Early detection of mental illness for women suffering high-risk pregnancies : an explorative study on self-perceived burden during pregnancy and early postpartum depressive symptoms among Chinese women hospitalized with threatened preterm labour

Background The mental health of pregnant women, particularly those with elevated risks, has been an issue of global concern. Thus far, few studies have addressed the mental health of pregnant women with threatened preterm labour (TPL). This study investigated the prevalence of self-perceived burden (SPB) among Chinese women hospitalized due to TPL during pregnancy and early postpartum depressive disorders, exploring the effect of SPB and other potential risk factors on the early signs of postpartum depressive disorders. Methods A self-reported survey was conducted in the obstetrics department of Anhui Provincial Hospital, China. Women hospitalized with TPL were approached 1 week after delivery. One hundred fifty women were recruited from January 2017 to December 2017. The Self-Perceived Burden Scale (SPBS) and Edinburgh Postnatal Depression Scale (EPDS) were the main measures. Descriptive statistics, Spearman correlations, and a multiple logistic regression were employed for data analysis. Results SPB and early postpartum depressive disorders were commonly experienced by Chinese women hospitalized with TPL, and SPB was positively and significantly correlated with depressive symptoms. A multiple logistic regression analysis revealed that for the women hospitalized with TPL during pregnancy, the emotional aspect of SPB (OR = 1.42, 95% CI = 1.11-1.83, p = 0.006), age (OR = 1.14, 95% CI = 1.02-1.27, p = 0.023), occupation (OR = 3.48, 95% CI = 1.18-10.20, p = 0.023), the history of scarred uterus (OR = 7.96, 95% CI = 1.49-42.48, p = 0.015), the delivery mode of the present birth (OR = 6.19, 95% CI = 1.72-22.30, p = 0.005), and family support during pregnancy (OR = 0.60, 95% CI = 0.45-0.82, p = 0.001) were significant factors predicting early postpartum depressive symptoms. Conclusion This study indicates that SPB and early postpartum depressive disorders are prevalent mental issues among Chinese women hospitalized with TPL, and that SPB, especially perceived emotional burden, is a strong predictor of early postpartum depressive disorders. Our study suggests the necessity of paying attention to mental health issues, e.g. SPB and postpartum depressive symptoms among hospitalized women with TPL, and providing appropriate interventions at the prenatal stage to prevent adverse consequences.Peer reviewe

Investigation of magnetic resonance coupling circuit topologies for wireless power transmission

© 2019 Wiley Periodicals, Inc. Magnetic resonance coupling circuits have four general topologies; however, there is a lack of comprehensive theoretical analysis with experimental verification for each of these topologies regarding their attractiveness for wireless power transfer (WPT). This article provides this for each of the four topologies to fully understand their differences and allow the selection of the most appropriate type based on system requirements. In addition, a problem associated with the resonance coupling method is the phenomenon of frequency splitting, which can lead to a high-power transfer efficiency but low-load power at the resonant frequency. Reasons for frequency splitting and methods of circumventing the problem will be illustrated in this article. Of the four topologies, the series-parallel (SP) (input-output) circuit configuration is the most efficient for the realization of a WPT system with a large load impedance, in terms of achieving both a high power transfer efficiency and high-load power

Indirect Economic Impact Incurred by Haze Pollution: An Econometric and Input–Output Joint Model

Econometrics and input–output models have been presented to construct a joint model (i.e., an EC + IO model) in the paper, which is characterized by incorporating the uncertainty of the real economy with the detailed departmental classification structure, as well as adding recovery period variables in the joint model to make the model dynamic. By designing and implementing a static model, it is estimated that the indirect economic loss for the transportation sector caused by representative haze pollution of Beijing in 2013 was 23.7 million yuan. The industrial-related indirect losses due to the direct economic losses incurred by haze pollution reached 102 million yuan. With the constructed dynamic model, the cumulative economic losses for the industrial sectors have been calculated for the recovery periods of different durations. The results show that: (1) the longer the period that an industrial department returns to normal output after haze pollution has impacted, the greater the cumulative economic loss will be; (2) when the recovery period is one year, the cumulative economic loss value computed by the dynamic EC + IO model is much smaller than the loss value obtained by the static EC + IO model; (3) the recovery curves of industrial sectors show that the recovery rate at the early stage is fast, while it is slow afterwards. Therefore, the governance work after the occurrence of haze pollution should be launched as soon as possible. This study provides a theoretical basis for evaluating the indirect economic losses of haze pollution and demonstrates the value of popularization and application

An Adaptive Kalman Filtering Approach to Sensing and Predicting Air Quality Index Values

