Resting-state functional connectivity of the cerebellum-cerebrum in older women with depressive symptoms.

BACKGROUND: Although there has been much neurobiological research on major depressive disorder, research on the neurological function of depressive symptoms (DS) or subclinical depression is still scarce, especially in older women with DS. OBJECTIVES: Resting-state functional magnetic resonance imaging (rs-fMRI) was used to compare functional connectivity (FC) between the cerebellum and cerebral in older women with DS and normal controls (NC), to explore unique changes in cerebellar FC in older women with DS. METHODS: In all, 16 older women with DS and 17 NC were recruited. All subjects completed rs-fMRI. The 26 sub-regions of the cerebellum divided by the AAL3 map were used as regions of interest (ROI) to analyze the difference in FC strength of cerebellar seeds from other cerebral regions between the two groups. Finally, partial correlation analysis between abnormal FC strength and Geriatric Depression Scale (GDS) score and Reminiscence Functions Scale (RFS) score in the DS group. RESULTS: Compared with NC group, the DS group showed significantly reduced FC between Crus I, II and the left frontoparietal region, and reduced FC between Crus I and the left temporal gyrus. Reduced FC between right insula (INS), right rolandic operculum (ROL), right precentral gyrus (PreCG) and the Lobule IX, X. Moreover, the negative FC between Crus I, II, Lobule IX and visual regions was reduced in the DS group. The DS group correlation analysis showed a positive correlation between the left Crus I and the right cuneus (CUN) FC and GDS. In addition, the abnormal FC strength correlated with the scores in different dimensions of the RFS, such as the negative FC between the Crus I and the left middle temporal gyrus (MTG) was positively associated with intimacy maintenance, and so on. CONCLUSION: Older women with DS have anomalous FC between the cerebellum and several regions of the cerebrum, which may be related to the neuropathophysiological mechanism of DS in the DS group.

Meta-analysis of the relationship between bullying and depressive symptoms in children and adolescents.

Childhood and adolescence are critical periods for physical and mental development; thus, they are high-risk periods for the occurrence of mental disorders. The purpose of this study was to systematically evaluate the association between bullying and depressive symptoms in children and adolescents. We searched the PubMed, MEDLINE and other databases to identify studies related to bullying behavior and depressive symptoms in children and adolescents. A total of 31 studies were included, with a total sample size of 133,688 people. The results of the meta-analysis showed that the risk of depression in children and adolescents who were bullied was 2.77 times higher than that of those who were not bullied; the risk of depression in bullying individuals was 1.73 times higher than that in nonbullying individuals; and the risk of depression in individuals who bullied and experienced bullying was 3.19 times higher than that in nonbullying-bullied individuals. This study confirmed that depression in children and adolescents was significantly associated with being bullied, bullying, and bullying-bullied behavior. However, these findings are limited by the quantity and quality of the included studies and need to be confirmed by future studies.

A fused biometrics information graph convolutional neural network for effective classification of patellofemoral pain syndrome.

Patellofemoral pain syndrome (PFPS) is a common, yet misunderstood, knee pathology. Early accurate diagnosis can help avoid the deterioration of the disease. However, the existing intelligent auxiliary diagnosis methods of PFPS mainly focused on the biosignal of individuals but neglected the common biometrics of patients. In this paper, we propose a PFPS classification method based on the fused biometrics information Graph Convolution Neural Networks (FBI-GCN) which focuses on both the biosignal information of individuals and the common characteristics of patients. The method first constructs a graph which uses each subject as a node and fuses the biometrics information (demographics and gait biosignal) of different subjects as edges. Then, the graph and node information [biosignal information, including the joint kinematics and surface electromyography (sEMG)] are used as the inputs to the GCN for diagnosis and classification of PFPS. The method is tested on a public dataset which contain walking and running data from 26 PFPS patients and 15 pain-free controls. The results suggest that our method can classify PFPS and pain-free with higher accuracy (mean accuracy = 0.8531 ± 0.047) than other methods with the biosignal information of individuals as input (mean accuracy = 0.813 ± 0.048). After optimal selection of input variables, the highest classification accuracy (mean accuracy = 0.9245 ± 0.034) can be obtained, and a high accuracy can still be obtained with a 40% reduction in test variables (mean accuracy = 0.8802 ± 0.035). Accordingly, the method effectively reflects the association between subjects, provides a simple and effective aid for physicians to diagnose PFPS, and gives new ideas for studying and validating risk factors related to PFPS.

