BACKGROUND: Recent studies have provided compelling evidence that exposure to brominated flame retardants (BFRs) can adversely affect human health. We aim to explore the potential impact of BFRs on adiposity and central obesity. METHODS: Data from the National Health and Nutrition Examination Surveys (NHANES) cycles conducted between 2009 and 2014 was used to study the connections between variables. After filtering, we analyzed a sample of 4,110 adults aged 20 years and above. Our goal was to examine the potential association between BFRs and consequences and investigate the part played by oxidative stress and inflammatory markers as intermediaries. To achieve this, we used advanced statistical methods such as weighted quantile sum (WQS) regression, quantile-based g-computation (QGC), and the Bayesian kernel machine regression (BKMR). RESULTS: The findings showed that among the examined chemicals, exposure to PBDE85 (weight: 41%), PBDE100 (24%), and PBB153 (23%) may be the dominant contributors to general obesity risk. Upon controlling for all variables that could impact the results, it was found that the QGC outcomes indicated a positive correlation between exposure to mixtures of brominated flame retardants and the occurrence of abdominal obesity (OR = 1.187, 95% CI: 1.056-1.334, p = 0.004). Significant contributions were made by PBDE85 (52%), PBB153 (27%), and PBDE100 (21%). Mediation analysis shows that lymphatic cells (LC) and albumin (ALB) partially mediate the link between brominated flame retardants and obesity. The results of BKMR are generally consistent with those of WQS and QGC. CONCLUSION At a population level, our research has revealed a noteworthy correlation between BFRs and obesity. However, further investigation is required through prospective cohort studies and in-depth mechanistic exploratory studies.
Humans ; Flame Retardants/adverse effects* ; Oxidative Stress/drug effects* ; Adult ; Male ; Female ; Middle Aged ; Inflammation/epidemiology* ; Obesity/chemically induced* ; Biomarkers/blood* ; Nutrition Surveys ; Mediation Analysis ; Young Adult ; United States/epidemiology* ; Environmental Exposure/adverse effects* ; Aged ; Environmental Pollutants/adverse effects* ; Halogenated Diphenyl Ethers/adverse effects*

Research indicates that microbe activity within the human body significantly influences health by being closely linked to various diseases. Accurately predicting microbe-disease interactions (MDIs) offers critical insights for disease intervention and pharmaceutical research. Current advanced AI-based technologies automatically generate robust representations of microbes and diseases, enabling effective MDI predictions. However, these models continue to face significant challenges. A major issue is their reliance on complex feature extractors and classifiers, which substantially diminishes the models' generalizability. To address this, we introduce a novel graph autoencoder framework that utilizes decoupled representation learning and multi-scale information fusion strategies to efficiently infer potential MDIs. Initially, we randomly mask portions of the input microbe-disease graph based on Bernoulli distribution to boost self-supervised training and minimize noise-related performance degradation. Secondly, we employ decoupled representation learning technology, compelling the graph neural network (GNN) to independently learn the weights for each feature subspace, thus enhancing its expressive power. Finally, we implement multi-scale information fusion technology to amalgamate the multi-layer outputs of GNN, reducing information loss due to occlusion. Extensive experiments on public datasets demonstrate that our model significantly surpasses existing top MDI prediction models. This indicates that our model can accurately predict unknown MDIs and is likely to aid in disease discovery and precision pharmaceutical research. Code and data are accessible at: https://github.com/shmildsj/MDI-IFDRL.

Research indicates that microbe activity within the human body significantly influences health by being closely linked to various diseases.Accurately predicting microbe-disease interactions(MDIs)offers critical insights for disease intervention and pharmaceutical research.Current advanced AI-based technologies automatically generate robust representations of microbes and diseases,enabling effec-tive MDI predictions.However,these models continue to face significant challenges.A major issue is their reliance on complex feature extractors and classifiers,which substantially diminishes the models' generalizability.To address this,we introduce a novel graph autoencoder framework that utilizes decoupled representation learning and multi-scale information fusion strategies to efficiently infer po-tential MDIs.Initially,we randomly mask portions of the input microbe-disease graph based on Bernoulli distribution to boost self-supervised training and minimize noise-related performance degradation.Secondly,we employ decoupled representation learning technology,compelling the graph neural network(GNN)to independently learn the weights for each feature subspace,thus enhancing its expressive power.Finally,we implement multi-scale information fusion technology to amalgamate the multi-layer outputs of GNN,reducing information loss due to occlusion.Extensive experiments on public datasets demonstrate that our model significantly surpasses existing top MDI prediction models.This indicates that our model can accurately predict unknown MDIs and is likely to aid in disease discovery and precision pharmaceutical research.Code and data are accessible at:https://github.com/shmildsj/MDI-IFDRL.

