methods Literature related to sweating syndrome were searched the Chinese Medical Canon electronic database.A total of 2392 prescriptions were collected,including 1974 for spontaneous sweating syndrome,368 for night sweating syndrome,and 50 for yellow sweating syndrome.Association rule analysis and cluster analysis were performed on the included drugs.Results For spontaneous sweating syndrome,the key herbs were Gancao and Renshen,mainly pungent in flavor,primarily affecting the spleen meridian.Association rule analysis revealed 22 core herb pairs,clustering into 5 patterns.For night sweating syndrome,the key herbs were Danggui and Renshen,mainly sweet in flavor,primarily affecting the lung meridian.Association rule analysis revealed 12 core herb pairs,clustering into 5 patterns.For yellow sweating syndrome,the key herbs were Guizhi and Huangqi,mainly pungent in flavor,primarily affecting the lung meridian.Association rule analysis revealed 8 core herb pairs,clustering into 4 patterns.Conclusion This study systematically reveal the complex syndrome structures within three types of sweating syndromes and identify representative herbal combinations and their pathogenesis by cluster and association rule analysis.

Objective To analyze the formulas and drugs related to sweating syndrome in Chinese Medical Canon through data mining,and explore the medication patterns of sweating syndrome treatment.Methods Literature related to sweating syndrome were searched the Chinese Medical Canon electronic database.A total of 2392 prescriptions were collected,including 1974 for spontaneous sweating syndrome,368 for night sweating syndrome,and 50 for yellow sweating syndrome.Association rule analysis and cluster analysis were performed on the included drugs.Results For spontaneous sweating syndrome,the key herbs were Gancao and Renshen,mainly pungent in flavor,primarily affecting the spleen meridian.Association rule analysis revealed 22 core herb pairs,clustering into 5 patterns.For night sweating syndrome,the key herbs were Danggui and Renshen,mainly sweet in flavor,primarily affecting the lung meridian.Association rule analysis revealed 12 core herb pairs,clustering into 5 patterns.For yellow sweating syndrome,the key herbs were Guizhi and Huangqi,mainly pungent in flavor,primarily affecting the lung meridian.Association rule analysis revealed 8 core herb pairs,clustering into 4 patterns.Conclusion This study systematically reveal the complex syndrome structures within three types of sweating syndromes and identify representative herbal combinations and their pathogenesis by cluster and association rule analysis.

Objective:This study investigated the expression of C-X-C motif chemokine receptor 3 (CXCR3) on CD45RO? T cells in the peripheral blood of patients with Alzheimer′s disease (AD) and its association with clinical features.Methods:A total of 41 AD patients and 30 age-and sex-matched healthy controls (HCs) were recruited from the Department of Neurology at the Medical Division of PLA General Hospital between September 2022 and March 2024. Flow cytometry was used to quantify CXCR3 expression on CD45RO? T cell subsets in peripheral blood. Dementia severity in AD patients was assessed using the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA). Spearman correlation analysis examined the relationship between CD45RO?CXCR3? T cell levels and cognitive function in the AD group. Receiver operating characteristic (ROC) curve analysis determined the predictive utility of CD45RO?CXCR3? T cells for AD, quantified by the area under the curve (AUC).Results:Compared to healthy controls, AD patients exhibited significantly elevated levels of CD8?CD45RO?CXCR3? T cells [17.8% (7.2%, 40.3%) vs. 8.2% (5.1%, 12.3%), Z=-2.59, P<0.05]. However, no significant differences were observed for CD4?CD45RO?CXCR3? T cells, CD4?CD45RO?CXCR3? T cells, or CD8?CD45RO?CXCR3? T cells ( P>0.05). Spearman correlation analysis revealed a negative correlation between CD8?CD45RO?CXCR3? T cell levels and cognitive scores (MMSE: r=-0.72, P<0.05; MoCA: r=-0.70, P<0.05). ROC analysis demonstrated an AUC of 0.81 for CD8?CD45RO?CXCR3? T cells in predicting AD, with a sensitivity of 59.0% and specificity of 93.3%. Conclusions:CXCR3 expression is significantly upregulated on CD8?CD45RO? T cells in AD patients, and its levels correlate with cognitive impairment severity. These findings suggest that CD8?CD45RO?CXCR3? T cells may serve as a potential biomarker for AD diagnosis and progression monitoring.

