Network pharmacology and molecular docking were employed to predict the mechanism of Zhizhu Decoction in regulating macrophage polarization to reduce adipose tissue inflammation in obese children, and animal experiments were then carried out to validate the prediction results. The active ingredients and targets of Zhizhu Decoction were retrieved from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform(TCMSP). The inflammation related targets in the adipose tissue of obese children were searched against GeneCards, OMIM, and DisGeNET, and a drug-disease-target network was established. STRING was used to construct a protein-protein interaction(PPI) network and screen for core targets. R language was used to carry out Gene Ontology(GO) and Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway enrichment analyses. AutoDock was used for the molecular docking between core targets and active ingredients. 24 SPF grade 6-week C57B/6J male mice were adaptively fed for 1 week, and 8 mice were randomly selected as the blank group. The remaining 16 mice were fed with high-fat diet for 8 weeks to onstruct a high-fat diet induced mouse obesity model. After successful modeling, the 16 mice were randomly divided into model group and Zhizhu Decoction group, with 8 mice in each group. Zhizhu Decoction group was intervened by gavage for 14 days, once a day. Blank group and model group were given an equal amount of sterile double distilled water(ddH_2O) by gavage daily. After the last gavage, serum and inguinal adipose tissue were collected from mice for testing. The morphology of inguinal adipose tissue was observed by hematoxylin-eosin(HE) staining, the levels of inflammatory factors interleukin-6(IL-6) and tumor necrosis factor-α(TNF-α)were detected by enzyme-linked immunosorbent assay(ELISA), and the protein expression of macrophage marker molecule nitric oxide synthase(iNOS) and epidermal growth factor like hormone receptor 1(F4/80) was detected by immunofluorescence staining. Network pharmacology predicted luteolin, naringenin, and nobiletin as the main active ingredients in Zhizhu Decoction and 15 core targets. KEGG pathway enrichment analysis revealed involvement in the key signaling pathway of nuclear factor κB(NF-κB). Molecular docking showed that the active ingredients of Zhizhu Decoction bound well to the core targets. Animal experiment showed that compared with the model group, Zhizhu Decoction reduced the distribution of inflammatory cytokines in the inguinal adipose tissue of mice, lowered the levels of TNF-α and IL-6 in the serum(P<0.05, P<0.01), and down-regulated the expression of iNOS and F4/80(P<0.05). The results showed that the active ingredients in Zhizhu Decoction, such as luteolin, naringenin, and nobiletin, inhibit the aggregation of macrophages in adipose tissue, downregulate their classic activated macrophage(M1) polarization, reduce the expression of inflammatory factors IL-6 and TNF-α, and thus improve adipose tissue inflammation in obese mice.
Animals ; Drugs, Chinese Herbal/pharmacology* ; Molecular Docking Simulation ; Adipose Tissue/immunology* ; Mice ; Male ; Humans ; Network Pharmacology ; Macrophages/immunology* ; Mice, Inbred C57BL ; Child ; Protein Interaction Maps/drug effects* ; Obesity/genetics* ; Inflammation/drug therapy*

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.

Background/Aims: Four-week treatment of linvencorvir (RO7049389) was generally safe and well tolerated, and showed anti-viral activity in chronic hepatitis B (CHB) patients. This study evaluated the efficacy, safety, and pharmacokinetics of 48-week treatment with linvencorvir plus standard of care (SoC) in CHB patients. Methods: This was a multicentre, non-randomized, non-controlled, open-label phase 2 study enrolling three cohorts: nucleos(t)ide analogue (NUC)-suppressed patients received linvencorvir plus NUC (Cohort A, n=32); treatment-naïve patients received linvencorvir plus NUC without (Cohort B, n=10) or with (Cohort C, n=30) pegylated interferon-α (Peg-IFN-α). Treatment duration was 48 weeks, followed by NUC alone for 24 weeks. Results: 68 patients completed the study. No patient achieved functional cure (sustained HBsAg loss and unquantifiable HBV DNA). By Week 48, 89% of treatment-naïve patients (10/10 Cohort B; 24/28 Cohort C) reached unquantifiable HBV DNA. Unquantifiable HBV RNA was achieved in 92% of patients with quantifiable baseline HBV RNA (14/15 Cohort A, 8/8 Cohort B, 22/25 Cohort C) at Week 48 along with partially sustained HBV RNA responses in treatment-naïve patients during follow-up period. Pronounced reductions in HBeAg and HBcrAg were observed in treatment-naïve patients, while HBsAg decline was only observed in Cohort C. Most adverse events were grade 1–2, and no linvencorvir-related serious adverse events were reported. Conclusions 48-week linvencorvir plus SoC was generally safe and well tolerated, and resulted in potent HBV DNA and RNA suppression. However, 48-week linvencorvir plus NUC with or without Peg-IFN did not result in the achievement of functional cure in any patient.

