Based on ultra-high performance liquid chromatography-quadrupole-Exactive Orbitrap mass spectrometry(UPLC-Q-Exactive Orbitrap-MS) technology and network pharmacology, this study explored the pharmacodynamic substances and potential mechanisms of Huazhuo Sanjie Chubi Decoction in the treatment of gouty arthritis(GA). UPLC-Q-Exactive Orbitrap-MS technology was used to identify the components in Huazhuo Sanjie Chubi Decoction, and the qualitative analysis of its active ingredients was carried out, with a total of 184 active ingredients identified. A total of 897 active ingredient targets were screened through the PharmMapper database, and 491 GA-related disease targets were obtained from the OMIM, GeneCards, CTD databases. After Venn analysis, 60 intersecting targets were obtained. The component target-GA target network was constructed through the Cytoscape platform, and the STRING database was used to construct a protein-protein interaction network, with 16 core targets screened. The core targets were subjected to Gene Ontology(GO) and Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway enrichment analyses, and the component-target-pathway network was constructed. It was found that the main active ingredients of the formula for the treatment of GA were phenols, flavonoids, alkaloids, and terpenoids, and the key targets were SRC, MMP3, MMP9, REN, ALB, IGF1R, PPARG, MAPK1, HPRT1, and CASP1. Through GO analysis, it was found that the treatment of GA mainly involved biological processes such as lipid response, bacterial response, and biostimulus response. KEGG analysis showed that the pathways related to the treatment of GA included lipids and atherosclerosis, neutrophil extracellular traps(NETs), IL-17, and so on. In summary, phenols, flavonoids, alkaloids, and terpenoids may be the core pharmacodynamic substances of Huazhuo Sanjie Chubi Decoction in the treatment of GA, and the pharmacodynamic mechanism may be related to SRC, MMP3, MMP9, and other targets, as well as lipids and atherosclerosis, NETs, IL-17, and other pathways.
Drugs, Chinese Herbal/therapeutic use* ; Network Pharmacology ; Arthritis, Gouty/metabolism* ; Chromatography, High Pressure Liquid/methods* ; Humans ; Mass Spectrometry/methods* ; Protein Interaction Maps/drug effects*

OBJECTIVES: To evaluate the effectiveness of single-balloon and double-balloon enteroscopy in diagnosing pediatric small bowel diseases and assess the diagnostic efficacy of computed tomography enterography (CTE) for small bowel diseases using enteroscopy as the reference standard. METHODS: Clinical data from 576 children who underwent enteroscopy at Hunan Children's Hospital between January 2017 and December 2023 were retrospectively collected. The children were categorized based on enteroscopy type into the single-balloon enteroscopy (SBE) group (n=457) and double-balloon enteroscopy (DBE) group (n=119), and the clinical data were compared between the two groups. The sensitivity and specificity of CTE for diagnosing small bowel diseases were evaluated using enteroscopy results as the standard. RESULTS: Among the 576 children, small bowel lesions were detected by enteroscopy in 274 children (47.6%).There was no significant difference in lesion detection rates or complication rates between the SBE and DBE groups (P>0.05), but the DBE group had deeper insertion, longer procedure time, and higher complete small bowel examination rate (P<0.05). The complication rate during enteroscopy was 4.3% (25/576), with 18 cases (3.1%) of mild complications and 7 cases (1.2%) of severe complications, which improved with symptomatic treatment, surgical, or endoscopic intervention. Among the 412 children who underwent CTE, the sensitivity and specificity for diagnosing small bowel diseases were 44.4% and 71.3%, respectively. CONCLUSIONS SBE and DBE have similar diagnostic efficacy for pediatric small bowel diseases, but DBE is preferred for suspected deep small bowel lesions and comprehensive small bowel examination. Enteroscopy in children demonstrates relatively good overall safety. CTE demonstrates relatively low sensitivity but comparatively high specificity for diagnosing small bowel diseases.
Retrospective Studies ; Treatment Outcome ; Double-Balloon Enteroscopy/statistics & numerical data* ; Single-Balloon Enteroscopy/statistics & numerical data* ; Humans ; Male ; Female ; Child ; Operative Time ; Tomography, X-Ray Computed/statistics & numerical data* ; Sensitivity and Specificity ; Intestine, Small/surgery* ; Intestinal Diseases/surgery*

