Exploring the relationship of cognitive function with and without COVID-19 recovered schizophrenic patients.

Background: The Coronavirus disease 2019 (COVID-19) is linked to the deterioration of cognitive function among individuals suffering from schizophrenia. The purpose of this study was to compare the cognitive performance of schizophrenic patients before and after COVID-19. Methods: A longitudinal cohort study involving a sample of 219 individuals diagnosed with schizophrenia was enrolled between June 2022 and May 2023. The participants were split into two groups infected with COVID-19 (n = 165) and not infected with COVID-19 (n = 54). The data were gathered via a questionnaire on demographic characteristics, the Brief Assessment of Cognition in Schizophrenia (BACS), the Positive and Negative Syndrome Scale (PANSS), the Calgary Depression Scale for Schizophrenia (CDSS), the Activities of Daily Living (ADL) scale, and the Insomnia Severity Index (ISI). Results: The repeated-measures ANOVA showed that Among patients diagnosed with COVID-19, there was a deterioration in global cognitive function (before COVID-19 = -2.45 vs. after COVID-19 = -3.02, p = 0.007), working memory (before COVID-19 = -2.76 vs. after COVID-19 = -3.34, p < 0.00 1), motor speed (before COVID-19 = -1.64 vs. after COVID-19 = -2.12, p < 0.001), attention and speed of information processing (before COVID-19 = -1.93 vs. after COVID-19 = -1.16, p = 0.008). multi-variable analysis showed that several factors as having a secondary grade of education (ß = 0.434), experiencing insomnia (ß = 0.411)and the interaction between COVID-19 diagnosis and cognition at baseline (ß = 0.796) were significantly associated with cognitive deficits. At the same time, no significant associations were found between global cognition and clinical symptoms, autonomy, or depression (p > 0.05). Conclusion: The COVID-19 pandemic has significantly impacted various cognitive functions, such as verbal memory, working memory, and global cognition. Insomnia has been identified as the predominant determinant of cognitive impairment, alongside the confirmation of a COVID-19 diagnosis. Additional research is imperative to elucidate the diversification of cognitive functionality observed in individuals diagnosed with schizophrenia who have acquired COVID-19.

Alleviation of cadmium toxicity in Zea mays L. through up-regulation of growth, antioxidant defense system and organic osmolytes under calcium supplementation.

Calcium (Ca) is a macronutrient and works as a modulator to mitigate oxidative stress induced by heavy metals. In this study, we investigated the role of Ca to ameliorate the Cd toxicity in Zea mays L. by modulating the growth, physio-biochemical traits, and cellular antioxidant defense system. Maize genotype Sahiwal-2002 was grown under a controlled glasshouse environment with a day/night temperature of 24 ± 4°C/14 ± 2°C in a complete randomized design with three replications and two Cd levels as (0 and 150 µM) and six regimes of Ca (0, 0.5, 1, 2.5, 5, and 10 mM). Maize seedlings exposed to Cd at 150 µM concentration showed a notable decrease in growth, biomass, anthocyanins, chlorophylls, and antioxidant enzymes activities. A higher level of Cd (150 µM) also caused an upsurge in oxidative damage observed as higher electrolyte leakage (increased membrane permeability), H2O2 production, and MDA accumulation. Supplementation of Ca notably improved growth traits, photosynthetic pigments, cellular antioxidants (APX, POD, and ascorbic acid), anthocyanins, and levels of osmolytes. The significant improvement in the osmolytes (proteins and amino acids), and enzymatic antioxidative defense system enhanced the membrane stability and mitigated the damaging effects of Cd. The present results concluded that exogenously applied Ca potentially improve growth by regulating antioxidants and enabling maize plants to withstand the Cd toxicity.

Exploration of Psychological Mechanism of Smartphone Addiction Among International Students of China by Selecting the Framework of the I-PACE Model.

The I-PACE (interaction of person-affect-cognition-execution) model explains that the causes of addiction are the result of individual susceptibility (genetic and personality), psychopathological factors (negative emotions), and cognitive and affective factor interaction. The issue of smartphone addiction and its emerging effects are now becoming an essential social enigma. This study is aimed at exploring how personal, affective, cognitive, and execution factors accelerate the mechanism of smartphone addiction among international students. Randomly selected, six hundred international students have constituted the population for our study. All participants were asked to complete self-administered questionnaires. The questionnaire included demographics (gender, place of stay, educational level, and reason for smartphone usage), Mobile Phone Addiction Index, Loneliness Scale (UCLA), Rosenberg Self-Esteem Scale, Beck Depression Inventory, Perceived Stress Scale, Eysenck Personality Questionnaire, and Simplified Coping Style Questionnaire. Statistical analysis was performed using SPSS. 20.3% (n = 122) of international students are agonized with smartphone addiction, while 79.7% (n = 478) use smartphones at an average level. Students' place of stay, neuroticism personality, social desirability, self-esteem, loneliness, depression, perceived stress, and passive coping are associated with smartphone addiction. Loneliness and depression show a strong positive significant correlation, among other variables while loneliness, neurotic personality, depression, low self-esteem, stress, and passive coping are risk factors for smartphone addiction. This study reveals that international students are a high-risk group for smartphone addiction. It has a great deal of impact on students' behavior and psyche. Multiple social, psychological, affective, and cognitive factors affect smartphone addiction. It would be beneficial to direct the students to limit their phone usage and indulge in other healthy physical activities to complete academic goals.