Objective: Emerging evidence suggests that air pollution exposure may increase the risk of Parkinson’s disease (PD). We aimed to investigate the association between exposure to fine particulate matter (PM2.5) and the risk of incident PD nationwide. Methods: We utilized data from the Taiwan National Health Insurance Research Database, which is spatiotemporally linked with air quality data from the Taiwan Environmental Protection Administration website. The study population consisted of participants who were followed from the index date (January 1, 2005) until the occurrence of PD or the end of the study period (December 31, 2017). Participants who were diagnosed with PD before the index date were excluded. To evaluate the association between exposure to PM2.5 and incident PD risk, we employed Cox regression to estimate the hazard ratio and 95% confidence interval (CI). Results: A total of 454,583 participants were included, with a mean (standard deviation) age of 63.1 (9.9) years and a male proportion of 50%. Over a mean follow-up period of 11.1 (3.6) years, 4% of the participants (n = 18,862) developed PD. We observed a significant positive association between PM2.5 exposure and the risk of PD, with a hazard ratio of 1.22 (95% CI, 1.20–1.23) per interquartile range increase in exposure (10.17 μg/m3) when adjusting for both SO2 and NO2. Conclusion We provide further evidence of an association between PM2.5 exposure and the risk of PD. These findings underscore the urgent need for public health policies aimed at reducing ambient air pollution and its potential impact on PD.

Objective: To observe the association between clinical phenotypes of hypertrophic cardiomyopathy (HCM) patients and a rare calcium channel and regulatory gene variation (Ca²⁺ gene variation) and to compare clinical phenotypes of HCM patients with Ca²⁺ gene variation, a single sarcomere gene variation and without gene variation and to explore the influence of rare Ca²⁺ gene variation on the clinical phenotypes of HCM. Methods: Eight hundred forty-two non-related adult HCM patients diagnosed for the first time in Xijing Hospital from 2013 to 2019 were enrolled in this study. All patients underwent exon analyses of 96 hereditary cardiac disease-related genes. Patients with diabetes mellitus, coronary artery disease, post alcohol septal ablation or septal myectomy, and patients who carried sarcomere gene variation of uncertain significance or carried>1 sarcomere gene variation or carried>1 Ca²⁺ gene variation, with HCM pseudophenotype or carrier of ion channel gene variations other than Ca²⁺ based on the genetic test results were excluded. Patients were divided into gene negative group (no sarcomere or Ca²⁺ gene variants), sarcomere gene variation group (only 1 sarcomere gene variant) and Ca²⁺ gene variant group (only 1 Ca²⁺ gene variant). Baseline data, echocardiography and electrocardiogram data were collected for analysis. Results: A total of 346 patients were enrolled, including 170 patients without gene variation (gene negative group), 154 patients with a single sarcomere gene variation (sarcomere gene variation group) and 22 patients with a single rare Ca²⁺ gene variation (Ca²⁺ gene variation group). Compared with gene negative group, patients in Ca²⁺ gene variation group had higher blood pressure and higher percentage of family history of HCM and sudden cardiac death (P<0.05); echocardiographic results showed that patients in Ca²⁺ gene variation group had thicker ventricular septum ((23.5±5.8) mm vs. (22.3±5.7) mm, P<0.05); electrocardiographic results showed that patients in Ca²⁺ gene variation group had prolonged QT interval ((416.6±23.1) ms vs. (400.6±47.2) ms, P<0.05) and higher RV5+SV1 ((4.51±2.26) mv vs. (3.50±1.65) mv, P<0.05). Compared with sarcomere gene variation group, patients in Ca²⁺ gene variation group had later onset age and higher blood pressure (P<0.05); echocardiographic results showed that there was no significant difference in ventricular septal thickness between two groups; patients in Ca²⁺ gene variation group had lower percentage of left ventricular outflow tract pressure gradient>30 mmHg (1 mmHg=0.133 kPa, 22.8% vs. 48.1%, P<0.05) and the lower early diastolic peak velocity of the mitral valve inflow/early diastolic peak velocity of the mitral valve annulus (E/e') ratio ((13.0±2.5) vs. (15.9±4.2), P<0.05); patients in Ca²⁺ gene variation group had prolonged QT interval ((416.6±23.1) ms vs. (399.0±43.0) ms, P<0.05) and lower percentage of ST segment depression (9.1% vs. 40.3%, P<0.05). Conclusion: Compared with gene negative group, the clinical phenotype of HCM is more severe in patients with rare Ca²⁺ gene variation; compared with patients with sarcomere gene variation, the clinical phenotype of HCM is milder in patients with rare Ca²⁺ gene variation.
Humans ; Cardiac Surgical Procedures/methods* ; Cardiomyopathy, Hypertrophic/genetics* ; Echocardiography ; Electrocardiography ; Phenotype ; Sarcomeres/genetics* ; Adult

