Objective To investigate the risk factors of relapse in patients with ischemic stroke using aspirin for single drug antiplatelet therapy.Methods A retrospective analysis was conducted on 200 patients with mild and medium ischemic stroke after 12 months of single drug treatment with aspirin,and the patients were divided into non-recurrence group and recurrence group according to the recurrence of stroke.Univariate analysis and multivariate logistic regression model were used to explore whether the related indicators were risk factors for recurrence.The ROC curve was used to predict the critical value of risk factors.Results Univariate analysis showed that platelet count,Hcy level and the A allele at rs12041331 site of PEAR1 gene were statistically significant risk factors(P<0.05).Multifactor analysis showed that the independent risk factor for recurrence was homocysteine level[OR = 1.16(1.089-1.240),P<0.001)].The critical value of ROC prediction was 8.35 μmol/L(sensitivity 0.847,specificity 0.532).Conclusions The Hcy level,platelet count and A allele at rs12041331 site of PEAR1 gene are risk factors for recurrence in patients with ischemic stroke treated with aspirin for Single drug antiplatelet therapy.Hcy level is independent risk factor and can be used to predict the risk of recurrence.

ObjectiveTo provide scientific support for the compilation of high-quality anti-nuclear， biological， and chemical （NBC） medical textbooks in China by retrieving books in the field of biological weapons defense in China， summarizing the publication time and distribution of publishing institutions， and categorizing content and key points of related books. MethodsRelevant subject terms in the field of biological weapons defense were searched through the official website of China National Digital Library and other websites， up until December 31， 2023， and were limited to books. Topic analysis was conducted on the introductions and contents of the books using the latent Dirichlet allocation （LDA） model. The number of topics was determined based on perplexity， and topics were identified according to the intertopic distance map， followed by a qualitative description of the core content of each topic. ResultsA total of 104 books were included in this study， among which four were identified as higher educational textbooks. The volume of publications increased during the periods 2002‒2004 and 2020‒2023. Research institutions accounted for the highest percentage of publishers （37.78%）， and 56.67% of the publishers were military institutions. The study identified six topics： "distribution， defense， and response to biological weapons"， "category， diagnosis， and treatment of biological warfare agents"， "response to biological public health emergencies"， "status of nuclear， biological， and chemical weapons internationally"， "biosafety risk management and prevention and control"， and "technologies and equipment related to biological hazard identification". ConclusionThere are few books in the field of biological weapons defense in China and the content is relatively outdated. In the future， the preparation of teaching materials should be aimed at practical emergency handling techniques for biological weapons， enhance the emphasis on biological weapons detection and biological warfare early warning， improve the fundamental theories at different training levels， and timely update the current research status in the field.

