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.

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 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.

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 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 investigate the method of simulating low-dose CT (LDCT) images using routine dose level scanning mode to generate LDCT images with correspondence to the routine dose CT (RDCT) images in the training sets for deep learning model, which would be used for LDCT noise reduction.Methods:The CT images reconstructed by different algorithms in Philips CT Big Core had different noise levels, where the noise was larger with iDose 4 algorithm and lower with IMR(knowledge-based iterative model reconstruction)algorithm. A new method of replacing LDCT image with noise equivalent reconstructed image was proposed. The uniform module of CTP712 was scanned with the exposure of 250 mAs for RDCT, 35 mAs for LDCT. The images were reconstructed using IMR algorithm for LDCT images and iDose 4 algorithm at multiple noise reduction levels for RDCT images, respectively. The noise distribution of each image set was analyzed to find the noise equivalent images of LDCT. Then, RDCT images, those selected images were used for training cycle-consistent adversarial networks (CycleGAN)model, and the noise reduction ability of the proposed method on real LDCT images of phantom was tested. Results:The RDCT images generated with iDose 4 level 1 could substitute the LDCT images reconstructed with IMR algorithm. The radiation dose was reduced by 86% in low dose scanning. Using CycleGAN model, the noise reduction degree was 45% for uniform module, and 50%, 13%, 7% for CIRS-SBRT 038 phantom in the specific regions of brain, spinal cord, bone, respectively. Conclusions:Equivalent noise level reconstructed images could potentially serve as the alternative of LDCT images for deep learning network training to avoid additional radiation dose. The generated CT images had substantially reduced noise relative to that of LDCT.

Objective:To investigate prognostic factors for acral lentiginous melanoma (ALM) , and to construct a nomogram to verify the predictive value of these factors.Methods:Clinical data on 1 573 patients with ALM were collected from the Surveillance, Epidemiology, and End Results (SEER) database of National Cancer Institute in United States between 2004 and 2015. Data about patients′ age, gender, ulcer status, SEER staging, surgical protocols, T-, N- and M-staging, overall survival rates and disease-specific survival rates were extracted. Chi-square test was used to analyze the correlation of clinical characteristics with overall survival rates and melanoma-specific survival rates, and univariate and multivariate Cox proportional hazards regression models were used to investigate prognostic factors and establish predictive models.Results:Among the 1 537 patients with ALM, 714 were males, 823 were females, 818 were under 64 years of age, and 1 363 were Caucasian. Skin lesions occurred on the lower limbs and buttocks in 1 205 cases, and 974 cases had ulcers; according to the SEER staging, non-spread localized skin lesions were observed in 1 048 cases. There were significant differences in the mortality rate among patients of different ages at diagnosis, different gender, with different ulcer status, surgical status, and at different SEER stages, T-stages, N-stages and M-stages (all P < 0.001) . Univariate and multivariate Cox regression analysis showed that age ≥ 65 years, male, ulcers and distant lymph node metastasis in the SEER staging were associated with increased risk of death in the patients (all P < 0.05) , and the mortality rate was significantly higher in the patients with T2-, T3- or T4-stage ALM than in those with T1-stage ALM (all P < 0.05) , and higher in the patients with N1-, N2- and N3-stage ALM than in those with N0-stage ALM (all P < 0.05) . Conclusion:Age, gender, ulcer status, SEER stage, T-stage and N-stage are independent prognostic factors for overall survival rates and disease-specific survival rates of ALM.

Objective:To analyze the occurrence of and risk factors for skin and mucosal infections after primary tumor resection in patients with paraneoplastic pemphigus, and to summarize relevant nursing experience.Methods:Clinical characteristics and postoperative skin and mucosal infections were retrospectively analyzed in patients with confirmed paraneoplastic pemphigus, who underwent primary tumor resection in Department of Dermatology, Peking University First Hospital between January 1999 and January 2021. Common infectious agents were analyzed, and infection-related risk factors were identified by logistic regression analysis.Results:A total of 44 patients with paraneoplastic pemphigus were included in this study, including 25 (56.8%) males and 19 (43.2%) females, and their age were 33.8 ± 15.4 years. Postoperatively, 21 (47.73%) patients developed skin and mucosal infections, and their postoperative hospital stay (median [ Q1, Q3]) was 38 (25, 60) days, which was significantly longer than that in patients without skin and mucosal infections (21 [12, 23] days, Z = -4.08, P < 0.001) . The most common pathogen was methicillin-resistant Staphylococcus aureus (15 cases, 34.09%) . High glucocorticoid dosage per kilogram of body weight ( OR = 1.21, 95% CI: 1.00 - 1.46, P = 0.047) and receiving assisted ventilation therapy ( OR = 9.20, 95% CI: 2.01 - 42.13, P = 0.004) were independent risk factors for postoperative skin and mucosal infections. After active treatment and care, 37 (84.1%) patients recovered well at discharge. Conclusion:Skin and mucosal infections are a common postoperative complication in patients with paraneoplastic pemphigus, and the pathogens are mostly drug-resistant bacteria, which can lead to prolonged hospital stay, so attention should be paid to postoperative skin care; high postoperative glucocorticoid dosage per body weight and respiratory support may be associated with postoperative skin and mucosal infections.