Comprehensive quality of elderly rehabilitation nursing staff in medical and health care institutions in Liaoning Province, China: a cross-sectional study.

BACKGROUND: With the global aging problem is becoming increasingly severe, the elderly care has become an important issue that needs attention. Chinese government attaches great importance to the development of medical and health care institutions, and is committed to improving the comprehensive quality of elderly rehabilitation nursing staff in medical and health care institutions. METHODS: From June to September 2019, a cross-sectional study among 193 elderly rehabilitation nursing staff was conducted in Liaoning Province, China. Using a self-designed questionnaire, the comprehensive quality of elderly rehabilitation nursing staff in medical and health care institutions was investigated by face to face. The multiple linear regression model was explored to analyze the influencing factors. RESULTS: A total of 193 questionnaires were distributed, and 189 (97.93%) valid questionnaires were recovered. Age was from 19 to 65 years old, with an average age of (38.34 ± 9.76) years old. Bachelor degree or above accounted for 54.00%. 57.10% have engaged in elderly rehabilitation nursing for more than one year. There were 163 nurses with qualification certificates, accounting for 86.20%. The total score of comprehensive quality was 118.52 ± 22.90. The total Cronbach ' s α coefficient of the questionnaire was 0.967, and the content validity index was 0.991. Only 61 (32.30%) elderly rehabilitation nurses received professional training in elderly rehabilitation nursing. The results of multiple linear regression analysis showed that the educational level of elderly rehabilitation nursing staff (P = 0.002) and the number of years engaged in elderly rehabilitation nursing (P = 0.005) were the main influencing factors of comprehensive quality. CONCLUSIONS: The comprehensive quality of elderly rehabilitation nursing staff is at a medium level in Liaoning Province's medical and health care institutions. However, the professional nursing talents were very short, and the education level and years of experience in elderly care were the main influencing factors of the comprehensive quality.

Determining the Online Measurable Input Variables in Human Joint Moment Intelligent Prediction Based on the Hill Muscle Model.

Introduction: Human joint moment is a critical parameter to rehabilitation assessment and human-robot interaction, which can be predicted using an artificial neural network (ANN) model. However, challenge remains as lack of an effective approach to determining the input variables for the ANN model in joint moment prediction, which determines the number of input sensors and the complexity of prediction. Methods: To address this research gap, this study develops a mathematical model based on the Hill muscle model to determining the online input variables of the ANN for the prediction of joint moments. In this method, the muscle activation, muscle-tendon moment velocity and length in the Hill muscle model and muscle-tendon moment arm are translated to the online measurable variables, i.e. muscle electromyography (EMG), joint angles and angular velocities of the muscle span. To test the predictive ability of these input variables, an ANN model is designed and trained to predict joint moments. The ANN model with the online measurable input variables is tested on the experimental data collected from ten healthy subjects running with the speeds of 2, 3, 4 and 5 m/s on a treadmill. The variance accounted for (VAF) between the predicted and inverse dynamics moment is used to evaluate the prediction accuracy. Results: The results suggested that the method can predict joint moments with a higher accuracy (mean VAF = 89.67±5.56 %) than those obtained by using other joint angles and angular velocities as inputs (mean VAF = 86.27±6.6%) evaluated by jack-knife cross-validation. Conclusions: The proposed method provides us with a powerful tool to predict joint moment based on online measurable variables, which establishes the theoretical basis for optimizing the input sensors and detection complexity of the prediction system. It may facilitate the research on exoskeleton robot control and real-time gait analysis in motor rehabilitation.