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Maternal antenatal depression (AD) is a nonpsychotic depressive episode during pregnancy that can harm both the pregnant woman and the fetus. This study aimed to investigate the intrinsic interrelationships between AD and its influencing factors by constructing a path model. This survey-based cross-sectional study included 1071 pregnant women who underwent pregnancy examinations in three hospitals in Nantong City, China, between February and June 2023. General information and information regarding maternal AD, pregnancy stress, prenatal anxiety, social support, marital satisfaction, sleep quality, and resilience were collected. Multiple linear regression analysis using SPSS 25.0 was employed to determine the factors influencing pregnancy depression, and Amos25.0 was used to construct a structural equation model. AD incidence was 19.4% (208/1071). The independent risk factors affecting AD in pregnant women have been integrated into the established path analysis model. The model demonstrated a good fit (χ2/DF = 1.238, comparative fit index = 0.999, goodness-of-fit index = 0.998, normed fit index = 0.996, adjusted goodness-of-fit index = 0.990, incremental fit index = 0.999, and root mean square error of approximation = 0.015). While prenatal anxiety (0.230) and hyperthyroidism (0.048) only had direct effects on AD, mental resilience was the biggest factor affecting AD, followed by pregnancy stress, marital satisfaction, prenatal anxiety, sleep quality, social support, and hyperthyroidism. Improved mental resilience, social support, sleep quality, and marital satisfaction; reduced pregnancy stress and prenatal anxiety; and effective hyperthyroidism treatment might reduce AD. This study underscored the significance of delivering actionable strategies and tangible assistance to pregnant women to reduce AD. [ABSTRACT FROM AUTHOR]

Objective: To translate the Maternal Postpartum Stress Scale (MPSS) into Chinese and validate its psychometric properties in postpartum women. Methods: A total of 406 postpartum women were recruited from six hospitals in Nantong, Jiangsu Province, China. Cronbach's α co-efficient, split-half reliability, and test-retest reliability were used to evaluate the reliability of the translated scale. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were used to evaluate the structural validity of the scale. The Edinburgh Postnatal Depression Scale, Depression Anxiety Stress Scale-21 anxiety dimension, and Perceived Stress Scale were used as calibration scales to measure the correlation of MPSS. All data were analyzed using SPSS 25.0 and Amos 24.0. Results: The Cronbach's α co-efficient of the Chinese version of MPSS and its three dimensions were 0.940 and 0.882–0.911, respectively. The split-half reliability was 0.825, and the test-retest reliability was 0.912. The scale's content validity index was 0.926. Three common factors were extracted from the EFA. The CFA validated the explored 3-factor structure, and the indicators were fitted well (χ2/Df = 2.167, comparative fit index = 0.918, Tucker–Lewis index = 0.907, incremental fit index = 0.919, and root mean square error of approximation = 0.075). Conclusion: The translated Chinese version of MPSS had suitable reliability and validity in assessing postpartum stress in Chinese women. The translated scale can also help with the early identification of postpartum stress and provide a scientific basis for the formulation of early personalized intervention measures. Overall, the scale has certain clinical value and practical significance for enhancing the physical and mental health of postpartum women. However, future studies including large, diverse populations are warranted. [ABSTRACT FROM AUTHOR]

This study investigated the changes in tea moisture content and shape features parameters during the roasting process of roasted green tea under different temperatures(70, 100 and 130 °C), leaf amounts (40, 70 and 100 kg), and cylinder speeds (25, 27.5 and 30 r/min), and developed a moisture content prediction model based on Random forest-Genetic algorithm-Back propagation(RF-GA-BP) neural network. Firstly, based on image processing technology, the shape parameters of tea leaves during the roasting process were automatically extracted, and the random forest algorithm was used to rank their importance to reduce the complexity of the model. Then, the activation function and the number of hidden layer nodes in the network structure were optimized. Finally, the initial weights and thresholds of the neural network were optimized using genetic algorithm to obtain the optimal parameters for constructing a moisture content prediction model during the roasting process. The results indicated that the importance of slightness was the highest, with a value of 0.865. The RF-GA-BP neural network model with mean absolute error, root mean square error, and coefficient of determination of 0.030, 0.036, and 0.915, respectively, was superior to the BP, GA-BP, and RF-BP neural network models, and could better predict the moisture content of tea during the roasting process. • Roasted green tea were imaged during roasting at different temperatures, leaf weights, cylinder speed. • Length, width, circularity, rectangularity, slightness, compactness and linearity were determined. • The random forest algorithm was used to rank the importance of shape features. • RF-GA-BP neural network model was constructed to predicted moisture content in the roasting process. [ABSTRACT FROM AUTHOR]