Objective To investigate the effects of Optimized New Shengmai Powder on myocardial fibrosis in rats with heart failure based on the β1-AR/cAMP/PKA/CREB signaling pathway.Methods Totally 50 SD rats were randomly divided into sham-operation group(10 rats)and operation group(40 rats).The left anterior descending coronary artery was ligated to establish a rat model of heart failure.The modeling rats were randomly divided into the model group,the captopril group,and TCM low-and high-dosage groups,with 8 rats in each group.The administration groups received relevant medicine for gavage for 4 weeks.LVEF and LVFS in rats were detected by echocardiography,and measurement of heart and lung mass and calculation of heart and lung organ coefficients were performed,myocardial fibrosis degree was observed by histopathology,serum NT-ProBNP and cAMP,Col Ⅰ,and Col Ⅲcontent in myocardial tissue were detected by ELISA,immunohistochemical was used to detect β1-AR,cAMP positive expression,and Western blot was used to detect the expression of β1-AR/cAMP/PKA/CREB signaling pathway related proteins.Results Compared with the sham-operation group,the LVEF and LVFS of the model group rats were significantly decreased(P<0.01),and the heart and lung organ coefficient significantly increased(P<0.01);the number of myocardial cells decreased,collagen volume fraction increased,and the proportion of type Ⅰ/Ⅲcollagen fibers increased(P<0.01),the contents of serum NT-ProBNP and myocardial tissue Col Ⅰ and Col Ⅲincreased significantly,while the cAMP content in myocardial tissue decreased significantly(P<0.01),the positive expressions of β1-AR and cAMP were significantly decreased(P<0.01),the expressions of β1-AR,AC1,cAMP,p-PKA,and p-CREB proteins were significantly decreased,while protein expressions of p-Smad2,Col Ⅰ,Col Ⅲ,and α-SMA significantly increased(P<0.05,P<0.01).Compared with the model group,the administration groups could increase LVEF and LVFS and decrease heart and lung organ coefficient to different degrees in rats;increase the number of myocardial cells,decrease collagen volume fraction and the proportion of type Ⅰ/Ⅲ collagen fibers,down-regulate serum NT-ProBNP and the content of Col Ⅰ and Col Ⅲ in myocardial tissue,up-regulate the content of cAMP,increase the positive expressions of β1-AR and cAMP in myocardial tissue,up-regulate β1-AR,AC1,cAMP,p-PKA,p-CREB protein expression,and inhibit p-Smad2,Col Ⅰ,Col Ⅲ,and α-SMA protein expression,in which the effects of the TCM high-dosage group and captopril group were more pronounced(P<0.01,P<0.05).Conclusion Optimized New Shengmai Powder can effectively reduce myocardial fibrosis in heart failure rats,improve myocardial hypertrophy and remodeling,and increase left ventricular contractility,and the mechanism may be related to the activation of the β1-AR/cAMP/PKA/CREB signaling pathway.

Synchronous multifocal osteosarcoma (SMOS) was analyzed for its predisposing age, sex, location, oncology characteristics, and survival time with different treatment. The key words about "multifocal osteosarcoma" had been used to search articles which includ Synchronous multifocal osteosarcoma patients databases from 1949 to 2020. The articles have been filtratedby title, abstract and full text. There were 80 articles used for thisstudy. All the patients were objects of thisstudy. Butthe same patients' data in different articles had not been used repeatedly. The patients' data had been collected as much aspossible, including age, location, treatment, survival timeand so on. All the patients' data had been used forsystematic analysis. All of the 80 articles and 264 patients had been studied. The mean onset age was 16.17 years old and the peak age of onset was 10-20 years old. The gender difference had been uncovered and the sex ratio was 1.76∶1. The incidence site of 188 patients (92.16%) was located in the extremities. Alkaline phosphatase was elevated in 135 patients (95.10%). The pathological type was osteoblastic osteosarcoma in 134 patients (76.14%). There were 3 patients with hypocalcemia and 2 patients with anemia. The mean survival time of 15 patients (15/58) who gave up treatment was 4.51 months. The mean survival time of 23 patients with chemotherapy was 8.97 months. The mean survival time was 16.17 months in 11 patients with preoperative chemotherapy and surgical treatment. Nine patients with neoadjuvant chemotherapy, surgery and postoperative chemotherapy had an average survival time of 23.28 months. Multiple osteosarcoma of the same type was rare, with high degree of malignancy and poor prognosis. The age of high incidence was 10-20 years old. Currently, the most effective treatment was neoadjuvant chemotherapy, surgery and postoperative chemotherapy.