Objective Rapid eye movement sleep behavior disorder（RBD） is a common sleep disorder in the elderly， and this study aims to investigate the activity of cerebral cortex based on the changes in electroencephalography （EEG） power spectrum and the difference in approximate entropy during the ictal period of RBD. Methods A total of 35 patients with idiopathic RBD who received video polysomnography monitoring were enrolled as RBD group， and 25 normal volunteers matched for age were enrolled as control group.REM EEG results with fewer artifacts was selected for both groups， and the RBD group had an increase in mandibular electromyographic activity or dream-enacting behaviors. The leads containing artifacts were excluded， and finally O1 （or O2 alternative） leads with relatively little interference were selected. After pretreatment， Fourier transform was performed for EEG data from both groups to calculate the absolute power and relative power ratio of EEG in five different frequency bands， i.e.， δ （0.5-3 Hz）， θ （4-7 Hz）， α （8-13 Hz）， β （14-30 Hz）， and γ （30-35 Hz）. The normal distribution of power values in each frequency band was tested for both groups， and the t-test was used for comparison. Approximate entropy was calculated for EEG in both two groups， and the t-test was used for comparison. Results The θ band was the dominant frequency band of REM EEG in both the control group and the RBD group. Compared with the control group， the RBD group had a significant increase in the absolute power of fast-wave activity on REM α band and significant reductions in δ/α and θ/α relative power ratios. There was a significant difference in EEG ApEn value between the control group and the RBD group （P<0.05）， and the RBD group had a higher ApEn value during REM sleep than the NC group， with a significant difference in EEG ApEn value during the phase of dream-enacting behaviors. Conclusion In the RBD group， there are significant increases in the absolute power and nonlinear approximate entropy of fast-wave activity on REM α band during the ictal period of REM， which reflects the hyperactive functional changes of cerebral cortex during the ictal period of RBD， and the involvement of cerebral cortex in RBD neural pathway disorders is an important supplement to the current theory. Moderate inhibition of cerebral cortex hyperactivity is of great significance for the treatment of RBD.

Objective To explore the changes in cognitive neural networks in AD patients at differ-ent stages of disease progression to provide electrophysiological basis for the pathogenesis and early diagnosis and treatment of AD.Methods A total of 84 patients admitted to Department of Neurology of General Hospital of Tianjin Medical University from September 2018 to June 2022 were prospectively recruited,and divided into AD patients group(42 cases)and MCI group(42 cases)according to their disease severity.Another 42 healthy individuals who taking physical examination during the same period were subjected and served as HC group.Post-resting EEG data were analyzed with Matlab software,and coherence analysis was performed by self-programmed program.Univariate analysis of variance was used to compare the interhemispheric and intra-hemispheric coherence values of six frequency bands in the three groups.Results The cohe-rence values of the related electrode pairs in the anterior head(frontal region+anterior tem-poral region)were significantly lower in the AD group than in the HC group(P＜0.05),with the Alpha and Beta dominant bands and dominant hemispheres dominant,while the values of related electrode pairs in the posterior head(temporal region)were obviously higher in the AD group than the HC group(P＜0.05),with the Gamma band dominant.In all the frequency bands except the Alpha band,the AD group had higher coherence values in most of the electrode pairs than the HC group,and the Gamma band was the most extensive.In the MCI group,only the coherence value of the electrodes in the occipito-posterior temporal region(O2-T6)in the right Alpha band was higher than that in the HC group(P＜O.05).Except Gamma band,the coherence value of the related electrode pairs in the frontal region was notably lower in the AD group than the MCI group(P＜0.05),and the Alpha and Beta bands were dominant.The results of interhemispheric cohe-rence showed that most of the electrode pairs in the AD group had higher coherence values than the MCI group in all the frequency bands and the Gamma band(P＜0.05).Conclusion EEG coherence can provide electrophysiological basis for the pathogenesis and early diagnosis of AD.

Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by persistent inflammation and joint damage, accompanied by the accumulation of plasma cells, which contributes to its pathogenesis. Understanding the genetic alterations occurring during plasma cell differentiation in RA can deepen our comprehension of its pathogenesis and guide the development of targeted therapeutic interventions. Here, our study elucidates the intricate molecular mechanisms underlying plasma cell differentiation by demonstrating that PRDX1 interacts with DOK3 and modulates its degradation by the autophagy-lysosome pathway. This interaction results in the inhibition of plasma cell differentiation, thereby alleviating the progression of collagen-induced arthritis. Additionally, our investigation identifies Salvianolic acid B (SAB) as a potent small molecular glue-like compound that enhances the interaction between PRDX1 and DOK3, consequently impeding the progression of collagen-induced arthritis by inhibiting plasma cell differentiation. Collectively, these findings underscore the therapeutic potential of developing chemical stabilizers for the PRDX1-DOK3 complex in suppressing plasma cell differentiation for RA treatment and establish a theoretical basis for targeting PRDX1-protein interactions as specific therapeutic targets in various diseases.

