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*

Accurate analysis of metal nanoparticles(MNPs)in sediments is a prerequisite for assessing the ecological risks of MNPs in aquatic environmental sediments.In this study,an analytical method for quantitative detection of concentration and particle size distribution of silver-containing nanoparticles(Ag-NPs),zinc-containing nanoparticles(Zn-NPs),cerium-containing nanoparticles(Ce-NPs),and titanium-containing nanoparticles(Ti-NPs)in sediments was established based on ultrasonic extraction-single particle inductively coupled plasma mass spectrometry(SP-ICP-MS).The effects of sample preparation conditions such as extraction solvent type,solid-liquid ratio,ultrasonic time,and settling time on the recovery of MNPs were investigated.The results showed that the extraction of MNPs from sediment by distilled water could effectively eliminate the high background signal interference introduced by the extractant under the conditions of solid-liquid ratio of 1∶400(g∶mL),ultrasonic extraction time of 1 h and settling time of 3 h.The detection limits for particle size of Ag-NPs,Zn-NPs,Ce-NPs and Ti-NPs in sediments were 31,35,26 and 85 nm,respectively,while the detection limits of particle concentrations were 1.21×104,1.90×104,5.26×107 and 1.48×107 particles/g,respectively.The spiking recoveries of Ag-NPs,Zn-NPs,Ce-NPs and Ti-NPs in sediments were 62.1％-108.7％,with relative standard deviations below 10％.This method could rapidly,accurately and simultaneously determine the concentration and particle size distribution of various MNPs in sediments,and was successfully applied to analysis of Ag-NPs,Zn-NPs,Ce-NPs,and Ti-NPs in authentic marine sediments.

Objective To investigate the postoperative efficacy of laparoscopic hiatal hernia repair(LHHR)combined with Toupet or Dor fundoplication for the treatment of esophageal hiatal hernia(HH).Methods A retrospective analysis was conducted on the case data of HH patients who underwent LHHR combined with Toupet(Toupet group,n=53)and Dor(Dor group,n=53)fundoplication between December 2018 and December 2022 in Department of General Surgery of Gansu Provincial Hospital.Intraoperative and postoperative recovery outcomes of both groups were observed.We analyzed and compared the incidence of dysphagia and gastroesophageal reflux disease questionnaire(GERD-Q)scores at preoperative and postoperative intervals of 1 month,6 months,and 1 year.The incidence of postoperative complications and the 1-year recurrence rate were compared between the two groups.Additionally,factors influencing postoperative dysphagia within the first month were examined.Results The differences between the two groups in operation time,intraoperative bleeding,postoperative ventilation time,postoperative extubation time and hospitalization time were not statistically significant(P>0.05).There was no significant difference in the incidence of immediate postoperative dysphagia in two groups(P>0.05).Furthermore,the differences between the two groups in the incidence of postoperative complications,such as bloating,abdominal pain and diarrhea,were not statistically significant(P>0.05).The incidence of dysphagia in Toupet group was higher than that in Dor group at 1 month postoperatively,and the difference was statistically significant(P=0.017);but the difference in the incidence of dysphagia between the two groups at 6 months and 1 year postoperatively was not statistically significant(P=0.767,1.000).The results of binary logistic regression analysis showed that both surgical procedure(OR=2.613,95%CI 1.141-5.983,P=0.023)and esophageal contractile reserve function(OR=2.921,95%CI 1.203-7.095,P=0.018)were independent risk factors for the incidence of dysphagia in patients with HH at 1 month after surgery.Compared with the preoperative period,the GERD-Q symptom scores were lower in both groups at 1 month,6 months,and 1 year postoperatively,and the difference was statistically significant(P<0.05);but there was no statistically significant difference between the groups at the same time point(Fintergroup=0.334,P=0.565).The difference between the two groups in 1-year postoperative recurrence rates was not statistically significant(P>0.05).Conclusions LHHR combined with Toupet or Dor fundoplication are both safe and effective surgical procedures for the treatment of HH,with excellent reflux control,fewer complications and lower recurrence rates,but Toupet fundoplication is more likely to have postoperative short-term dysphagia than Dor fundoplication.

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.