Background Work-related musculoskeletal disorders (WMSDs) are a major occupational health concern, particularly among workers exposed to adverse ergonomic conditions. Manganese production involves heavy physical demands, yet research on WMSDs among manganese workers remains limited. Objective To investigate the prevalence and influencing factors of WMSDs among manganese workers in a manganese enterprise in Guangxi. Methods A cross-sectional survey was conducted from May to June 2024 on workers at a manganese factory in Guangxi. The Chinese Musculoskeletal Disorders Questionnaire was used to collect information on demographic characteristics, distribution of musculoskeletal symptoms, and work-related exposures. χ² test was applied to compare differences in positive WMSDs rates across groups, and logistic regression analysis was performed to identify associated factors. Results A total of 1476 workers were enrolled in the study after pre-determined inclusion and exclusion criteria. The overall prevalence of WMSDs was 34.15%. The most commonly affected body regions were the lower back (17.28%), neck (16.67%), and shoulders (13.82%). The results of logistic regression analysis indicated that female, older age, and education level of college or above were associated with a higher risk of WMSDs (P<0.05). Awkward working postures were significantly associated with WMSDs in corresponding body regions; in particular, awkward postures of the neck, upper limbs, trunk, and lower limbs were related to an increased risk of WMSDs in multiple body sites (P<0.05). In addition, poor lighting conditions, high workplace temperature, frequent or sustained arm support during work, and high job demands were associated with an increased risk of overall or site-specific WMSDs (P<0.05). Conclusion The high prevalence of WMSDs among manganese workers is closely associated with demographic characteristics, working postures, and work environment and organizational factors. Targeted ergonomic interventions focusing on high-risk body regions and key ergonomic exposures are warranted to reduce the risk of WMSDs among manganese workers.

Schizophrenia (SZ) stands as a severe psychiatric disorder. This study applied diffusion tensor imaging (DTI) data in conjunction with graph neural networks to distinguish SZ patients from normal controls (NCs) and showcases the superior performance of a graph neural network integrating combined fractional anisotropy and fiber number brain network features, achieving an accuracy of 73.79% in distinguishing SZ patients from NCs. Beyond mere discrimination, our study delved deeper into the advantages of utilizing white matter brain network features for identifying SZ patients through interpretable model analysis and gene expression analysis. These analyses uncovered intricate interrelationships between brain imaging markers and genetic biomarkers, providing novel insights into the neuropathological basis of SZ. In summary, our findings underscore the potential of graph neural networks applied to multimodal DTI data for enhancing SZ detection through an integrated analysis of neuroimaging and genetic features.
Humans ; Schizophrenia/pathology* ; Diffusion Tensor Imaging/methods* ; Male ; Female ; Adult ; Brain/metabolism* ; Young Adult ; Middle Aged ; White Matter/pathology* ; Gene Expression ; Nerve Net/diagnostic imaging* ; Graph Neural Networks

The rich club, as a community of highly interconnected nodes, serves as the topological center of the network. However, the similarities and differences in how the rich club supports functional integration and segregation in the brain across different species remain unknown. In this study, we first detected and validated the rich club in the structural networks of mouse, monkey, and human brains using neuronal tracing or diffusion magnetic resonance imaging data. Further, we assessed the role of rich clubs in functional integration, segregation, and balance using quantitative metrics. Our results indicate that the presence of a rich club facilitates whole-brain functional integration in all three species, with the functional networks of higher species exhibiting greater integration. These findings are expected to help to understand the relationship between brain structure and function from the perspective of brain evolution.
Animals ; Humans ; Brain/diagnostic imaging* ; Mice ; Male ; Nerve Net/diagnostic imaging* ; Macaca ; Female ; Neural Pathways/diagnostic imaging* ; Magnetic Resonance Imaging ; Biological Evolution ; Adult ; Diffusion Magnetic Resonance Imaging ; Brain Mapping ; Species Specificity ; Mice, Inbred C57BL