Objective To explore the changes in cognitive neural networks in AD patients at differ-ent stages of disease progression to provide electrophysiological basis for the pathogenesis and early diagnosis and treatment of AD.Methods A total of 84 patients admitted to Department of Neurology of General Hospital of Tianjin Medical University from September 2018 to June 2022 were prospectively recruited,and divided into AD patients group(42 cases)and MCI group(42 cases)according to their disease severity.Another 42 healthy individuals who taking physical examination during the same period were subjected and served as HC group.Post-resting EEG data were analyzed with Matlab software,and coherence analysis was performed by self-programmed program.Univariate analysis of variance was used to compare the interhemispheric and intra-hemispheric coherence values of six frequency bands in the three groups.Results The cohe-rence values of the related electrode pairs in the anterior head(frontal region+anterior tem-poral region)were significantly lower in the AD group than in the HC group(P＜0.05),with the Alpha and Beta dominant bands and dominant hemispheres dominant,while the values of related electrode pairs in the posterior head(temporal region)were obviously higher in the AD group than the HC group(P＜0.05),with the Gamma band dominant.In all the frequency bands except the Alpha band,the AD group had higher coherence values in most of the electrode pairs than the HC group,and the Gamma band was the most extensive.In the MCI group,only the coherence value of the electrodes in the occipito-posterior temporal region(O2-T6)in the right Alpha band was higher than that in the HC group(P＜O.05).Except Gamma band,the coherence value of the related electrode pairs in the frontal region was notably lower in the AD group than the MCI group(P＜0.05),and the Alpha and Beta bands were dominant.The results of interhemispheric cohe-rence showed that most of the electrode pairs in the AD group had higher coherence values than the MCI group in all the frequency bands and the Gamma band(P＜0.05).Conclusion EEG coherence can provide electrophysiological basis for the pathogenesis and early diagnosis of AD.

Objective:To analyze the electroencephalogram (EEG) features of patients with Alzheimer’s disease (AD) and mild cognitive impairment (MCI), and to combine the characteristics for classification and prediction.Methods:One hundred and thirty-five patients attending the Department of Neurology at the General Hospital of Tianjin Medical University were enrolled, including 34 patients with AD, 67 patients with MCI, and 34 healthy control (HC). The electroencephalogram signals of these patients in the resting state were collected and preprocessed. Relative power spectral density features and sample entropy features on a multi-band scale were extracted to compare the whole-brain differences in electroencephalogram features among the 3 groups of subjects, and then subdivided into brain regions and individual leads for in-depth analysis. The above two features were fused to classify and predict AD, MCI, and HC by support vector machine (SVM).Results:The frontal regions had higher δ relative power spectral densities than the other regions, and the occipital and temporal regions showed relatively lower distributions. θ-Band relative power spectral densities had a more even distribution of sizes across brain regions. α-Band relative power spectral densities were concentrated in the occipital lobe, while β-band relative power spectral densities were mainly concentrated in the parietal and temporal lobes. Except for the central lobe, the δ-band relative power spectral densities of the AD group were higher than those of the MCI group ( P < 0.05) and HC group ( P < 0.01) in all brain regions and the whole brain. θ-band relative power spectral densities of the AD group were higher than those of the MCI gourp ( P < 0.001) and HC group ( P < 0.001) in the whole brain and in all brain regions. α-Band relative power spectral densities of the AD group were lower than those of the other groups only in the temporal lobe (all P < 0.05). The relative power spectral density of the β-band in the AD group was higher than that of the other groups in the whole brain and in all brain regions ( P < 0.05, 0.01, 0.001). The difference in the relative power spectral density of the δ-band in the C3 lead in the central lobe of the AD and HC groups was statistically significant ( P < 0.05). The relative power spectral density of the γ-band in the temporal lobe was higher than that in the other regions of the AD group, the MCI group, and the HC group. The relative power spectral density of the γ-band in the T3 lead in the AD group was significantly lower than that in the T4 lead. The average entropy of samples in the whole brain and in each brain region was lower than that in the HC group in the AD and MCI groups (all P < 0.05). The entropy of the samples at lead C3 in the AD group was lower than that in the MCI group ( P < 0.05). The differences between the relative power spectral density, sample entropy, and the actual data classification evaluation indexes (accuracy rate, precision rate, recall rate, and F1 score) that fused the two features, and the rearranged data were all statistically significant (all P < 0.001). When the relative power spectral density feature and the sample entropy feature were fused in the classification features, the best classification prediction was achieved, with an accuracy rate of 80%, a precision rate of 78%, a recall rate of 78%, and the F1 score of 79%. Conclusions:Relative power spectral density and sample entropy analysis can reveal the abnormalities of electroencephalogram activities of AD and MCI patients from different perspectives (linear and nonlinear), and the combination of these two features in classification prediction can improve the classification effect.