Perceived Stress and Smartphone Addiction in Medical College Students: The Mediating Role of Negative Emotions and the Moderating Role of Psychological Capital.

Background: Many studies have confirmed the existence of an extremely close relationship between smartphone addiction and perceived stress. However, the mediating and moderating mechanisms underlying the association between perceived stress and smartphone addiction in medical college students remain largely unexplored. Methods: A questionnaire was distributed among a total of 769 medical college students in Heilongjiang Province, China. Participants completed measures of perceived stress, smartphone addiction, negative emotions, and psychological capital. Pearson's correlation analysis was used to test the correlations between variables. The analysis of a moderated mediation model was performed using Hayes's PROCESS macro. Results: Pearson's correlation analysis indicated that perceived stress (r = 0.18, p < 0.01) and negative emotions (r = 0.31, p < 0.01) were positively correlated with smartphone addiction, and psychological capital was negatively correlated with smartphone addiction (r = -0.29, p < 0.01). The moderated mediation analysis indicated that negative emotions partially mediated the association between perceived stress and smartphone addiction [mediation effect accounted for 33.3%, SE = 0.10, 95% CI = (0.10, 0.24)], and the first stage of the mediation process was significantly moderated by psychological capital [moderated mediation = -0.01, SE = 0.01, 95% CI = (-0.01, -0.00)]. Conclusion: Negative emotions play a mediating role between perceived stress and smartphone addiction, and psychological capital plays an important moderating role in the first stage of the mediation process.

A non-uniform quantization scheme for visualization of CT images.

Medical science heavily depends on image acquisition and post-processing for accurate diagnosis and treatment planning. The introduction of noise degrades the visual quality of the medical images during the capturing process, which may result in false perception. Therefore, medical image enhancement is an essential topic of research for the improvement of image quality. In this paper, a clustering-based contrast enhancement technique is presented for computed tomography (CT) images. Our approach uses the recursive splitting of data into clusters targeting the maximum error reduction in each cluster. This leads to grouping similar pixels in every cluster, maximizing inter-cluster and minimizing intra-cluster similarities. A suitable number of clusters can be chosen to represent high precision data with the desired bit-depth. We use 256 clusters to convert 16-bit CT scans to 8-bit images suitable for visualization on standard low dynamic range displays. We compare our method with several existing contrast enhancement algorithms and show that the proposed technique provides better results in terms of execution efficiency and quality of enhanced images.

Roles of Anxiety and Depression in Predicting Cardiovascular Disease Among Patients With Type 2 Diabetes Mellitus: A Machine Learning Approach.

Cardiovascular disease (CVD) is a major complication of type 2 diabetes mellitus (T2DM). In addition to traditional risk factors, psychological determinants play an important role in CVD risk. This study applied Deep Neural Network (DNN) to develop a CVD risk prediction model and explored the bio-psycho-social contributors to the CVD risk among patients with T2DM. From 2017 to 2020, 834 patients with T2DM were recruited from the Department of Endocrinology, Affiliated Hospital of Harbin Medical University, China. In this cross-sectional study, the patients' bio-psycho-social information was collected through clinical examinations and questionnaires. The dataset was randomly split into a 75% train set and a 25% test set. DNN was implemented at the best performance on the train set and applied on the test set. The receiver operating characteristic curve (ROC) analysis was used to evaluate the model performance. Of participants, 272 (32.6%) were diagnosed with CVD. The developed ensemble model for CVD risk achieved an area under curve score of 0.91, accuracy of 87.50%, sensitivity of 88.06%, and specificity of 87.23%. Among patients with T2DM, the top five predictors in the CVD risk model were body mass index, anxiety, depression, total cholesterol, and systolic blood pressure. In summary, machine learning models can provide an automated identification mechanism for patients at CVD risk. Integrated treatment measures should be taken in health management, including clinical care, mental health improvement, and health behavior promotion.

The Correlation between Dietary Selenium Intake and Type 2 Diabetes: A Cross-Sectional Population-Based Study on North Chinese Adults.

The relationship between selenium (Se) and type 2 diabetes (T2D) remains controversial. In previous animal and cell studies, Se was found to be insulin mimic and antidiabetic, whereas recent epidemiological and interventional trials have shown an unexpected association between high Se intake and increased risk of T2D. The present study aimed to investigate the significance of dietary Se and T2D in North Chinese adults. A large sample of the population was enrolled through cluster sampling in Northern China (N=8824). Information on basic characteristics, anthropometric measures, and dietary Se intake was collected from each subject for analysis. Multivariable logistic regression was used to investigate the association between dietary Se and T2D through adjusted odds ratio (OR) and the corresponding 95% confidence interval (CI). The average nutritional Se intake was 52.43 µg/day, and the prevalence of T2D was 20.4% in the studied population. The OR for developing T2D was 1.66 (95% CI: 1.38, 1.99; P for linear trend <0.005), comparing the highest to the lowest quintile of energy-adjusted Se intake in multivariate logistic regression analysis. The mediation analysis discovered that glucose metabolism (indicated by FBG and HbA1c) mediated this association. In conclusion, our research adds further support to the role of high dietary Se in the incidence of T2D. The results also suggested that this association was mediated by glucose metabolism.