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.

Closed-loop neuromodulation, especially using the phase of the electroencephalography (EEG) rhythm to assess the real-time brain state and optimize the brain stimulation process, is becoming a hot research topic. Because the EEG signal is non-stationary, the commonly used EEG phase-based prediction methods have large variances, which may reduce the accuracy of the phase prediction. In this study, we proposed a machine learning-based EEG phase prediction network, which we call EEG phase prediction network (EPN), to capture the overall rhythm distribution pattern of subjects and map the instantaneous phase directly from the narrow-band EEG data. We verified the performance of EPN on pre-recorded data, simulated EEG data, and a real-time experiment. Compared with widely used state-of-the-art models (optimized multi-layer filter architecture, auto-regress, and educated temporal prediction), EPN achieved the lowest variance and the greatest accuracy. Thus, the EPN model will provide broader applications for EEG phase-based closed-loop neuromodulation.
Humans ; Electroencephalography/methods* ; Brain/physiology* ; Machine Learning ; Signal Processing, Computer-Assisted ; Male ; Adult ; Neural Networks, Computer ; Brain Waves/physiology*

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 observe the drainage pathways of interstitial fluid(ISF)and substance clearance pattern in rat brain with fluorescence tracing imaging and treacer-based MRI.Methods Thirty-three male SD rats were randomly divided into fluorescence tracing group(F group,n=18)and treacer-based MRI group(MRI group,n=15),then further divided into thalamic,hippocampal and caudate nucleus subgroups,respectively.Evans blue was injected to rats in F group,and cardiac perfusion was performed after injection,then brain tissue was harvested,and frozen sections were made to observe the drainage pathways of IFS in different subgroups.MRI was performed on rats in MRI group before and after injection of gadolinium-diethylenetriamine pentaacetic acid(Gd-DTPA)to observe signal intensity in ROI of brain regions in different subgroups,the signal unit ratio was calculated,and the changing trend was explored.Results ISF in thalamus,hippocampus and caudate nucleus had different dominant drainage pathways,and the time of tracer reached to adjacent brain regions and whole brain in F group were different.In MRI group,within 4 h after injection of Gd-DTPA,there were differences in direction and clearance rate among tracer in thalamus,hippocampus and caudate nucleus,mainly manifesting as the tracer in thalamus and hippocampus drained to the ipsilateral cortex and lateral ventricle,while the tracer in the caudate nucleus diffused to the cortex and midbrain,and there were differences of the peak time of tracer signal among adjacent drainage brain regions.Conclusion Fluorescence and MR dual-mode imaging showed that there were differences in the dominant drainage pathways of IFS and clearance rates of small molecule substances among hypothalamus,hippocampus and caudate nucleus of rats.

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.

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 determine the radioactive enrichment level in marine biological media around Qinshan Nuclear Power Plant and to evaluate the impact of effluent on marine biological media in the surrounding sea area based on the discharge of effluent from the nuclear power plant over the years and the monitoring level of surrounding marine media. Methods The monitoring data of ³H, ¹⁴C, ⁹⁰Sr, ¹³⁷Cs, and ^110mAg in the environmental media monitored in Qinshan Nuclear Power Plant from 2008 to 2017 were collected, and the data were processed and analyzed by IBM SPSS Statistics 26. Results The activity concentrations of tritium in mullet and oyster in the sea area around Qinshan Nuclear Power Plant were 0.5-2.9 Bq/kg and 0.55-8.2 Bq/kg, respectively, and the activity concentrations of tritium in both increased generally under the influence of effluent from the nuclear power plant. The activity concentrations of ¹⁴C, ¹³⁷Cs, and ⁹⁰Sr in the monitored organisms were lower than the background values. The results of correlation analysis showed that the effluent from the nuclear power plant had almost no effect on ¹⁴C. The activity level of ^110mAg decreased significantly year by year in oyster, and remained below the background level in mullet. Conclusion Qinshan Nuclear Power Plant is the longest operating nuclear power plant in mainland China. Most of the nuclides in biological media in the surrounding sea area are lower than the background levels, and the effluents from the nuclear power plant have little influence on marine biological media in the surrounding sea area.

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*

OBJECTIVE: To study the difference in age estimation based on quantitative analysis of DNA methylation by MassARRAY and pyrosequencing techniques. METHODS: The methylation levels of 9 CpG sites from two independent whole blood sample sets (containing 65 and 62 samples) were detected using MassARRAY and pyrosequencing techniques. Z-score transformation was used to remove the batch effects of different techniques, and a linear regression model was used for age prediction. RESULTS: For age prediction using the MassARRAY system, the 65 samples showed a mean absolute difference (MAD) of 2.49 years before Z-score transformation of the data and 2.44 years after the transformation, similar to the results in the 62 samples (MAD of 3.36 years before and 3.42 years after Z-score transformation). For data typed from pyrosequencing, the 65 samples showed a MAD of 4.20 years before and 2.76 years after data Z-score transformation, also similar to the results in the 62 samples (MAD of 3.92 years before and 3.63 years after data transformation). CONCLUSIONS Z-score transformation can effectively reduce the system batch effect between MassARRAY and pyrosequencing. Data from the MassARRAY system allows direct age estimation without further data processing, while the pyrosequencing data may increase the error in age estimation, which can be corrected by Z-score transformation of the data.
CpG Islands/genetics* ; DNA Methylation ; High-Throughput Nucleotide Sequencing ; Linear Models ; Sequence Analysis, DNA