Automatic recognition of schizophrenia from brain-network features using graph convolutional neural network.

Schizophrenia is a severe mental illness that imposes considerable economic burden on families and society. However, its clinical diagnosis primarily relies on scales and doctors' clinical experience and lacks an objective and accurate diagnostic approach. In recent years, graph convolutional neural networks (GCN) have been used to assist in psychiatric diagnosis owing to their ability to learn spatial-association information. Therefore, this study proposes a schizophrenia automatic recognition model based on graph convolutional neural network. Herein, the resting-state electroencephalography (EEG) data of 103 first-episode schizophrenia patients and 92 normal controls (NCs) were obtained. The automatic recognition model was trained with a nodal feature matrix that comprised the time and frequency-domain features of the EEG signals and local features of the brain network. The most significant regions that contributed to the model classification were identified, and the correlation between the node topological features of each significant region and clinical evaluation metrics was explored. Experiments were conducted to evaluate the performance of the model using 10-fold cross-validation. The best performance in the theta frequency band with a 6 s epoch length and phase-locked value. The recognition accuracy was 90.01%. The most significant region for identifying with first-episode schizophrenia patients and NCs was located in the parietal lobe. The results of this study verify the applicability of the proposed novel method for the identification and diagnosis of schizophrenia.

Method for persistent topological features extraction of schizophrenia patients' electroencephalography signal based on persistent homology.

With the development of network science and graph theory, brain network research has unique advantages in explaining those mental diseases, the neural mechanism of which is unclear. Additionally, it can provide a new perspective in revealing the pathophysiological mechanism of brain diseases from the system level. The selection of threshold plays an important role in brain networks construction. There are no generally accepted criteria for determining the proper threshold. Therefore, based on the topological data analysis of persistent homology theory, this study developed a multi-scale brain network modeling analysis method, which enables us to quantify various persistent topological features at different scales in a coherent manner. In this method, the Vietoris-Rips filtering algorithm is used to extract dynamic persistent topological features by gradually increasing the threshold in the range of full-scale distances. Subsequently, the persistent topological features are visualized using barcodes and persistence diagrams. Finally, the stability of persistent topological features is analyzed by calculating the Bottleneck distances and Wasserstein distances between the persistence diagrams. Experimental results show that compared with the existing methods, this method can extract the topological features of brain networks more accurately and improves the accuracy of diagnostic and classification. This work not only lays a foundation for exploring the higher-order topology of brain functional networks in schizophrenia patients, but also enhances the modeling ability of complex brain systems to better understand, analyze, and predict their dynamic behaviors.

Intermittent pneumatic compression (IPC) combined with an electric stimulator in the prevention of venous thromboembolism in stroke patients.

OBJECTIVE: To explore the clinical value of intermittent pneumatic compression (IPC) combined with an electric stimulator in the prevention of venous thromboembolism in stroke patients. METHODS: 58 stroke patients with hemiplegia admitted to the Department of Neurology in our hospital were recruited as the study cohort and randomly placed into a control group or an observation group, with 29 patients in each group. The control group was administered routine care and IPC, and the observation group was administered electric stimulation in addition to the treatment administered to the control group. We conducted a comparison and an analysis of the occurrences of thrombosis, the blood rheology indexes, the femoral vein flow rates, and the nursing satisfaction levels in the two groups. The circumferences of the hemiplegia patients' lower extremities were measured and recorded, and the circumferences of the healthy sides and the affected limbs were compared. RESULTS: On the 7th day after the intervention, the observation group had a higher incidence of deep vein thrombosis (DVT) than the control group (6.90% vs. 31.03%, P<0.05). The hemorheology indexes were lower after the treatment, and the hemorheology indexes in the observation group were higher compared with the control group (P<0.05). The observation group had a higher femoral vein flow velocity than the control group (P<0.05). On the 7th and 14th days after the intervention, the peak flow and average flow velocities in the observation group exceeded those of the control group (P<0.05). The nursing satisfaction rate in the observation group was higher than it was in the control group (96.55% vs. 82.76%, P<0.05). After 7 and 14 days of treatment, smaller changes in the hemiplegic limbs of the observation group were observed, compared to the control group (P<0.05). CONCLUSION: IPC combined with an electrical stimulator can enhance the patients' blood hypercoagulability, effectively prevent the occurrence of DVT, and improve the nursing satisfaction levels.

Curative Effect of Early Full Nursing Combined with Postdischarge Continuation Nursing on Patients after Craniocerebral Trauma.