Objective:To analyze the characteristics of gamma oscillation in chronic insomnia patients with anxiety and depression, and to investigate its underlying neural mechanism.Methods:According to the anxiety and depression scores, the subjects with chronic insomnia who met the diagnostic criteria were divided into chronic insomnia with anxiety and depression group ( n=19) and chronic insomnia group ( n=13). Healthy subjects matched with age, gender, and educational background were selected as the normal control group ( n=16). The EEGs from the three groups under resting state and cognitive load state were collected.The relative gamma power was then calculated by fast Fourier transform.The spatial distribution pattern of the gamma oscillation in the three groups was analyzed.Spearman correlation analysis was employed to quantify the correlation between relative gamma powers and sleep scale, anxiety and depression scale scores. Results:In the resting state, the relative gamma power in the chronic insomnia with anxiety and depression, chronic insomnia and normal control group was 0.192 1±0.008 0, 0.210 3±0.009 6, 0.237 3±0.006 4, respectively.In the cognitive load state, the relative gamma power in the three groups increased compared with those in the resting state (0.220 7±0.008 1, 0.249 5±0.009 8, 0.267 7±0.007 2, respectively) (all P<0.05). In the resting state, the relative gamma power (F3, F4, C3, C4, P3, P4, O2, F8, T4) in the chronic insomnia with anxiety and depression group (0.179 9±0.009 7) and the chronic insomnia group (0.194 4±0.010 4) was lower than that in control (0.236 0±0.012 0, P<0.05). In the cognitive load state, the relative gamma power (F3, C3, C4, P3, P4, T4) in the chronic insomnia with anxiety and depression group (0.207 3±0.009 7) was lower than that in control (0.259 1±0.009 4)( P<0.05). There was a significant negative correlation between the relative gamma power in the nodes(F3, C3, P3)and the insomnia, anxiety and depression in the three groups(correlation coefficient r=-0.467--0.274, P<0.05). Conclusion:Chronic insomnia patients with anxiety and depression are often accompanied by cognitive dysfunction.The loss of gamma oscillation in left posterior, left central and left apex may be one of the potential neural mechanisms of cognitive dysfunction in chronic insomnia patients with anxiety and depression.

Objective:To develop an evaluation index system for dynamic adjustment effect of medical service prices in public hospitals, as a set of quantitative evaluation tools for management departments to keep track of the trend in time, implement dynamic monitoring and guide decision-making.Methods:Based on the evaluation system of price adjustment effect, through the importance assessment of expert consultation and multiple index percentile method, the scoring criteria were formulated and the empirical analysis was carried out.Results:The total scores of hospital A and hospital B were 71.31 and 77.94 respectively, classified as " average" . The evaluation could basically reflect the effect of dynamic adjustment of medical service price in public hospitals.Conclusions:The evaluation has the functions of displaying differences, witnessing achievements and tracing causes. It can be used to evaluate the effect of dynamic adjustment of regional prices, to assist the regulators to keep track of trends, monitor dynamically and guide decision-making in time, and be used by hospitals in self-evaluation to find problems, improve their own operation and promote the healthy development of hospitals.

Objective: To construct an index system for evaluating the effectiveness of dynamic pricing adjustment of medical services, for the purpose of providing a set of evaluation tools for price regulatory authorities to evaluate the effectiveness of pricing adjustment of medical services, to keep track of pricing trends, to implement dynamic monitoring and to guide decision-making. Methods: Oriented to public hospitals in Guangdong province, literature analysis and Delphi method were used to construct the index system for evaluating the effectiveness of dynamic adjustment of medical service price. Descriptive analysis, consistency test and index importance evaluation were applied to statistical analysis. Results: Thirty-two experts evaluated the importance of 41 alternative indicators. The index system for evaluating the effectiveness of dynamic adjustment of medical service price was finally constructed, including six structural indicators, six process indicators and six result indicators. Conclusions Experts are representative, authoritative and well-coordinated. The consultation results are reliable. The evaluation index system has high reliability and validity, and can be used to objectively evaluate the dynamic adjustment effect of medical service price.

Objective To study the characteristics of brain electrical signals in mild cognitive impairment (MCI) patients and healthy elderly volunteers in rest and task loading state.Methods Twenty MCI patients served as a MIC group and 22 sex-,age-and education-matched healthy volunteers served as a control group in this study.The patients were scored according to the MoCA,HADS,MMSE and CMS,their brain electrical signals in a different state were recorded with a digital EEG recording device,and their Lemple-Zie complexity (LZC) was computed on the MATLAB 7.04 platform and comparatively analyzed.Results The MQ value and MoCA,HADS,MMSE,CMS scores were significantly higher in control group than in MCI group (P＜0.01).The LZC value in left frontal region and left temporal region was significantly higher in control group than in MCI group in rest state (0.577±0.059 vs 0.545±0.067,0.576±0.061 vs 0.548±0.059,P＜0.05).The LZC value in right frontal region and right hemisphere was significantly lower in MCI group than in control group in brain electrical activity state (0.535±0.065 vs 0.559±0.033,0.541±0.064 vs 0.551±0.047,P＜0.05).The LZC value in left frontal region,left temporal region,left hemisphere was lower in control group than in MCI group (0.523±0.040 vs 0.547±0.070,0.547±0.048 vs 0.561±0.092,0.542±0.044 vs 0.557±0.067,P＜0.05).Conclusion The LZC value is lost in the left hemisphere but its functional compensation is present in the right and Left parietal lobe,left frontal lobe and right brain areas in MCI patients in a non-linear manner.