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Objective To establish a mouse model of pregnancy pain-depression comorbidity induced by chronic unpredictable stress (CUS), complete Freund's adjuvant (CFA), and formalin, and to systematically evaluate the associated phenotypes and preliminarily explore the pathological basis of the comorbidity. Methods Eight-week-old C57BL/6J female mice were randomly strarified divided into a control group (no intervention before pregnancy) and a CUS model group (CUS intervention before pregnancy) based on sucrose preference test (SPT) data. After completing the CUS treatment, female and male mice were paired and mated. Pain was induced by injecting 50% CFA and 5% formalin in the right hind foot during pregnancy to create a model of pregnancy pain-depression comorbidity. The experiment was divided into 8 subgroups: control-blank group, CUS-blank group, control-CFA group, CUS-CFA group, control-formalin group, CUS-formalin group, control-CFA+formalin group, and CUS-CFA+formalin group, with 10 mice in each group. The mice in each group were subject to behavioral tests, including the SPT, forced swimming test, tail suspension test, and open field test before and after CUS intervention, during pregnancy, and after delivery. Pain sensitivity changes were measured using mechanical allodynia and thermal hyperalgesia tests. Mice were then euthanized. Levels of interleukin-6 (IL-6) and tumor necrosis factor-α (TNF-α) in hippocampus, as well as cortisol and adrenocorticotropic hormone (ACTH) in serum, were detected by enzyme-linked immunosorbent assay (ELISA). ResultsCompared with the control-blank group, the CUS-blank group showed a significant depression-like behavior with reduced pain threshold (P

Background/Objectives: Excessive reactive oxygen species (ROS) can lead to oxidative stress, which has become an urgent problem requiring effective solutions. Due to the drawbacks of chemically synthesized antioxidants, there is a growing interest in natural antioxidants, particularly antioxidant peptides. Methods: By reviewing recent literature on antioxidant peptides, particularly those extracted from various parts of fish, summarize which fish by-products are more conducive to the extraction of antioxidant peptides and elaborate on their characteristics. Results: This article summarizes recent advancements in extracting antioxidant peptides from fish processing by-products, Briefly introduced the purification and identification process of antioxidant peptides, specifically focusing on the extraction of antioxidant peptides from various fish by-products. Additionally, this article comprehensively reviews the relationship between amino acid residues that compose antioxidant peptides and their potential mechanisms of action. It explores the impact of amino acid types, molecular weight, and structure–activity relationships on antioxidant efficacy. Conclusions: Different amino acid residues can contribute to the antioxidant activity of peptides by scavenging free radicals, chelating metal ions, and modulating enzyme activities. The smaller the molecular weight of the antioxidant peptide, the stronger its antioxidant activity. Additionally, the antioxidant activity of peptides is influenced by specific amino acids located at the C-terminus and N-terminus positions. Simultaneously, this review provides a more systematic analysis and a broader perspective based on existing research, concluded that fish viscera are more favorable for the extraction of antioxidant peptides, providing new insights for the practical application of fish by-products. This could increase the utilization of fish viscera and reduce the environmental pollution caused by their waste, offering valuable references for the study and application of antioxidant peptides from fish by-products. [ABSTRACT FROM AUTHOR]

