Prevalence and predictors of prenatal depression during the COVID-19 pandemic: A multistage observational study in Beijing, China.

OBJECTIVE: While growing psychological health issues among pregnant women during the COVID-19 pandemic have been clearly validated, most research was conducted in countries with relatively lax quarantine measures. This study aimed to compare the prevalence of prenatal depression among pre-, peak-, and post-COVID-19 in Beijing, the region with a stringent response policy in China. We also explore predictors of prenatal depression throughout the outbreak. METHODS: We investigated prenatal depression among 742 pregnant women who received antenatal checkups in Beijing from March 28, 2019 to May 07, 2021 using the Edinburgh Postnatal Depression Scale and associative demographic, pregnancy-related, and psychosocial characteristics were measured. The phase was divided into pre-, peak-, and post-COVID-19 in light of the trajectory of COVID-19. Pearson's Chi-square test was used after the examination of confounders homogeneity. The bivariable and multivariable logistic regression was conducted to explore predictors. RESULTS: The pooled prevalence of prenatal depression was 11.9% throughout the COVID-19 pandemic. Rates at different phases were 10.6%, 15.2%, and 11.1% respectively and no significant difference was observed. Multivariable logistic regression revealed that history of mental illness, number of boy-preference from both pregnant women and husband's family, social support, occupation, and living space were independent predictors of prenatal depression in Beijing. CONCLUSION: Our data suggested that the impact of this pandemic on prenatal depression in Beijing appears to be not significant, which will strengthen confidence in adhering to current policy for decision-makers and provide important guidance for the development of major outbreak control and management policies in the future. Our findings may also provide a more efficient measure to identify high-risk pregnant women for professionals and help raise gender equity awareness of pregnant women and their husbands' families. Future studies should focus on the value of targeted care and family relations on the mental health of pregnant women.

Asymmetric Non-Fullerene Acceptor Derivatives Incorporated Ternary Organic Solar Cells.

Incorporating ITIC derivatives as guest acceptors into binary host systems is an effective strategy for constructing high-performance ternary organic solar cells (TOSCs). In this work, we introduced A-D-A type ITIC derivatives PTBTT-4F (asymmetric) and PTBTP-4F (symmetric) into the PM6:BTP-BO-4F (Y6-BO) binary blend and investigated the impacts of two guest acceptors on the performance of TOSCs. Differentiated device performance was observed, although PTBTT-4F and PTBTP-4F presented similar chemical structures and comparable absorptions. The PTBTT-4F ternary devices exhibited an improved power conversion efficiency (PCE) of 17.67% with increased open circuit (VOC) and current density (JSC), whereas the PTBTP-4F-based ternary devices yielded a relatively lower PCE of 16.34%. PTBTT-4F showed much better compatibility with the host acceptor BTP-BO-4F, so that they formed a well-mixed alloy phase state; more precise phase separation and increased crystallinity were thus induced in the ternary blends, leading to reduced molecular recombination and improved charge mobilities, which contributed to improved fill factors of the ternary devices. In addition, the optimized PTBTT-4F devices exhibited good performance tolerance of the photoactive layer thickness, as they even delivered a PCE of 15.25% when the active layer was as thick as up to ∼300 nm.

Functional gradient structural design of customized diabetic insoles.

Diabetic foot is a common and serious complication of diabetes, largely due to sensory neuropathy and excessive mechanical stresses. Studies have shown that reducing the contact pressure can effectively lower the incidence of diabetic foot. A new design method is proposed in this study for optimizing the stress distribution of the contact surfaces between the foot and the insole by applying functional gradient structural properties to the insole. Finite element analysis was employed for studying the contact mechanics, which laid the foundation for modulus readjustment during the optimization process. The moduli of the materials were correlated to the properties of the structural porous units. The customized insoles were manufactured using additive manufacturing technology and put into mechanical test. Results show that the designed insole helps in increasing the foot contact area by approximately 30% and reducing the peak contact pressure by 35%. Hence, the proposed method can be used to design customized insoles, particularly diabetic insoles, by offering better contact mechanics and good potential for reducing the severity of diabetic foot. The methodology is equally applicable to other designs involving optimization of material properties.