Tourette syndrome(TS)is a chronic neurodevelopmental disorder.Acupuncture can effectively improve the clinical symptoms of TS patients.This article systematically summarized the clinical research status of acupuncture for the treatment of TS in recent years from the aspects of characteristic acupuncture methods,characteristic needles and comprehensive therapies,and put forward suggestions and prospects for systematically elaborating the peripheral-central mechanism of acupuncture for TS around the intestinal immunity and brain network mechanism in the future,so as to provide reference for optimizing clinical research and treatment.

Objective:To explore the genetic basis of eighteen patients with tetrahydrobiopterin deficiency (BH4D) from Gansu Province.Methods:Eighteen patients diagnosed with BH4D at Gansu Provincial Maternal and Child Health Care Hospital from January 2018 to December 2021 were selected as the study subjects. Whole exome sequencing was carried out, and candidate variants were verified by Sanger sequencing.Results:All of the thirty-six alleles of the eighteen patients were successfully determined by molecular genetic testing. Sixteen patients were found to harbor variants of the PTS gene, and two had harbored variants of the QDPR gene. Ten variants were detected in the PTS gene, with the most common ones being c. 259C>T (34.38%) and c. 286G>A (15.63%). Based on the guidelines from the American College of Medical Genetics and Genomics (ACMG), the c. 259C>T was classified as a pathogenic variant, whilst the c. 286G>A, c. 166G>A, c. 200C>T, c. 272A>G, c. 402A>C, c. 421G>T, c. 84-291A>G and c. 317C>T were classified as likely pathogenic variants. A novel c. 289_290insCTT variant was classified as likely pathogenic (PM1+ PM2_Supporting+ PM3+ PP3+ PP4). The two variants (c.478C>T and c. 665C>T) detected in the QDPR gene were both classified as variants of uncertain significance (PM1+ PM2_Supporting+ PP3+ PP4). Conclusion:Genetic testing has clarified the pathogenic variants in these BH4D patients, which has enabled timely and accurate clinical intervention and treatment, and provided a reference for genetic counseling and reproductive guidance for their families.

Objective To develop the'E-Bone',a comprehensive one-stop preoperative planning system for reverse total shoulder arthroplasty with improved accuracy and efficiency.Methods The nnU-net deep neural network was utilized for scapula segmentation to obtain precise scapula segmentation results.Based on the 3 key factors,namely bone density,upward and downward angle and nail length,the base was automatically positioned.The quantitative parameters required for surgical planning were calculated.A personalized guide plate was generated by combining glenoid morphology and base positioning information.The system interface was developed to modularize various functions for easy use,providing interactive operation and real-time display.Results Compared with the Mimics system,the'E-bone'preoperative planning system reduced complex manual adjustments during the planning process.The average planned nail length was longer than that of the Mimics system,and the planning time was reduced by 86%.The scapula segmentation accuracy of this system reached 99.93%,better than that of Mimics to achieve a higher precision.Conclusion The"E-bone"system provides a one-stop,efficient,and accurate preoperative planning system for reverse shoulder replacement and potentially broader clinical applications.

Objective To evaluate the effects of the multiple factors especially image geometric accuracy of the imaging system on the segmentations of target areas and organs-at-risk.Methods The study used phantoms to test the imaging performance of the 1.5T magnetic resonance(MR)linear accelerator system,including the assessments of MR image geometric distortion and the segmentation errors caused by factors such as image geometric distortion.Model 604-GS large field MR image distortion phantom was used to explore the geometric distortion of the MR images for MR-guided radiotherapy;and CIRS Model 008z upper abdominal phantom was used to analyze the segmentation errors of target areas and organs-at-risk.Results The average geometric distortion and maximum distortion of 3D T1WI-FFE images vs 3D T2WI-TSE images were 0.54 mm vs 0.53 mm and 1.96 mm vs 1.68 mm,respectively;and the control points of the large distortions were distributed at the edges of the phantom,which was consistent with the MR imaging characteristics previously reported.Compared with CT-based segmentation contour,the MDA was 1.17 mm and DSC was 0.91 for 3D T1WI-FFE,while MDA was 0.86 mm and DSC was 0.94 for 3D T2WI-TSE.Conclusion The study quantitatively assesses the geometric accuracy of the imaging system for MR-guided radiotherapy.The phantom-based contour analysis reveals that with CT image as gold standard,the segmentation error in MRI images meets the clinical requirements,and that 3D T2WI-TSE image is advantageous over 3D T1WI-FFE image in segmentation accuracy.