Background: The goal of our research was to determine the effects of plant-based dietary patterns on sleep health among older adults and to examine the parallel mediated effects of anxiety and depression. Methods: This investigation utilized data obtained from the 2018 Chinese Longitudinal Healthy Longevity Survey (CLHLS) and contained 6853 participants. Logistic regression and the restricted cubic splines (RCSs) model were employed to examine how plant-based dietary patterns affect sleep health. Additionally, Amos 26.0 was used to construct a structural equation model to examine the parallel mediated effects of anxiety and depression. Results: A higher plant-based diet index (PDI) was connected to higher odds of better sleep quality (OR = 1.209, 95% CI: 1.039–1.407) and sleep duration (OR = 1.241, 95% CI: 1.072–1.437). Conversely, an elevated unhealthy plant-based diet index (uPDI) was correlated with a lower likelihood of sleep quality (OR = 0.678, 95% CI: 0.574–0.800) and sleep duration (OR = 0.762, 95% CI: 0.647–0.896). The RCSs regression further identified a significant dose–response relationship. Mediation analysis confirmed that anxiety and depression partially mediate the relationship between plant-based diets and sleep health. Conclusions: Our study exhibited significant correlations between plant-based diets and sleep health in the elderly. Depression and anxiety were determined as parallel mediators between plant-based diets and sleep health. Controlling early dietary patterns and affective disorder could help improve sleep quality in older adults. [ABSTRACT FROM AUTHOR]

Background: It is uncertain whether the weekend warrior pattern is associated with all-cause mortality among adults living with type 2 diabetes. This study explored how the 'weekend warrior' physical activity (PA) pattern was associated with all-cause mortality among adults living with type 2 diabetes. Methods: This prospective cohort study investigated US adults living with type 2 diabetes in the National Health and Nutrition Examination Survey (NHANES). Mortality data was linked to the National Death Index. Based on self-reported leisure-time and occupational moderate-to-vigorous PA (MVPA), participants were categorized into 3 groups: physically inactive (< 150 min/week of MVPA), weekend warrior (≥ 150 min/week of MVPA in 1 or 2 sessions), and physically active (≥ 150 min/week of MVPA in 3 or more sessions). Results: A total of 6067 participants living with type 2 diabetes [mean (SD) age, 61.4 (13.5) years; 48.0% females] were followed for a median of 6.1 years, during which 1206 deaths were recorded. Of leisure-time and occupational activity, compared with inactive individuals, hazard ratios (HRs) for all-cause mortality were 0.49 (95% CI 0.26–0.91) and 0.57 (95% CI 0.38–0.85) for weekend warrior individuals, and 0.55 (95% CI 0.45–0.67) and 0.64 (95% CI 0.53–0.76) for regularly active individuals, respectively. However, when compared leisure-time and occupational weekend warrior with regularly active participants, the HRs were 0.82 (95% CI 0.42–1.61) and 1.00 (95% CI 0.64–1.56) for all-cause mortality, respectively. Conclusions: Weekend warrior PA pattern may have similar effects on lowering all-cause mortality as regularly active pattern among adults living with type 2 diabetes, regardless of leisure-time or occupational activity. Therefore, weekend warrior PA pattern may be sufficient to reduce all-cause mortality for adults living with type 2 diabetes. [ABSTRACT FROM AUTHOR]

Accurately predicting industrial electricity consumption is of paramount importance for optimizing energy management and operational efficiency. Traditional forecasting approaches face significant challenges in capturing the complex factors influencing industrial electricity consumption, often due to the inadequate representation of correlations, thus limiting their predictive capabilities. To overcome these limitations, we propose a novel graph-based forecasting model termed Industry–Geography Time Series Forecasting Model (IG-TFM). Our approach leverages historical electricity consumption data and geographical information relevant to similar industries to construct an industry–geography relationship graph. This graph serves as the foundation of a comprehensive network that encompasses all industries of interest, allowing us to identify sectors closely associated with the target industry. The structured graph data are then processed within a graph convolutional neural network framework, which effectively captures the impact of geographical location, industry similarities, and inter-industry relationships on electricity consumption patterns. Utilizing this enriched representation, we develop our IG-TFM for accurate time series forecasting of industrial electricity consumption. Experiments conducted on real-world data, including 31 industries across 9 cities in a southern province of China, demonstrate the significant advantages of our proposed method across key performance indicators such as the Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). These findings underscore the importance and efficacy of employing complex networks to encode sequence-related information, thereby substantially improving prediction accuracy in industrial electricity consumption forecasting. [ABSTRACT FROM AUTHOR]