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.

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

Objective To explore the diagnosis of infertility and clinical analysis of tubal patency by three-dimensional ultrasonic hysterosalpingography.Methods A total of 102 infertility patients admitted to Lishui Hospital of Traditional Chinese Medicine Affiliated to Zhejiang Chinese Medicine University from December 2022 to December 2024 were selected as the study objects to analyze the diagnosis of infertility and the clinical application of three-dimensional ultrasound hysterosalpingography on tubal patency.Results Of 102 infertility patients,the detection rate was 97.06％,the misdiagnosis rate was 0,and the missed diagnosis rate was 2.94％.The sensitivity,specificity and area under the curve of three-dimensional ultrasound hysterosalpingography for infertility were 72.65％,84.83％and 0.819.A total of 204 fallopian tubes were examined in 102 patients.Hysteroscopic fallopian tube staining showed patency(30.39％),obstruction(31.37％),and three-dimensional ultrasonic hysterosalpingography showed patency(31.86％),obstruction(32.35％).Compared with inflammation,the injection pressure,flow time and reverse flow of contrast agent in tumor and cyst lesions and congenital dysplasia were decreased(P<0.05),but there was no significant difference between tumor and cyst lesions and congenital dysplasia(P<0.05).In 102 patients,there was no hypersensitivity or other adverse reactions in the examination of three-dimensional ultrasound hysterosalpingography,and the satisfaction rate was 96.08％.Conclusion Three-dimensional ultrasound hysterosalpingography has a high diagnostic value for infertility,and can effectively evaluate the patency of fallopian tube,with high safety and patient satisfaction.

Objective To explore the diagnosis of infertility and clinical analysis of tubal patency by three-dimensional ultrasonic hysterosalpingography.Methods A total of 102 infertility patients admitted to Lishui Hospital of Traditional Chinese Medicine Affiliated to Zhejiang Chinese Medicine University from December 2022 to December 2024 were selected as the study objects to analyze the diagnosis of infertility and the clinical application of three-dimensional ultrasound hysterosalpingography on tubal patency.Results Of 102 infertility patients,the detection rate was 97.06％,the misdiagnosis rate was 0,and the missed diagnosis rate was 2.94％.The sensitivity,specificity and area under the curve of three-dimensional ultrasound hysterosalpingography for infertility were 72.65％,84.83％and 0.819.A total of 204 fallopian tubes were examined in 102 patients.Hysteroscopic fallopian tube staining showed patency(30.39％),obstruction(31.37％),and three-dimensional ultrasonic hysterosalpingography showed patency(31.86％),obstruction(32.35％).Compared with inflammation,the injection pressure,flow time and reverse flow of contrast agent in tumor and cyst lesions and congenital dysplasia were decreased(P<0.05),but there was no significant difference between tumor and cyst lesions and congenital dysplasia(P<0.05).In 102 patients,there was no hypersensitivity or other adverse reactions in the examination of three-dimensional ultrasound hysterosalpingography,and the satisfaction rate was 96.08％.Conclusion Three-dimensional ultrasound hysterosalpingography has a high diagnostic value for infertility,and can effectively evaluate the patency of fallopian tube,with high safety and patient satisfaction.

Objective To investigate the expression of lymphoid enhancer binding factor 1(LEF1)in B cell chronic lymphoproliferative disorders(B-CLPD)and estimate its value in the differential diagnosis of the subtype of B-CLPD.Methods A retrospective study was conducted on 58 patients diagnosed with B-CLPD by using bone marrow biopsy samples or lymphnode biopsy samples from Hematology Department of The Second People′s Hospital of Wuhu from September 2018 to June 2023,as well as 20 bone marrow biopsy samples which were diagnosis as non-hematologic malignancy in the control group.Immunohistochemical method was used to detect the expression of LEF1 in 78 samples,and statistical analysis was conducted.Results In all 78 cases,16 of 20 chronic lymphocytic leukemia(CLL)patients were LEF1 positive,the positive rate was 80%;mantle cell lymphoma 1/12;Follicular lymphoma 1/5;marginal zone cell lymphoma 0/11;Lymphoplasmacyticlymphom 0/8;hairy cell leukemia 0/2;in Control group no patient was LEF1 positive(P = 0.000).The expression of LEF1 is correlated with CD200 and CLL score(P<0.05).In the LEF1 negative group with 4 CLL patients,2 were detected with +12 chromosomal abnormality,the detective rate was higher than that of the LEF1 positive group(P>0.05).Conclusion LEF1 was a sensitive and specific diagnosis marker in CLL and B-CLPD subtype.

The European Association for the Study of Liver Diseases issued the "Clinical Practice Guidelines for the Management of Hepatic Encephalopathy" in 2022, which included recommendations for clinical diagnosis, assessment, treatment, management, and prevention. The Society’s "Hepatic Encephalopathy Clinical Practice Guidelines in Chronic Liver Disease," which was last published in 2014, and the "Guidelines for the Diagnosis and Treatment of Hepatic Encephalopathy in Cirrhosis," which the Chinese Society of Hepatology, Chinese Medical Association, released in 2018, have certain differences and updates in terms of comparison to terminology, grading and classification, diagnosis, clinical evaluation and treatment, management, and prevention. Herein, the updated points of this guideline and the differences between it and our nation’s guidelines are summarized in order to refine and understand the guiding role of the new version of the guideline for the clinical treatment of hepatic encephalopathy and provide aid for standardizing clinical diagnosis and treatment.

Intestinal flora is closely associated with chronic hepatitis B (CHB). Recent studies have shown that the imbalance of intestinal flora is associated with the development, progression, and prognosis of CHB, and the environment of intestinal flora may also change with disease progression, suggesting that intestinal flora and CHB interact with each other. This article reviews the influence of intestinal flora on the progression of CHB and related liver diseases and the role of intestinal flora regulation in the diagnosis and treatment of CHB and related liver diseases, in order to provide new ideas for the clinical treatment of CHB.

Exosomes (EXOs) are formed by intracellular multivesicular bodies and carry a variety of biomacromolecules such as lipids, proteins, encoding and non-coding RNAs, and mitochondrial DNA. EXOs can be released in vivo by different cell types, including hepatocytes, hepatic stellate cells, and immune cells and play the role of intercellular communication. More and more studies have shown that EXOs are involved in the development, progression, and prognosis of chronic hepatitis B (CHB) and hepatocellular carcinoma (HCC) caused by hepatitis B virus (HBV) infection and are expected to become potential biomarkers for the early diagnosis and prognostic evaluation of HBV-related HCC. This article reviews the role of EXOs in the host infection process of HBV and the importance of EXOs in the development, progression, and prognosis of CHB and HCC, in order to provide new ideas for the basic and clinical research in this field.

Hypoxic microenvironment is a common phenomenon of liver diseases and runs through the whole process of the development and progression of liver diseases. In recent years, the research on liver fibrosis and hypoxic microenvironment of hepatocellular carcinoma has attracted more and more attention. Hypoxia-inducible factor (HIF) is the most important hypoxic stress factor found at present. This article reviews the characteristics of hypoxic microenvironment in the liver and liver diseases and further elaborates on the association of HIF overexpression with hepatocyte damage, formation of fibrosis, and malignant transformation of hepatocytes.