Objective To investigate the changes in cerebral blood flow(CBF)and whole-brain functional connectivity(FC)in patients with neuromyelitis optica spectrum disorder(NMOSD)examined by conventional MRI.Methods Twenty-two patients with NMOSD and 25 matched healthy controls(HC)were included,and all subjects'conventional MRI sequences,arterial spin labeling(ASL),and resting-state functional magnetic resonance imaging(rs-fMRI)image data were acquired,and the brain regions with CBF differences between groups extracted based on ASL were used as seed points for whole-brain FC analysis,the FC differences between the groups were compared,and the correlation between the parameter values and the expanded disability status scale(EDSS)scores were analyzed.Results NMOSD patients exhibited a decrease in CBF value in the right supratemporal gyrus,bilateral calcarine fissure and right supplementary motor area,while the right middle temporal gyrus displayed an increase when compared to HC.Whole-brain FC analysis based on seed points revealed an increase in FC value between bilateral calcarine fissure and olfactory cortex,as well as between right supplementary motor area and left superior frontal gyrus.While FC value between the right supplementary motor area and left precentral gyrus,bilateral thalamus,right supraoccipital gyrus were decreased.The FC value between the right supratemporal gyrus and right inferior parietal lobule,left supratemporal gyrus,right precuneus lobes showed a decrease[Gaussian random field(GRF)-corrected,P＜0.05].There was a correlation between CBF values of bilateral calcarine fissure and EDSS scores in NMOSD patients(rs=-0.456,P=0.033).Conclusion There are CBF and FC abnormalities in multiple brain regions in NMOSD patients,which provide imaging basis and insights for the brain micropathological changes of NMOSD,and help to understand the pathology and neural mechanism of NMOSD.

The implementation of amplicon sequencing and metagenomic sequencing methods in the whole-genome sequen-cing for MPXV testing was compared,to provide a technical reference for sequencing,tracing,and epidemic prevention and control of MPXV.For amplicon sequencing,targeted amplification of the viral whole genome was performed on MPXV DNA,and was followed by next-generation sequencing of the amplification products.For metagenomic sequencing,next-generation sequencing was performed directly on MPXV DNA.After the sequences were obtained,software such as CLC and IGV were used to analyze the effective data percentage,sequencing depth,and whole-genome sequencing coverage under different sequen-cing depths for both sequencing methods,to evaluate sequencing quality.Nextclade was used to analyze virus typing,muta-tions,and deletions.Subsequently,the similarity and completeness of sequences obtained through both sequencing methods were further compared.On the basis of mapping to the refer-ence sequence of strain MPXV-M5312_HM12_Rivers(Gen-Bank number NC_063383.1),the percentage effective data obtained from amplicon sequencing and metagenomic sequen-cing was 99.72％and 7.54％,respectively,with a sequencing depth range of 0× to 334 839 ×,and 44 × to 1 000 ×.On the basis of a sequencing depth of 10 ×,the site coverage of the above was 90.3％and 100％,respectively.IGV was used to validate the whole-genome coverage under different sequencing depths.The depth coverage of whole-genome sites for metagenomic sequencing was uniform,whereas that of the whole-genome sites for amplicon sequencing was uneven and significantly differed.Virus typing and sequence similarity analysis indicated that the viral sequences obtained with the two sequencing methods all belonged to the Ⅱb B.1 lineage of MPXV.Comparison with the reference sequence indicated that metagenomic sequencing identified 73 nucleotide mutation sites,whereas amplicon sequen-cing identified 68 mutation sites.Further analysis demonstrated that seven common mutation sites of Ⅱb B.1 were not detected in the amplicon sequencing,and two false positive private mutation sites were identified.Amplicon or metagenomic sequencing methods thus can be flexibly used in MPXV virus whole-genome sequencing.Amplicon sequencing yields more effective data,whereas metagenomic sequencing provides better uniformity of coverage and sequence accuracy.This study provides a prelimi-nary understanding of the efficacy of each method and may serve as a technical reference for improving the success rate of whole-genome sequencing of MPXV.