Early full nursing helps patients with some dysfunctions speed up the reorganization of central nervous system functions and coordinate muscle and limb activities. Postdischarge continuation nursing for patients who have not fully recovered after being discharged from the hospital can transfer nursing work from the hospital to the family to meet their nursing needs. In this study, early full nursing combined with postdischarge continuation nursing was used for patients with traumatic brain injury to explore its efficacy and its impact on patients' motor function, quality of life, and complications. The results of the study show that the total effective rate of the observation group (95.92%) was higher than that of the control group (85.71%). At discharge and 1 month, 3 months, and 6 months after discharge, the upper limb Fugl-Meyer score, lower limb Fugl-Meyer score, ARAT score, FIM score, 4 dimensions of GQOLI-74 score, and Barthel index scores of the observation group were higher than those of the control group in the same period. The postoperative complication rate (10.20%) of the observation group was lower than that of the control group (26.53%). Early full nursing combined with postdischarge continuation nursing can improve the rehabilitation effect, effectively promote the recovery of motor function in patients with traumatic brain injury, improve the quality of life, and reduce postoperative complications.

Ultrasonic thermometric measurement system for solid rocket combustion chambers.

Solid rocket motor (SRM) temperature is an important physical parameter for which there is no reliable in situ measurement device, apart from a thermocouple, for such a high temperature environment apart. In this study, an ultrasonic temperature measurement system was designed with an iridium-rhodium-40% alloy waveguide. Laboratory experiments showed that the device obtained ultrasonic signals up to 1800 °C with a temperature fitting curve from room temperature to 1800 °C. The thermometer also operated stably under high temperature and produced a repeatable calibration curve, at 97% repeatability. An error band was obtained with 95% confidence. At temperatures above 1000 °C, sensitivity gradually increased to a maximum of 0.0035 µs/°C. A corresponding application structure was established for an SRM before subjecting the sensor to a temperature test experiment. The temperature-time curve obtained detected a peak temperature at 1744 °C.

Synchronization Stability Model of Complex Brain Networks: An EEG Study.

In this paper, from the perspective of complex network dynamics we investigated the formation of the synchronization state of the brain networks. Based on the Lyapunov stability theory of complex networks, a synchronous steady-state model suitable for application to complex dynamic brain networks was proposed. The synchronization stability problem of brain network state equation was transformed into a convex optimization problem with Block Coordinate Descent (BCD) method. By using Random Apollo Network (RAN) method as a node selection rule, the brain network constructs its subnet work dynamically. We also analyzes the change of the synchronous stable state of the subnet work constructed by this method with the increase of the size of the network. Simulation EEG data from alcohol addicts patients and Real experiment EEG data from schizophrenia patients were used to verify the robustness and validity of the proposed model. Differences in the synchronization characteristics of the brain networks between normal and alcoholic patients were analyzed, so as differences between normal and schizophrenia patients. The experimental results indicated that the establishment of a synchronous steady state model in this paper could be used to verify the synchronization of complex dynamic brain networks and potentially be of great value in the further study of the pathogenic mechanisms of mental illness.

The early stage of face detection in patients with major depressive disorder: an ERP study.

To investigate whether perceptual processes involved in early stages of face processing are influenced by depressive disorder, the face detection and configural analysis were assessed by recording the N170 component elicited by faces and objects (tables) presented under upright and inverted conditions. The N170 component elicited at occipital-temporal sites by faces was larger and peaked later than that elicited by tables, and inverted faces significantly enhanced and delayed the N170. The N170 in response to faces was enhanced in patients with major depressive disorder and the N170 face effect for upright condition was significantly larger in major depressive disorder patients than that in controls. However, the N170 inversion effect did not differ between two groups. These data indicate that, compared with normal controls, there is abnormal face perception at the early stage of processing faces in major depressive disorder patients but the major depressive disorder patients may have intact configural processing of faces.

Impacts of self-care education on adverse events and mental health related quality of life in breast cancer patients under chemotherapy.

OBJECTIVE: As a consequence of its high incidence, breast cancer has become a severe health risk in women. Chemotherapy is one of the main treatments for breast cancer, but causes a decline in life quality of patients. Self-care is a non-medical intervention and has been reported to improve the life quality of colorectal cancer patients. We aim to explore whether self-care is also effective in breast cancer. MATERIALS AND METHODS: 85 breast cancer patients under chemotherapy participated in this research, among whom 44 patients received the self-care education. The physical and mental conditions of patients before and after chemotherapy were evaluated by Anxiety Inventory, Rotterdam Symptom checklists and QLQ-C30. RESULTS: The result showed that the occurrence rates of symptoms were significantly reduced after self-care measures. Anxiety Inventory and Rotterdam Symptom checklists indicated that self-care measures could improve both the physical and mental conditions of patients. The Global Quality of Life (QoL) from QLQ-C30 questionnaire further confirmed the effectiveness of self-care measures in breast cancer patients. CONCLUSIONS: Based on the results, self-care measures are effective in improving the physical and mental conditions of breast cancer patients under chemotherapy. Self-care measures play an important role in improving patients' life quality.

Adjustment of Synchronization Stability of Dynamic Brain-Networks Based on Feature Fusion.

