Enhanced Beta2-band Oscillations Denote Auditory Hallucination in Schizophrenia Patients and a Monkey Model of Psychosis.

An electroencephalographic (EEG) signature of auditory hallucinations (AHs) is important for facilitating the diagnosis and treatment of AHs in schizophrenia. We recorded EEG from 25 schizophrenia patients with recurrent AHs. During the period of AHs, EEG recordings exhibited significantly elevated beta2-band power in the temporal region, as compared to those recorded in the absence of AHs or during stimulation with verbal sounds. We further generated methamphetamine-treated rhesus monkeys exhibiting psychosis-like behaviors, including repetitive sudden searching actions in the absence of external intrusion, suggesting the occurrence of AHs. Epidural EEG beta2-band power in the temporal region of these monkeys was enhanced immediately after methamphetamine treatment and positively correlated with the frequency of sudden searching actions. Thus, the enhancement of temporal beta2-band oscillations represents a signature for AHs in both patients and a monkey model of psychosis, and this monkey model can be used for developing closed-loop neuromodulation approaches for the treatment of refractory AHs in schizophrenia.

Identification of feature genes and pathways for Alzheimer's disease via WGCNA and LASSO regression.

While Alzheimer's disease (AD) can cause a severe economic burden, the specific pathogenesis involved is yet to be elucidated. To identify feature genes associated with AD, we downloaded data from three GEO databases: GSE122063, GSE15222, and GSE138260. In the filtering, we used AD for search keywords, Homo sapiens for species selection, and established a sample size of > 20 for each data set, and each data set contains Including the normal group and AD group. The datasets GSE15222 and GSE138260 were combined as a training group to build a model, and GSE122063 was used as a test group to verify the model's accuracy. The genes with differential expression found in the combined datasets were used for analysis through Gene Ontology (GO) and The Kyoto Encyclopedia of Genes and Genome Pathways (KEGG). Then, AD-related module genes were identified using the combined dataset through a weighted gene co-expression network analysis (WGCNA). Both the differential and AD-related module genes were intersected to obtain AD key genes. These genes were first filtered through LASSO regression and then AD-related feature genes were obtained for subsequent immune-related analysis. A comprehensive analysis of three AD-related datasets in the GEO database revealed 111 common differential AD genes. In the GO analysis, the more prominent terms were cognition and learning or memory. The KEGG analysis showed that these differential genes were enriched not only in In the KEGG analysis, but also in three other pathways: neuroactive ligand-receptor interaction, cAMP signaling pathway, and Calcium signaling pathway. Three AD-related feature genes (SST, MLIP, HSPB3) were finally identified. The area under the ROC curve of these AD-related feature genes was greater than 0.7 in both the training and the test groups. Finally, an immune-related analysis of these genes was performed. The finding of AD-related feature genes (SST, MLIP, HSPB3) could help predict the onset and progression of the disease. Overall, our study may provide significant guidance for further exploration of potential biomarkers for the diagnosis and prediction of AD.

Interaction of sex, age, body mass index and race on hypertension risk in the American population: a cross-sectional study.

BACKGROUND: The joint effects of sex, age, body mass index (BMI) and race on hypertension have not been fully addressed. Herein, we carried out this study aiming to investigate the possible effects of the interaction of sex, age, BMI and race on risk of hypertension. METHODS: By using the data of a sample-adjusted 2656 women and 2515 men in American National Health and Nutrition Examination Survey 2015-16, we analyzed the interaction of sex, age, BMI and race by logistic regression models, followed by strata-specific analyses. Hypertension was defined as a systolic blood pressure ≥130 mmHg/diastolic blood pressure ≥80 mmHg or taking anti-hypertensive medication. RESULTS: A total of 5171 participants were included in analysis, and the prevalence of hypertension was 53.68%. The interactive effect of sex and age, BMI and age, race and age were statistically significant on hypertension. Strata-specific analyses showed that female at 40 years and above were positively associated with hypertension than those at 20-39 years. The associations also persistence in male. The risk estimates for age ≥40 on hypertension were consistently positive across all overweight/obesity and race groups. The effect was most prominent among overweight populations aged 60-80 years and Other Hispanic aged 40 years and above. CONCLUSION: There exists interactive effect of sex and age, BMI and age, race and age on hypertension in American population. The effect of age on hypertension was more prominent in female, overweight populations and Other Hispanic populations. Differences in age, BMI and race should be considered when providing corresponding antihypertensive measures.