OBJECTIVE To establish the analysis methods of 33 banned pesticides in Ginkgo Biloba leaves and the extracts, and conduct the risk assessment study. METHODS One hundred and thirty-six batches of Ginkgo Biloba leaves and 58 batches of Ginkgo Biloba leaves extract were detected by UPLC-MS and GC-MS. The acute and chronic intake risk of pesticide residues in samples were calculated by point assessment method, and the risk scores of the pesticides were calculated by the Britain veterinary residues matrix ranking. RESULTS Six kinds of banned pesticides were detected in 136 batches of Ginkgo Biloba leaves, the total detection rate was 35.29%, and the detection amount was 0.002−0.210 mg·kg−1. The chronic dietary intake risk was 0.018%−0.620%, and the acute dietary intake risk was 0.000 1%−0.014 0%, indicated that the dietary exposure risk of pesticides in Ginkgo biloba leaves was at a low level. Two kinds of banned pesticides were detected in 58 batches of Ginkgo Biloba leaves extract, the detection rate was 55.17%, and the detection amount was 0.002−1.788 mg·kg−1. The percentage of acceptable daily intake was 0.003%−0.143%, and the percentage of acute reference dose was 0.002 4%, which was also at a low level. Risk ranking results indicated that the risk of phorate was the highest and should be focused on the production and safety supervision. CONCLUSION The method has good stability, high precision and promising repeatability, which can be used for the detection of 33 prohibited pesticides in Ginkgo biloba leaves and their extracts. The results show that the residual amounts of 33 banned pesticides in Ginkgo Biloba leaves and its extracts were extremely low, and there is no significant health risks.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective This study aimed to evaluate whether the onset of the plateau phase of slow hepatitis B surface antigen decline in patients with chronic hepatitis B treated with intermittent interferon therapy is related to the frequency of dendritic cell subsets and expression of the costimulatory molecules CD40,CD80,CD83,and CD86. Method This was a cross-sectional study in which patients were divided into a natural history group(namely NH group),a long-term oral nucleoside analogs treatment group(namely NA group),and a plateau-arriving group(namely P group).The percentage of plasmacytoid dendritic cell and myeloid dendritic cell subsets in peripheral blood lymphocytes and monocytes and the mean fluorescence intensity of their surface costimulatory molecules were detected using a flow cytometer. Results In total,143 patients were enrolled(NH group,n = 49;NA group,n = 47;P group,n = 47).The results demonstrated that CD141/CD1c double negative myeloid dendritic cell(DNmDC)/lymphocytes and monocytes(%)in P group(0.041[0.024,0.069])was significantly lower than that in NH group(0.270[0.135,0.407])and NA group(0.273[0.150,0.443]),and CD86 mean fluorescence intensity of DNmDCs in P group(1832.0[1484.0,2793.0])was significantly lower than that in NH group(4316.0[2958.0,5169.0])and NA group(3299.0[2534.0,4371.0]),Adjusted P all<0.001. Conclusion Reduced DNmDCs and impaired maturation may be associated with the onset of the plateau phase during intermittent interferon therapy in patients with chronic hepatitis B.

Objective To explore characteristics of clinical parameters and cytokines in patients with drug-induced liver injury(DILI)caused by different drugs and their correlation with clinical indicators. Method The study was conducted on patients who were up to Review of Uncertainties in Confidence Assessment for Medical Tests(RUCAM)scoring criteria and clinically diagnosed with DILI.Based on Chinese herbal medicine,cardiovascular drugs,non-steroidal anti-inflammatory drugs(NSAIDs),anti-infective drugs,and other drugs,patients were divided into five groups.Cytokines were measured by Luminex technology.Baseline characteristics of clinical biochemical indicators and cytokines in DILI patients and their correlation were analyzed. Results 73 patients were enrolled.Age among five groups was statistically different(P=0.032).Alanine aminotransferase(ALT)(P=0.033)and aspartate aminotransferase(AST)(P=0.007)in NSAIDs group were higher than those in chinese herbal medicine group.Interleukin-6(IL-6)and tumor necrosis factor alpha(TNF-α)in patients with Chinese herbal medicine(IL-6:P<0.001;TNF-α:P<0.001)and cardiovascular medicine(IL-6:P=0.020;TNF-α:P=0.001)were lower than those in NSAIDs group.There was a positive correlation between ALT(r=0.697,P=0.025),AST(r=0.721,P=0.019),and IL-6 in NSAIDs group. Conclusion Older age may be more prone to DILI.Patients with NSAIDs have more severe liver damage in early stages of DILI,TNF-α and IL-6 may partake the inflammatory process of DILI.

Epigenomic imbalance drives abnormal transcriptional processes,promoting the onset and progression of cancer.Although defective gene regulation generally affects carcinogenesis and tumor suppression networks,tumor immunogenicity and immune cells involved in antitumor responses may also be affected by epigenomic changes,which may have significant implications for the development and application of epigenetic therapy,cancer immunotherapy,and their combinations.Herein,we focus on the impact of epigenetic regulation on tumor immune cell function and the role of key abnormal epigenetic processes,DNA methylation,histone post-translational modification,and chromatin structure in tumor immunogenicity,and introduce these epigenetic research methods.We emphasize the value of small-molecule inhibitors of epigenetic modulators in enhancing antitumor immune responses and discuss the challenges of developing treatment plans that combine epigenetic therapy and immuno-therapy through the complex interaction between cancer epigenetics and cancer immunology.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.