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*

Objective: To investigate the molecular mechanism and identify potential drugs for subthreshold depression (SD), and elucidate the detalied mechanism of Danzhi Xiaoyao powder (DZXY) in SD. Methods: Using RNA-sequencing, we identified differentially expressed genes (DEGs) in leukocytes of SD compared to healthy controls, deciphered their functions and pathways, and identified the hub genes of SD. We also assessed changes in leukocyte transcription factor activity in patients with SD using the TELiS platform. The Connectivity Map database was retrieved to screen candidate drugs for SD. Based on network pharmacology, we elucidated the “multi-component, multi-target, and multi-pathway” mechanism of DZXY in the treatment of SD. Results: We identified 1080 DEGs (padj <0.05 and |log2 (fold change)| ≥ 1 & protein coding) in the leukocytes of patients with SD. These DEGs, including hub genes, were primarily involved in immune and inflammatory response-related processes. Transcription factor activity analysis revealed similarities between the leukocyte transcriptome profile in SD and the conserved transcriptional response to adversities in immune cells. Connectivity Map analysis identified 28 potential drugs for SD treatment, particularly SB-202190 and TWS-119. Constructing the “Direct Compounds-Direct Targets-Pathways” network for DZXY and SD revealed the curative mechanisms of DZXY in SD, primarily including inflammatory response, lipid metabolism, immune response, and other processes. Conclusion These results provide new insights into the characteristics and functional changes of leukocytes in SD, partially illustrate the pathogenesis of SD, and suggest potential drugs for SD. The curative mechanisms of DZXY in SD are also partially elucidated.

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: 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.

OBJECTIVES: To analyze the gross pathological data of sudden cardiac death (SCD) with different causes, to provide data support for the identification of sudden cardiac death with unknown causes. METHODS: A total of 167 adult SCD cases in the archive of the Forensic Expertise Institute of Nanjing Medical University from 2010 to 2020 were collected. The gross pathological data of SCD cases were summarized and the characteristics of different causes of death were statistically analyzed. RESULTS: The ratio of male to female SCD cases was 3.4∶1. Coronary heart disease was the leading cause of SCD, and mainly distributed in people over 40 years old. SCD caused by myocarditis was mainly distributed in young people and the mean age of death was (34.00±9.55) years. By analyzing the differences in cardiac pathological parameters of SCD with different causes, it was found that the aortic valve circumference was significantly dilated in the SCD caused by aortic aneurysm or dissection (P<0.05). The heart weight of SCD caused by aortic aneurysm or dissection and combined factors was greater, and both pulmonary and tricuspid valvular rings were dilated in the SCD caused by combined factors in adult males (P<0.05). CONCLUSIONS Various gross pathological measures of SCD with different causes are different, which has reference value in the cause of death identification of SCD.
Humans ; Adult ; Male ; Female ; Adolescent ; Young Adult ; Death, Sudden, Cardiac/pathology* ; Coronary Disease ; Heart ; Forensic Medicine ; Autopsy

Objective To apply the best evidence of airway clearance for ICU patients and promote an application of the best evidence in clinical practice to promte nuring quality.Methods The best evidence of airway clearance for ICU patients was summarised.Based on the best evidence,a system of 11 review indicators was established for clinical baseline review in combination with clinical scenario analysis and professional judgment according to the principle of operability,measurability,and understandability.On the basis of the results of review and the analysis of obstacle factors,strategies of airway clearance for ICU patients were proposed and implemented in clinical practice.Between September and December 2022,72 hospitalised patients and 30 nursing staff in the ICU of a general hospital in Wuhan were recruited in the study.Between September and October 2022,routine nursing care for airway clearances was given to the patients,and evidence-based nursing care for airway clearance was offered to the ICU patients between November and December 2022.Clinical pulmonary infection score,nursing staff's knowledge of airway clearance and implementation rate of review indicators were compared before and after the application of evidence-based nursing.Results All of the patients went through the study.After the application of evidence-based nursing practice,the clinical pulmonary infection score was decreased from(4.94±1.66)to(4.14±1.68).The score of airway clearance knowledge was increased from(49.17±9.38)points to(82.17±10.56)points.The implementation rate of the 11 indicators of evidence-based practice before the evidence-based practice was 0～80.00%,and it was significantly improved up to 96.67%～100.00%after the evidence-based practice(all P<0.05).Conclusion Implementation of evidence-based nursing practice in the airway clearance for ICU patients can reduce clinical pulmonary infections in ICU patients,improve the knowledge of nurses in cognition of airway clearance hence to improve the quality of nursing and promote the recovery of patients.