Objective:To assess the association between diabetes mellitus and the risk of sudden cardiac death (SCD), and to identify potential contributing factors.Methods:This meta-analysis was an updated version of the original study Diabetes mellitus and the risk of sudden cardiac death： a systematic review and meta-analysis of prospective studies. The original review included all eligible case-control and cohort studies published in PubMed and Embase up to 2017 that investigated the association between diabetes and SCD risk. In this updated study, newly published studies were added, including those available in PubMed, Embase, China National Knowledge Infrastructure (CNKI), and WANFANG MED ONLINE up to December 3, 2023. Search terms included "diabetes""glucose""sudden cardiac death" "cardiac arrest" and their Chinese equivalent. The primary outcome was the risk of SCD, while factors such as country, ethnicity, skin color, follow-up duration, left ventricular ejection fraction (LVEF), baseline comorbidities, and other relevant variables were analyzed as potential influencing factors. Relative risk ( RR) was used as the summary measure. A random-effects model was used when significant heterogeneity was detected, otherwise a fixed-effects model was used. Cochran′s Q test was used for subgroup analysis to assess the influence of factors such as region, baseline diseases, LVEF, and ethnicity (based on skin color) on the outcomes. Results:A total of 32 cohort/case-control studies with a combined sample size of 3 252 954 individuals were included. The meta-analysis showed that the risk of SCD in patients with diabetes was double that of non-diabetics ( RR=2.00, 95% CI: 1.83-2.19, P<0.001). In Asian populations, the risk of SCD in diabetic patients was 1.78 times that of non-diabetic individuals ( RR=1.78, 95% CI: 1.51-2.10), 2.05 times that of in European populations ( RR=2.05, 95% CI: 1.79-2.34), and 2.12 times that of in American populations ( RR=2.12, 95% CI: 1.82-2.47), with no statistically significant heterogeneity between regions ( P=0.287). Among individuals without other baseline comorbidities, the risk of SCD was 2.12 times higher in diabetic patients than in those without diabetes ( RR=2.12, 95% CI: 1.89-2.38). In patients with baseline coronary heart disease, the risk was 1.75 times that of non-diabetics ( RR=1.75, 95% CI: 1.45-2.11). In those with baseline heart failure, the risk was 1.92 times that of non-diabetics ( RR=1.92, 95% CI: 1.51-2.43). In patients with baseline atrial fibrillation, the risk was 4.00 times that of non-diabetic individuals ( RR=4.00, 95% CI: 1.38-11.56). In patients undergoing hemodialysis due to renal failure, the risk was 1.76 times that of non-diabetic individuals ( RR=1.76, 95% CI: 1.25-2.48), with no statistically significant heterogeneity between groups ( P=0.262). In cardiac patients with LVEF>50%, the risk of SCD in diabetic patients was 2.08 times that of non-diabetic individuals ( RR=2.08, 95% CI: 1.57-2.75), and in those with LVEF<50%, the risk was 1.69 times that of non-diabetic individuals ( RR=1.69, 95% CI: 1.30-2.18), with no statistically significant heterogeneity between groups ( P=0.277). In yellow-skinned populations, the risk of SCD in diabetic patients was 1.80 times that of healthy individuals ( RR=1.80, 95% CI: 1.73-1.87), and in white-skinned populations, it was 2.18 times that of healthy individuals ( RR=2.18, 95% CI: 1.88-2.54), with statistically significant heterogeneity between groups ( P=0.014). Conclusions:Diabetes mellitus significantly increased the risk of SCD, and this effect may be more pronounced in white-skinned populations, while region, baseline comorbidities, and LVEF had no further effect.

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 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 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.