Objective To develop the'E-Bone',a comprehensive one-stop preoperative planning system for reverse total shoulder arthroplasty with improved accuracy and efficiency.Methods The nnU-net deep neural network was utilized for scapula segmentation to obtain precise scapula segmentation results.Based on the 3 key factors,namely bone density,upward and downward angle and nail length,the base was automatically positioned.The quantitative parameters required for surgical planning were calculated.A personalized guide plate was generated by combining glenoid morphology and base positioning information.The system interface was developed to modularize various functions for easy use,providing interactive operation and real-time display.Results Compared with the Mimics system,the'E-bone'preoperative planning system reduced complex manual adjustments during the planning process.The average planned nail length was longer than that of the Mimics system,and the planning time was reduced by 86%.The scapula segmentation accuracy of this system reached 99.93%,better than that of Mimics to achieve a higher precision.Conclusion The"E-bone"system provides a one-stop,efficient,and accurate preoperative planning system for reverse shoulder replacement and potentially broader clinical applications.

Objective To find out the advantages and disadvantages of medical quality and provide a scientific basis for more scientific and fine medical quality management by comprehensively evaluating the medical quality of surgical departments of 3 Grade class A hospital.Methods Through literature research and expert consulting,combined with the actual conditions of the hospital,the evaluation indicators were determined.Weights were objectively assigned to the evaluation indicators with the entropy weight method and the surgical medical quality was comprehensively evaluated with non-integer rank WRSR.Results Seen from medical service capacity,the entropy weights of RW ≥2 and Level-4 surgery are relatively big;seen from the efficiency of medical service,the entropy weights of the time consumption index and cost consumption index are relatively big;seen from the safety of medical services,the entropy weight of postoperative death in the perioperative period is the biggest.The results of grad-ing of medical quality evaluation of surgical departments indicated that joint surgery,spinal surgery,neurosurgery,and thoracic surgery were"good"departments;15 departments,such as obstetrics,traumatic orthopedics,ENT,and gynecology,were"gen-eral"departments;and cardiac surgery and stomatology were"poor"departments.Conclusion The medical quality of surgical departments was comprehensively evaluated with non-integer rank WRSR.The evaluation results were consistent with the actual conditions of the hospital.Surgical departments should take targeted measures and make rectifications according to the results of the evaluation;and the hospital should reasonably allocate medical resources and make related policies with evaluation results combined with the needs of the development of departments.

Objective To find out the advantages and disadvantages of medical quality and provide a scientific basis for more scientific and fine medical quality management by comprehensively evaluating the medical quality of surgical departments of 3 Grade class A hospital.Methods Through literature research and expert consulting,combined with the actual conditions of the hospital,the evaluation indicators were determined.Weights were objectively assigned to the evaluation indicators with the entropy weight method and the surgical medical quality was comprehensively evaluated with non-integer rank WRSR.Results Seen from medical service capacity,the entropy weights of RW ≥2 and Level-4 surgery are relatively big;seen from the efficiency of medical service,the entropy weights of the time consumption index and cost consumption index are relatively big;seen from the safety of medical services,the entropy weight of postoperative death in the perioperative period is the biggest.The results of grad-ing of medical quality evaluation of surgical departments indicated that joint surgery,spinal surgery,neurosurgery,and thoracic surgery were"good"departments;15 departments,such as obstetrics,traumatic orthopedics,ENT,and gynecology,were"gen-eral"departments;and cardiac surgery and stomatology were"poor"departments.Conclusion The medical quality of surgical departments was comprehensively evaluated with non-integer rank WRSR.The evaluation results were consistent with the actual conditions of the hospital.Surgical departments should take targeted measures and make rectifications according to the results of the evaluation;and the hospital should reasonably allocate medical resources and make related policies with evaluation results combined with the needs of the development of departments.