Latent profile analysis of depression in US adults with obstructive sleep apnea hypopnea syndrome.

Objective: This study used latent profile analysis to explore the level of depression among US adults with obstructive sleep apnea hypopnea syndrome (OSAHS) symptoms and to identify different latent categories of depression to gain insight into the characteristic differences between these categories. Methods: The data of this study were obtained from the National Health and Nutrition Examination Survey (NHANES) database, and the subjects with OSAHS symptoms were aged 18 years and older. The latent profile analysis (LPA) method was used to fit the latent depression categories in subjects with OSAHS symptoms. The chi-square test, rank sum test, and binary logistic regression were used to analyze the influencing factors of depression subgroups in subjects with OSAHS symptoms. Results: Three latent profiles were identified: low-level (83.7%), moderate-level (14.5%) and high-level (1.8%) depression. The scores of 9 items in the high-level depression group were higher than those in the other two groups. Among them, item 4 "feeling tired or lack of energy" had the highest score in all categories. Conclusion: Depression in subjects with OSAHS symptoms can be divided into low-level, moderate-level and high-level depression. There are significant differences among different levels of depression in gender, marital status, PIR, BMI, smoking, general health condition, sleep duration and OSAHS symptom severity.

Construction of a machine learning-based risk prediction model for depression in middle-aged and elderly hypertensive people in China: a longitudinal study.

Background: Hypertension is a common chronic disease that can trigger symptoms such as anxiety and depression. Therefore, it is essential to predict their risk of depression. The aim of this study is to find the best prediction model and provide effective intervention strategies for health professionals. Methods: The study subjects were 2733 middle-aged and older adults who participated in the China Health and Retirement Longitudinal Study (CHARLS) between 2018 and 2020. R software was used for Lasso regression analysis to screen the best predictor variables, and logistic regression, random forest and XGBoost models were constructed. Finally, the prediction efficiency of the three models was compared. Results: In this study, 18 variables were included, and LASSO regression screened out 10 variables that were important for the establishment of the model. Among the three models, Logistic Regression model showed the best performance in various evaluation indicators. Conclusion: The prediction model based on machine learning can accurately assess the likelihood of depression in middle-aged and elderly patients with hypertension in the next three years. And by combining Logistic regression and nomograms, we were able to provide a clear interpretation of personalized risk predictions.

Association between disability and cognitive function in older Chinese people: a moderated mediation of social relationships and depressive symptoms.

Objective: Many previous studies have found that disability leads to cognitive impairment, and in order to better understand the underlying mechanisms between disability and cognitive impairment, the present study aimed to investigate the moderating role of social relationships, including their role as mediators between disability and cognitive impairment in depressive symptoms. Study design: This is a cross-sectional study. Methods: A total of 5,699 Chinese older adults from the 2018 China Longitudinal Healthy Longevity Survey (CLHLS) were included in this study, and PROCESS macro was used to perform simple mediator and moderator mediator analyses, which were used to analyze the relationship between depressive symptoms and social relationships between disability and cognitive impairment. Results: The results of this study showed significant correlations between disability, cognitive impairment, depressive symptoms, and social relationships, and that depressive symptoms mediated the relationship between disability and cognitive functioning [B = -0.232; 95% CI: (-0.304, -0.164)], and that social relationships mediated disability and cognitive functioning through pathway a (Disability-Depressive Symptoms) [B = 0.190; 95% CI: (0.020, 0.036)], path b (depressive symptoms-cognitive impairment) [B = 0.029; 95% CI: (0.015, 0.042)], and path c' (incapacitation-cognitive impairment) [B = 0.492; 95% CI: (0.298, 0.685)] to modulate the effect of incapacitation on cognitive impairment. In addition, social activities and social networks moderated the mediation model directly or indirectly, whereas social support moderated only the direct effect. Conclusion: This study explains the intrinsic link between incapacitation and cognitive impairment in Chinese older adults, and that social relationships and depressive symptoms can directly or indirectly modulate the effects between them. This provides a basis for healthcare professionals to be able to better develop interventions that can be used to improve the level of cognitive functioning and mental health of older adults.

A machine learning model to predict the risk of depression in US adults with obstructive sleep apnea hypopnea syndrome: a cross-sectional study.