In recent years, Air Quality Index (AQI) have been widely used to describe the severity of haze and other air pollutions yet suffers from inefficiency and compatibility on real-time perception and prediction. In this paper, an Auto-Regressive (AR) prediction model based on sensed AQI values is proposed, where an adaptive Kalman Filtering (KF) approach is fitted to achieve efficient prediction of the AQI values. The AQI values were collected monthly from January 2018 to March 2019 using a WSN-based network, whereas daily AQI values started to be collected from October 1, 2018 to March 31, 2019. These data have been used for creation and evaluation purposes on the prediction model. According to the results, predicted values have shown high accuracy compared with the actual sensed values. In addition, when monthly AQI values were used, it has depicted higher accuracy compared to the daily ones depending on the experimental results. Therefore, the hybrid AR-KF model is accurate and effective in predicting haze weather, which has practical significance and potential value

PM2.5 Pollution and Inhibitory Effects on Industry Development: A Bidirectional Correlation Effect Mechanism

In this paper, a vector autoregression (VAR) model has been constructed in order to analyze a two-way mechanism between PM2.5 pollution and industry development in Beijing via the combination of an impulse response function and variance decomposition. According to the results, long-term equilibrium interconnection was found between PM2.5 pollution and the development of primary, secondary, and tertiary industries. One-way Granger causalities were found in the three types of industries shown to contribute to PM2.5 pollution, though the three industries showed different scales of influences on the PM2.5 pollution that varied for about 1–2 years. The development of the primary and secondary industries increased the emission of PM2.5, but the tertiary industry had an inhibitory effect. In addition, PM2.5 pollution had a certain inhibitory effect on the development of the primary and secondary industries, but the inhibition of the tertiary industry was not significant. Therefore, the development of the tertiary industry can contribute the most to the reduction of PM2.5 pollution. Based on these findings, policy-making recommendations can be proposed regarding upcoming pollution prevention strategies

Intergenomic and epistatic interactions control free radical mediated pancreatic β-cell damage.

Alloxan (AL)-generated Reactive Oxygen Species (ROS) selectively destroy insulin-producing pancreatic β-cells. A previous genome-wide scan (GWS) using a cohort of 296 F2 hybrids between NOD (AL-sensitive) and ALR (AL-resistant) mice identified linkages contributing to β-cell susceptibility or resistance to AL-induced diabetes on Chromosomes (Chr) 2, 3, 8, and a single nucleotide polymorphism i

A hybrid CNN-LSTM model for predicting PM2.5 in Beijing based on spatiotemporal correlation

Long-term exposure to air environments full of suspended particles, especially PM2.5, would seriously damage people's health and life (i.e., respiratory diseases and lung cancers). Therefore, accurate PM2.5 prediction is important for the government authorities to take preventive measures. In this paper, the advantages of convolutional neural networks (CNN) and long short-term memory networks (LSTM) models are combined. Then a hybrid CNN-LSTM model is proposed to predict the daily PM2.5 concentration in Beijing based on spatiotemporal correlation. Specifically, a Pearson's correlation coefficient is adopted to measure the relationship between PM2.5 in Beijing and air pollutants in its surrounding cities. In the hybrid CNN-LSTM model, the CNN model is used to learn spatial features, while the LSTM model is used to extract the temporal information. In order to evaluate the proposed model, three evaluation indexes are introduced, including root mean square error, mean absolute percent error, and R-squared. As a result, the hybrid CNN-LSTM model achieves the best performance compared with the Multilayer perceptron model (MLP) and LSTM. Moreover, the prediction accuracy of the proposed model considering spatiotemporal correlation outperforms the same model without spatiotemporal correlation. Therefore, the hybrid CNN-LSTM model can be adopted for PM2.5 concentration prediction

PM2.5 Pollution: Health and Economic Effect Assessment Based on a Recursive Dynamic Computable General Equilibrium Model

At present particulate matter (PM₂.₅) pollution represents a serious threat to the public health and the national economic system in China. This paper optimizes the whitening coefficient in a grey Markov model by a genetic algorithm, predicts the concentration of fine particulate matter (PM₂.₅), and then quantifies the health effects of PM₂.₅ pollution by utilizing the predicted concentration, computable general equilibrium (CGE), and a carefully designed exposure–response model. Further, the authors establish a social accounting matrix (SAM), calibrate the parameter values in the CGE model, and construct a recursive dynamic CGE model under closed economy conditions to assess the long-term economic losses incurred by PM₂.₅ pollution. Subsequently, an empirical analysis was conducted for the Beijing area: Despite the reduced concentration trend, PM₂.₅ pollution continued to cause serious damage to human health and the economic system from 2013 to 2020, as illustrated by various facts, including: (1) the estimated premature deaths and individuals suffering haze pollution-related diseases are 156,588 (95% confidence intervals (CI): 43,335–248,914)) and six million, respectively; and (2) the accumulated labor loss and the medical expenditure negatively impact the regional gross domestic product, with an estimated loss of 3062.63 (95% CI: 1,168.77–4671.13) million RMB. These findings can provide useful information for governmental agencies to formulate relevant environmental policies and for communities to promote prevention and rescue strategies