Phenotypic Resemblance to Neuropsychiatric Disorder and Altered mRNA Profiles in Cortex and Hippocampus Underlying IL15Rα Knockout

Reduced Serum Levels of Soluble Interleukin-15 Receptor α in Schizophrenia and Its Relationship to the Excited Phenotype

Our previous studies documented that interleukin-15 receptor α (IL-15Rα) knockout (KO) mice exhibited hyperactivity, memory impairment, and desperate behavior, which are core features of schizophrenia and depression. Due to the overlapping symptomology and pathogenesis observed for schizophrenia and depression, the present study attempted to determine whether IL-15Rα was associated with the risk of schizophrenia or depression. One hundred fifty-six participants, including 63 schizophrenia patients, 29 depressive patients, and 64 age-matched healthy controls, were enrolled in the study. We investigated the circulating levels of soluble IL-15Rα and analyzed potential links between the IL-15Rα levels and clinical symptoms present in schizophrenia or depressive patients. We observed reduced serum IL-15Rα levels in schizophrenia patients, but not depressive patients compared with controls. Moreover, a significant negative association was observed between the circulating IL-15Rα levels and excited phenotypes in the schizophrenia patients. The IL-15Rα KO mice displayed pronounced pre-pulse inhibition impairment, which was a typical symptom of schizophrenia. Interestingly, the IL-15Rα KO mice exhibited a remarkable elevation in the startle amplitude in the startle reflex test compared to wild type mice. These results demonstrated that serum levels of soluble IL-15Rα were reduced in schizophrenia and highlighted the relationship of IL-15Rα and the excited phenotype in schizophrenia patients and mice.

The Application of Convolutional Neural Network Model in Diagnosis and Nursing of MR Imaging in Alzheimer's Disease

The disease Alzheimer is an irrepressible neurologicalbrain disorder. Earlier detection and proper treatment of Alzheimer's disease can help for brain tissue damage prevention. The study was intended to explore the segmentation effects of convolutional neural network (CNN) model on Magnetic Resonance (MR) imaging for Alzheimer's diagnosis and nursing. Specifically, 18 Alzheimer's patients admitted to Indira Gandhi Medical College (IGMC) hospital were selected as the experimental group, with 18 healthy volunteers in the Ctrl group. Furthermore, the CNN model was applied to segment the MR imaging of Alzheimer's patients, and its segmentation effects were compared with those of the fully convolutional neural network (FCNN) and support vector machine (SVM) algorithms. It was found that the CNN model demonstrated higher segmentation precision, and the experimental group showed a higher clinical dementia rating (CDR) score and a lower mini-mental state examination (MMSE) score (P < 0.05). The size of parahippocompalgyrus and putamen was bigger in the Ctrl (P < 0.05). In experimental group, the amplitude of low-frequency fluctuation (ALFF) was positively correlated with the MMSE score in areas of bilateral cingulum gyri (r = 0.65) and precuneus (r = 0.59). In conclusion, the grey matter structure is damaged in Alzheimer's patients, and hippocampus ALFF and regional homogeneity (ReHo) is involved in the neuronal compensation mechanism of hippocampal damage, and the caregivers should take an active nursing method.