Objective: Depression is very common and harmful in patients with obstructive sleep apnea hypopnea syndrome (OSAHS). It is necessary to screen OSAHS patients for depression early. However, there are no validated tools to assess the likelihood of depression in patients with OSAHS. This study used data from the National Health and Nutrition Examination Survey (NHANES) database and machine learning (ML) methods to construct a risk prediction model for depression, aiming to predict the probability of depression in the OSAHS population. Relevant features were analyzed and a nomogram was drawn to visually predict and easily estimate the risk of depression according to the best performing model. Study design: This is a cross-sectional study. Methods: Data from three cycles (2005-2006, 2007-2008, and 2015-2016) were selected from the NHANES database, and 16 influencing factors were screened and included. Three prediction models were established by the logistic regression algorithm, least absolute shrinkage and selection operator (LASSO) algorithm, and random forest algorithm, respectively. The receiver operating characteristic (ROC) area under the curve (AUC), specificity, sensitivity, and decision curve analysis (DCA) were used to assess evaluate and compare the different ML models. Results: The logistic regression model had lower sensitivity than the lasso model, while the specificity and AUC area were higher than the random forest and lasso models. Moreover, when the threshold probability range was 0.19-0.25 and 0.45-0.82, the net benefit of the logistic regression model was the largest. The logistic regression model clarified the factors contributing to depression, including gender, general health condition, body mass index (BMI), smoking, OSAHS severity, age, education level, ratio of family income to poverty (PIR), and asthma. Conclusion: This study developed three machine learning (ML) models (logistic regression model, lasso model, and random forest model) using the NHANES database to predict depression and identify influencing factors among OSAHS patients. Among them, the logistic regression model was superior to the lasso and random forest models in overall prediction performance. By drawing the nomogram and applying it to the sleep testing center or sleep clinic, sleep technicians and medical staff can quickly and easily identify whether OSAHS patients have depression to carry out the necessary referral and psychological treatment.

Effects of Exogenous Hydrogen Sulfide in the Hypothalamic Paraventricular Nucleus on Gastric Function in Rats.

Background: Hydrogen sulfide (H2S) is a new type of gas neurotransmitter discovered in recent years. It plays an important role in various physiological activities. The hypothalamus paraventricular nucleus (PVN) is an important nucleus that regulates gastric function. This study aimed to clarify the role of H2S in the paraventricular nucleus of the hypothalamus on the gastric function of rats. Methods: An immunofluorescence histochemistry double-labelling technique was used to determine whether cystathionine-beta-synthase (CBS) and c-Fos neurons are involved in PVN stress. Through microinjection of different concentrations of NaHS, physiological saline (PS), D-2-Amino-5-phosphonovaleric acid (D-AP5), and pyrrolidine dithiocarbamate (PDTC), we observed gastric motility and gastric acid secretion. Results: c-Fos and CBS co-expressed the most positive neurons after 1 h of restraint and immersion, followed by 3 h, and the least was at 0 h. After injection of different concentrations of NaHS into the PVN, gastric motility and gastric acid secretion in rats were significantly inhibited and promoted, respectively (p < 0.01); however, injection of normal saline, D-AP5, and PDTC did not cause any significant change (p > 0.05). The suppressive effect of NaHS on gastrointestinal motility and the promotional effect of NaHS on gastric acid secretion could be prevented by D-AP5, a specific N-methyl-D-aspartic acid (NMDA) receptor antagonist, and PDTC, an NF-κB inhibitor. Conclusion: There are neurons co-expressing CBS and c-Fos in the PVN, and the injection of NaHS into the PVN can inhibit gastric motility and promote gastric acid secretion in rats. This effect may be mediated by NMDA receptors and the NF-κB signalling pathway.

Genome-wide identification and characterization of nonspecific lipid transfer protein (nsLTP) genes in Arachis duranensis.

Nonspecific lipid transfer proteins (nsLTPs) play vital roles in lipid metabolism, cell apoptosis and biotic and abiotic stresses in plants. However, the distribution of nsLTPs in Arachis duranensis has not been fully characterized. In this study, we identified 64 nsLTP genes in A. duranensis (designated AdLTPs), which were classified into six subfamilies and randomly distributed along nine chromosomes. Tandem and segmental duplication events were detected in the evolution of AdLTPs. The Ks and ω values differed significantly between Types 1 and D subfamilies, and eight AdLTPs were under positive selection. The expression levels of AdLTPs were changed after salinity, PEG, low-temperature and ABA treatments. Three AdLTPs were associated with resistance to nematode infection, and DOF and WRI1 transcription factors may regulate the AdLTP response to nematode infection. Our results may provide valuable genomic information for the breeding of peanut cultivars that are resistant to biotic and abiotic stresses.