When the brain is active, the neural activities of different regions are integrated on various spatial and temporal scales; this is termed the synchronization phenomenon in neurobiological theory. This synchronicity is also the main underlying mechanism for information integration and processing in the brain. Clinical medicine has found that some of the neurological diseases that are difficult to cure have deficiencies or abnormalities in the whole or local integration processes of the brain. By studying the synchronization capabilities of the brain-network, we can intensively describe and characterize both the state of the interactions between brain regions and their differences between people with a mental illness and a set of controls by measuring the rapid changes in brain activity in patients with psychiatric disorders and the strength and integrity of their entire brain network. This is significant for the study of mental illness. Because static brain network connection methods are unable to assess the dynamic interactions within the brain, we introduced the concepts of dynamics and variability in a constructed EEG brain functional network based on dynamic connections, and used it to analyze the variability in the time characteristics of the EEG functional network. We used the spectral features of the brain network to extract its synchronization features and used the synchronization features to describe the process of change and the differences in the brain network's synchronization ability between a group of patients and healthy controls during a working memory task. We propose a method based on the fusion of traditional features and spectral features to achieve an adjustment of the patient's brain network synchronization ability, so that its synchronization ability becomes consistent with that of healthy controls, theoretically achieving the purpose of the treatment of the diseases. Studying the stability of brain network synchronization can provide new insights into the pathogenic mechanism and cure of mental diseases and has a wide range of potential applications.

Classification of Alzheimer's Disease, Mild Cognitive Impairment, and Normal Controls With Subnetwork Selection and Graph Kernel Principal Component Analysis Based on Minimum Spanning Tree Brain Functional Network.

Effective and accurate diagnosis of Alzheimer's disease (AD), as well as its early stage (mild cognitive impairment, MCI), has attracted more and more attention recently. Researchers have constructed threshold brain function networks and extracted various features for the classification of brain diseases. However, in the construction of the brain function network, the selection of threshold is very important, and the unreasonable setting will seriously affect the final classification results. To address this issue, in this paper, we propose a minimum spanning tree (MST) classification framework to identify Alzheimer's disease (AD), MCI, and normal controls (NCs). The proposed method mainly uses the MST method, graph-based Substructure Pattern mining (gSpan), and graph kernel Principal Component Analysis (graph kernel PCA). Specifically, MST is used to construct the brain functional connectivity network; gSpan, to extract features; and subnetwork selection and graph kernel PCA, to select features. Finally, the support vector machine is used to perform classification. We evaluate our method on MST brain functional networks of 21 AD, 25 MCI, and 22 NC subjects. The experimental results show that our proposed method achieves classification accuracy of 98.3, 91.3, and 77.3%, for MCI vs. NC, AD vs. NC, and AD vs. MCI, respectively. The results show our proposed method can achieve significantly improved classification performance compared to other state-of-the-art methods.

The dysfunction of processing emotional faces in schizophrenia revealed by expression-related visual mismatch negativity.

To investigate the emotional face processing in patients with schizophrenia, the preattentive automatic processing of emotional faces in individuals with schizophrenia was compared with that of age-matched healthy control group as indexed by the expressional mismatch negativity (EMMN) elicited by facial expressions. Compared with neutral faces as standard stimuli, deviant emotional faces elicited posterior EMMN between 150 and 500 ms after stimuli onset, with larger amplitudes for sad than happy deviant faces. Both early and late EMMNs significantly decreased in the schizophrenia group, regardless of sad or happy EMMN, in comparison with the healthy control group. These data suggest the dysfunction of automatic processing of expressional information in patients with schizophrenia.

Topical application of quercetin improves wound healing in pressure ulcer lesions.

The ischaemia-reperfusion (I/R)-induced skin lesion has been identified as primary cause of pressure ulcers. To date, attempts to prevent pressure ulcers have not produced a significant improvement. Quercetin, one of the most widely distributed flavonoids in fruits and vegetables, exhibits its antioxidant and anti-inflammatory properties against many diseases, including ischaemic heart disease, atherosclerosis and renal injury. In vitro wound scratch assay was first used to assess the function of quercetin in wounding cell model. Next, animal pressure ulcers model was established with two cycles of I/R. The impact of quercetin in the wound recovery, immune cell infiltration and pro-inflammatory cytokines production was investigated in this model. Mechanistic regulation of quercetin at the wound site was also studied. Quercetin accelerated wound closure in cell scratch assay. Dose-response study suggested 1 µmol/L quercetin for in vivo study. In I/R injury model, quercetin treatment significantly accelerated wound closure, reduced immune cell infiltration and pro-inflammatory cytokines production. Signalling study showed quercetin treatment inhibited MAPK but not NFĸB activation. Quercetin treatment improved the wound healing process in I/R lesions by suppressing MAPK pathway. Our results supported that quercetin could be a potential therapeutic agent for pressure ulcers.