Objective:To establish a method for the rapid detection of varicella-zoster virus (VZV) by recombinase-aid amplification (RAA) combined with Clustered regularly interspaced short palindromic repeats (CRISPR)/Cas12a system.Methods:Clinical samples of suspected herpes zoster in Shandong province and Shanghai from 2023 to 2024 were collected, nucleic acids of positive samples were extracted, RAA-specific primers and crRNA (CRISPR RNA, crRNA) were designed for the conserved region of VZV, and the fluorescence intensity generated by Cas12a non-specific cleavage of single-stranded fluorescent probes was used to screen highly sensitive crRNAs and optimize the concentrations of crRNA, Cas12a and ssDNA probes. The sensitivity and reproducibility of the RAA-CRISPR/Cas12a detection method were evaluated by using synthesized plasmids and clinical samples, and the specificity of the method was evaluated by using other viral nucleic acids. The method was used to detect clinical samples by using the method and quantitative real-time PCR (qPCR) method, and the detection rate and consistency of the two method were compared.Results:The highly sensitive crRNA-4 was screened from the four crRNAs designed, and a VZV detection method for RAA-CRISPR/CAS12a based on fluorescence intensity measurement was established, which could be detected at 37℃ in 45 min, and the sensitivity of the detection could reach 10 copies/μL, a minimum clinical sample with a Ct value of 38.980 can be detected. It has high specificity and no cross-reactivity with Adenovirus 7, Herpes simplex virus type I, Herpes simplex virus type II, Coxsackieviruses A16, Cytomegalovirus, Epstein-Barr virus, Measles virus, Mumps virus, Enterovirus 71, Japanese encephalitis virus genotype 5. It has good stability, and can be successfully detected in low, medium and high concentrations of viral positive plasmids with good consistency. The detection rate of the clinically positive samples was 100%, which was completely consistent with the qPCR test result.Conclusions:RAA isothermal amplification technology combined with CRISPR-CAS12a technology was used to establish an accurate method for the detection of VZV virus, which was highly sensitive, specific, and had low requirements for experimental conditions, and could be completed within 45 min, which could provide strong technical support for the early detection of VZV.

Objective:To establish a method for the rapid detection of varicella-zoster virus (VZV) by recombinase-aid amplification (RAA) combined with Clustered regularly interspaced short palindromic repeats (CRISPR)/Cas12a system.Methods:Clinical samples of suspected herpes zoster in Shandong province and Shanghai from 2023 to 2024 were collected, nucleic acids of positive samples were extracted, RAA-specific primers and crRNA (CRISPR RNA, crRNA) were designed for the conserved region of VZV, and the fluorescence intensity generated by Cas12a non-specific cleavage of single-stranded fluorescent probes was used to screen highly sensitive crRNAs and optimize the concentrations of crRNA, Cas12a and ssDNA probes. The sensitivity and reproducibility of the RAA-CRISPR/Cas12a detection method were evaluated by using synthesized plasmids and clinical samples, and the specificity of the method was evaluated by using other viral nucleic acids. The method was used to detect clinical samples by using the method and quantitative real-time PCR (qPCR) method, and the detection rate and consistency of the two method were compared.Results:The highly sensitive crRNA-4 was screened from the four crRNAs designed, and a VZV detection method for RAA-CRISPR/CAS12a based on fluorescence intensity measurement was established, which could be detected at 37℃ in 45 min, and the sensitivity of the detection could reach 10 copies/μL, a minimum clinical sample with a Ct value of 38.980 can be detected. It has high specificity and no cross-reactivity with Adenovirus 7, Herpes simplex virus type I, Herpes simplex virus type II, Coxsackieviruses A16, Cytomegalovirus, Epstein-Barr virus, Measles virus, Mumps virus, Enterovirus 71, Japanese encephalitis virus genotype 5. It has good stability, and can be successfully detected in low, medium and high concentrations of viral positive plasmids with good consistency. The detection rate of the clinically positive samples was 100%, which was completely consistent with the qPCR test result.Conclusions:RAA isothermal amplification technology combined with CRISPR-CAS12a technology was used to establish an accurate method for the detection of VZV virus, which was highly sensitive, specific, and had low requirements for experimental conditions, and could be completed within 45 min, which could provide strong technical support for the early detection of VZV.

Objective　To establish a dual droplet digital PCR (ddPCR) assay for herpes simplex virus type I (HSV-1) and varicella-zoster virus (VZV). Methods The specific primers and probes were derived based on the conserved regions of HSV-1 and VZV genome. The primer-probe combinations were screened, and the annealing temperatures and primer-probe concentration ratios of the dual-droplet digital PCR reaction were optimized to establish a dual-droplet digital PCR reaction system for HSV-1 and VZV, which was tested for other viruses and validated for clinical samples. The sensitivity, specificity, and reproducibility of the established dual microtiter digital PCR method were analyzed. Results The optimal concentrations of primers and probes for the dual ddPCR detection method of HSV-I and VZV were determined to be 800 nmol/L and 250 nmol/L, respectively, with an optimal annealing temperature of 56 ℃. The correlation coefficient (R2) of the standard curve of the dual ddPCR assay was 0.99, showing a clear linear relationship. The method showed high sensitivity, with the lowest detection limit of herpes simplex virus type I being 2.97 copies/μL, and for VZV being 2.73 copies/μL. The repeatability was high with a small coefficient of variation and stable detection results; the specificity was excellent, and no cross-reaction was found with herpes simplex virus type Ⅱ, Epstein-Barr virus, Adenovirus, Coxsackievirus (CA6/CA10/CA16), Cytomegalovirus, Human Cytomegalovirus, Human enterovirus 71, Japanese Encephalitis virus, West Nile virus, Measles virus, Mumps virus, and human nucleic acids. Conclusions The dual droplet digital PCR assay for herpes simplex virus type I and varicella-zoster virus established in this experiment has strong sensitivity, specificity, and high repeatability, and can provide a solution for rapid quantitative detection of the two viruses in different scenarios.

