Objective To investigate the efficacy and white matter integrity of low frequency repetitive transcranial magnetic stimulation (rTMS) and the modified electroconvulsive therapy (MECT) in patients with schizophrenia. Methods From May 2015 to October 2016,120 cases with schizophrenia were randomly enrolled into the MECT group and of the rTMS group. Patients in the MECT group were treated with the modified electric convulsive therapy for 8 times ,while patients in the rTMS group were treated with the repetitive transcranial magnetic stimulation for 12 times. PANSS were used to evalue the clinical effects. Repeatable battery for the assess-ment of neuropsychological status was used to assess the cognitive function. Treatment emergent side-effect scale was used to assess the adverse effects. Brain fractional anisotropy was used to assess white matter integrity. Results After treatment,the PANSS scores were significantly lowered,however,the RBANS scores were signifi-cantly higher in the MECT group and rTMS group than those before treatment ,with significant differences (P <0.05). No significant differences for the PANSS scores and the RBANS scores were observed between the two groups before and after treatment. There was no significant difference for the TESS scores between the two groups before treatment(P > 0.05). After treatment,the TESS scores in the MECT group were significantly higher than those in the rTMS group(P < 0.05). After treatment,the FA values of left anterior cingulate gyrus,left posterior cingulate gyrus ,left prefrontal cortex and genu of corpus callosum for both MECT group and rTMS group were significantly increased (P < 0.05 ,respectively). Compared with the MECT group ,the FA value significantly increased in the rTMS group after treatment (P < 0.05). Conclusions Both MECT and rTMS have significant clinical efficacies and can improve the cognitive function of schizophrenics. rTMS is more safe than MECT ,with a stronger effect on preventing the integrity of white matter than MECT.

Objective To explore the potential causal relationship between occupational pneumoconiosis (hereinafter referred to as "pneumoconiosis") and five mental disorders (depression, bipolar disorder, schizophrenia, insomnia and anxiety) using the two-sample Mendelian randomization (MR) method. Methods Single nucleotide polymorphisms (SNPs) loci associated with pneumoconiosis and five mental disorders were screened from Genome-Wide Association Studies. Inverse variance weighting (IVW), weighted median (WM) and MR-Egger regression methods were used to evaluate the significance of the causal relationship between pneumoconiosis and five mental disorders. Sensitivity analysis was used to evaluate the accuracy and reliability of the research results. Results After matching data of pneumoconiosis and the five mental disorders, 16 SNPs were ultimately included as instrumental variables in this study. The result of MR analysis revealed a positive causal relationship between pneumoconiosis and both depression [IVW: odds ratio (OR) and 95% confidence interval (CI) was 1.017 (1.000-1.035), P<0.05] and bipolar disorder [IVW: OR(95%CI)was 1.046(1.009-1.083), P<0.05; WM: OR (95%CI) was 1.055(1.007-1.105), P<0.05]. Result of sensitivity analysis indicated there was no heterogeneity and horizontal pleiotropy in the above results. There was no causal association observed between pneumoconiosis and schizophrenia, insomnia, or anxiety disorders (all P>0.05). Conclusion This study provides genetic evidence supporting a positive causal relationship between pneumoconiosis and both depression and bipolar disorder.

A non-invasive deep learning method is proposed for reconstructing arterial blood pressure signals from photoplethysmography signals.The method employs U-Net as a feature extractor,and a module referred to as bidirectional temporal processor is designed to extract time-dependent information on an individual model basis.The bidirectional temporal processor module utilizes a BiLSTM network to effectively analyze time series data in both forward and backward directions.Furthermore,a deep supervision approach which involves training the model to focus on various aspects of data features is adopted to enhance the accuracy of the predicted waveforms.The differences between actual and predicted values are 2.89±2.43,1.55±1.79 and 1.52±1.47 mmHg on systolic blood pressure,diastolic blood pressure and mean arterial pressure,respectively,suggesting the superiority of the proposed method over the existing techniques,and demonstrating its application potential.