Objective: To study the non/hypo-response to hepatitis B vaccination in HIV-infected patients, identify the influencing factors and provide evidence for the development of hepatitis B prevention and control strategies and measures for special population. Methods: On the basis of the randomized controlled trial of 20 µg hepatitis B vaccine immunization at 0-1-6 month, 0-1-2-6 month and 60 µg hepatitis B vaccine immunization at 0-1-2-6 month, the HIV-infected patients who completed one-month follow-up after the full course vaccination were selected as study subjects. Quantification of antibody to hepatitis B surface antigen (anti-HBs) in serum samples was performed by using chemiluminescent microparticle immunoassay (CMIA) and demographic characteristics, disease history, HIV infection and treatment status of the study subjects were collected. Statistical analysis was conducted by χ² test, t test, unconditional logistic regression and interaction analyses. Results: The non/hypo-response rates to hepatitis B vaccination were 34.65% (35/101), 24.49% (24/98) and 10.99% (10/91) in 20 µg group at 0-1-6 month or 0-1-2-6 month and 60 µg group at 0-1-2-6 month (P<0.001), respectively. Logistic regression analysis showed that after controlling for confounding factors, the risk for non/hypo-response was 0.22 times higher in HIV-infected patients receiving 60 µg hepatitis B vaccine at 0-1-2-6 month than in patients receiving 20 µg hepatitis B vaccine at 0-1-6 month (95%CI: 0.10-0.50), the risk for non/hypo-response was higher in men than in women (OR=3.65, 95%CI: 1.88-7.07), and the risk for non/hypo-response was 2.64 times higher in those without hepatitis B vaccination history than in those with hepatitis B vaccination history (95%CI: 1.10-6.32). Moreover, there were multiplicative interactions between immunization schedule and gender (OR=2.49, 95%CI: 1.24-5.00). Conclusion: The non/hypo-response rate to hepatitis B vaccination was significantly lower in HIV-infected patients receiving 60 µg hepatitis B vaccine at 0-1-2-6 month than in those receiving 20 µg hepatitis B vaccine at 0-1-6 month and 0-1-2-6 month. Gender, vaccination schedule and history of hepatitis B vaccination were the influencing factors of the non/hypo-response to hepatitis B vaccination. There was a multiplicative interaction between vaccination schedule and gender, and men receiving 20 µg hepatitis B vaccines had a higher risk for non/hypo-response to hepatitis B vaccination.
Female ; Follow-Up Studies ; HIV Infections/immunology* ; Hepatitis B/prevention & control* ; Hepatitis B Antibodies ; Hepatitis B Surface Antigens ; Hepatitis B Vaccines/administration & dosage* ; Humans ; Immunization Schedule ; Male