Objective To explore the value of artificial intelligence federated learning system based on chest X-ray films for pathogen diagnosis of community-acquired pneumonia(CAP)in children.Methods Totally 900 cases of CAP children from 2 hospitals were retrospectively enrolled,including bacterial,viral and mycoplasma CAP(each n=300),and chest posteroanterior X-ray films were collected.Meanwhile,chest posteroanterior X-ray films of 5856 children from the publicly available dataset GWCMCx were collected,including 4273 CAP images and 1583 healthy chest images.All above 6756 images were divided into training set(n=5359)and validation set(n=1397)at the ratio of 8∶2.Then a pathogen diagnosis model of children CAP was established based on attention mechanism.Binary and ternary diagnostic algorithms were designed,and federated deployment training was performed.The efficacy of this system for pathogen diagnosis of children CAP was analyzed and compared with DenseNet model.Results Based on all data,the accuracy of the obtained artificial intelligence federated learning system model for diagnosing children CAP was 97.00%,with the area under the curve(AUC)of 0.990.Based on hospital data,the AUC of this system using single imaging data and clinical-imaging data for pathogen diagnosis of children CAP was 0.858 and 0.836,respectively,both better than that of DenseNet model(0.740,both P＜0.05).Conclusion The artificial intelligence federated learning system based on chest X-ray films could be used for pathogen diagnosis of children CAP.

Objective To observe the alterations in functional complexity of brain regions in autism spectrum disorder(ASD)patients and correlations with the predicted brain age.Methods Open brain resting-state functional MRI(rs-MRI)data of 93 ASD patients and 96 typically developing adolescents(healthy subjects)were downloaded.The functional complexity in brain regions were extracted with self-developed virtual digital brain software,and the alterations in functional complexity of brain regions in ASD patients and correlations with their ages were analyzed.Two networks were prospectively trained with data of 65 ASD patients and 67 healthy subjects as the training set to predict brain age,and the results were evaluated,and the predicting errors were compared using test set,i.e.the other 28 ASD patients and 29 healthy subjects.Results Compared to healthy subjects,on the basis of anatomical automatic labeling(AAL)atlas,ASD patients exhibited significantly reduced functional complexity based on Shannon entropy in the left precuneus,left cuneus and right parahippocampal gyrus.Conversely,functional complexity of ASD patients based on permutation entropy significantly increased in the left cuneus and right cerebellar Crus Ⅱ region.The left hippocampus showed reduced functional complexity based on Pearson correlation coefficient,while the left middle temporal gyrus showed increased functional complexity based on Pearson correlation coefficient.The functional complexity in brain regions of ASD patients were not closely correlated with ages(all|r|＜0.4).According to the trained fully connected network,the predicted brain ages of ASD patients and healthy subjects in test set were all lower than their physiological ages,but no significant difference was found between the prediction errors of ASD patients and healthy subjects(P=0.283).Conclusion Functional complexity changed in some brain region functions in ASD patients.The predicted brain ages of ASD patients based on the obtained fully connected network were on the low side,but not obviously affected by the alterations of functional complexity in brain regions.

Humans ; Artificial Intelligence ; Algorithms ; Brain ; Head

Alzheimer's disease (AD) is associated with the impairment of white matter (WM) tracts. The current study aimed to verify the utility of WM as the neuroimaging marker of AD with multisite diffusion tensor imaging datasets [321 patients with AD, 265 patients with mild cognitive impairment (MCI), 279 normal controls (NC)], a unified pipeline, and independent site cross-validation. Automated fiber quantification was used to extract diffusion profiles along tracts. Random-effects meta-analyses showed a reproducible degeneration pattern in which fractional anisotropy significantly decreased in the AD and MCI groups compared with NC. Machine learning models using tract-based features showed good generalizability among independent site cross-validation. The diffusion metrics of the altered regions and the AD probability predicted by the models were highly correlated with cognitive ability in the AD and MCI groups. We highlighted the reproducibility and generalizability of the degeneration pattern of WM tracts in AD.
Humans ; White Matter/diagnostic imaging* ; Diffusion Tensor Imaging/methods* ; Alzheimer Disease/complications* ; Reproducibility of Results ; Cognition ; Cognitive Dysfunction/complications* ; Brain/diagnostic imaging*