Schizophrenia recognition based on three-dimensional adaptive graph convolutional neural network.

Previous deep learning-based brain network research has made significant progress in understanding the pathophysiology of schizophrenia. However, it ignores the three-dimensional spatial characteristics of EEG signals and cannot dynamically learn the interactions between nodes. To address this issue, a schizophrenia classification model based on a three-dimensional adaptive graph convolutional neural network (3D-AGCN) is proposed.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.