Background: Atrophic chronic gastritis (ACG) is a preneoplastic condition of gastric carcinoma.Numerous studies have shown anxiety and depression can affect gastrointestinal function, which may promote gastrointestinal disorders development and progression. Thus, we hypothesized that anxiety and depression may enhance the development and progression of ACG. In this study, we aimed to analyse risk factors for anxiety and depression in ACG patients and integrate these risk factors to construct an effective clinical prediction model. Methods:In total, 118 ACG patients were included from July 2021 to May 2022. Anxiety and depression were assessed utilizing the Generalized Anxiety Disorder-7 (GAD-7) and Patient Health Questionnaire-9 (PHQ-9). Data were collected on demographic characteristics, lifestyle, and dietary habits. Risk factors for anxiety and depression were explored with univariate analysis and multivariate stepwise logistic regression, and risk prediction models were built.Results: Among 118 ACG patients, 36.4% had anxiety, 25.4% had depression, and 21.2% had both anxiety and depression. Poor sleep quality [odd ratio (OR) 4.32, 95% confidence interval (CI): 1.60-11.65, P=0.004] was positively associated with risk of anxiety, while smoking (OR 0.15, 95% CI: 0.03-0.68, P=0.014) and weekly exercise time (OR 0.89, 95% CI: 0.79-0.99, P=0.037) were negatively associated with risk of anxiety.The area under the receiver operating characteristic (ROC) curve was 80.3%, 95% CI: [0.722-0.885].

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Attractin (ATRN) is a widely expressed glycoprotein that is involved in energy homeostasis, neurodevelopment, and immune response. It is encoded by a gene spanning 180 kb on chromosome 20p13, a region previously implicated in schizophrenia by linkage studies. To address a possible role of ATRN in disorders of the central nervous system, we created an atrn knockout zebrafish line and performed behavioral tests. Adult atrn–/– zebrafish exhibited more pronounced attack behavior relative to wild-type control zebrafish in a tracking analysis. Biochemical analysis revealed elevated testosterone levels in atrn–/– zebrafish. At the gene expression level, we noted an upregulation of cyp51 and hsd17b7, key proteins in testosterone synthesis in the brains of both adult and larvae of atrn–/– zebrafish. In order to further elucidate the relationship between testosterone and behavioral syndromes, we then compared testosterone levels of 9,008 psychiatric patients and 247 healthy controls from the same catchment area. Of all subjects examined, male subjects with schizophrenia exhibited lower testosterone levels compared with controls. In contrast, female subjects with a diagnosis of schizophrenia or bipolar disorder featured higher testosterone levels than did same sex controls. Purposeful sampling of extreme groups showed reduced ATRN expression in a subset of these subjects. Finally, we identified 14 subjects with ATRN mutations. All of whom displayed abnormal testosterone levels. In summary, the interplay of ATRN and testosterone may help to explain sexual dimorphisms in selected behavioral phenotypes.

Schizophrenia is a severe mental disorder affecting around 0.5–1% of the global population. A few studies have shown the functional disconnection in the default-mode network (DMN) of schizophrenia patients. However, the findings remain discrepant. In the current study, we compared the intrinsic network organization of DMN of 57 first-diagnosis drug-naïve schizophrenia patients with 50 healthy controls (HCs) using a homogeneity network (NH) and explored the relationships of DMN with clinical characteristics of schizophrenia patients. Receiver operating characteristic (ROC) curves analysis and support vector machine (SVM) analysis were applied to calculate the accuracy of distinguishing schizophrenia patients from HCs. Our results showed that the NH values of patients were significantly higher in the left superior medial frontal gyrus (SMFG) and right cerebellum Crus I/Crus II and significantly lower in the right inferior temporal gyrus (ITG) and bilateral posterior cingulate cortex (PCC) compared to those of HCs. Additionally, negative correlations were shown between aberrant NH values in the right cerebellum Crus I/Crus II and general psychopathology scores, between NH values in the left SMFG and negative symptom scores, and between the NH values in the right ITG and speed of processing. Also, patients’ age and the NH values in the right cerebellum Crus I/Crus II and the right ITG were the predictors of performance in the social cognition test. ROC curves analysis and SVM analysis showed that a combination of NH values in the left SMFG, right ITG, and right cerebellum Crus I/Crus II could distinguish schizophrenia patients from HCs with high accuracy. The results emphasized the vital role of DMN in the neuropathological mechanisms underlying schizophrenia.

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Time series prediction model plays an important role in stock price prediction, such as ARIMA, LSTM neural network. However, due to the need for stationary assumption of time series itself and the problems of high dimension and high noise, the common time series prediction methods have limitations. Based on this, this paper propose a framework for the optimization of the stock price time series prediction model. The proposed method uses the intra-day price as the auxiliary variable and obtains the function feature information based on Karhunen-Loève expansion. Considering that the feature variables after dimension reduction still have problems such as information loss and irrelevant noise. This paper use data enhancement method to improve the effective information of feature variables and reduce the influence of irrelevant noise. Then, since the potential model structure between the characteristic variable and the residual sequence is unknown, this paper develop a weighted ensemble regression method based on information gain to balance the variance and deviation of the prediction model, thereby improving the prediction accuracy. The actual data analysis results show that the proposed method can greatly improve the fitting accuracy of ARIMA and LSTM neural networks for stock prices. Finally, the optimization framework can also be used for the prediction of average temperature, air quality and port cargo flow.

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