Eliminating Voids and Residual PbI2 beneath a Perovskite Film via Buried Interface Modification for Efficient Solar Cells.

The commercialization process of perovskite solar cells (PSCs) is markedly restricted by the power conversion efficiency (PCE) and long-term stability. During fabrication and operation, the bottom interface of the organic-inorganic hybrid perovskite layer frequently exhibits voids and residual PbI2, while these defects inevitably act as recombination centers and degradation sites, affecting the efficiency and stability of the devices. Therefore, the degradation and nonradiative recombination originating from the buried interface should be thoroughly resolved. Here, we report a multifunctional passivator by introducing malonic dihydrazide as an interfacial chemical bridge between the electron transport layer and the perovskite (PVK) layer. MADH with hydrazine groups improves the surface affinity of SnO2 and provides nucleation sites for the growth of PVK, leading to the reduced residual PbI2 and the voids resulting from the inhomogeneous solvent volatilization at the bottom interface. Meanwhile, the hydrazine group and carbonyl group synergistically coordinate with Pb2+ to improve the crystal growth environment, reducing the number of Pb-related defects. Eventually, the PCE of the PSCs is significantly enhanced benefiting from the reduced interfacial defects and the increased carrier transport. Moreover, the reductive nature of hydrazide further inhibits I2 generation during long-term operation, and the device retains 90% of the initial PCE under a 1 sun continuous illumination exposure of 700 h.

Lockdown and psychological stress in Wuhan, China.

BACKGROUND: Wuhan was the epicenter of the Coronavirus Disease 2019 (COVID-19), outbreak in China. We aimed at surveying the general public in China to better understand their levels of psychological state and its influencing factors after the Wuhan shutdown on January 23. METHODS: The cross-sectional survey was conducted online and 4,701 respondents participated in this survey. Of them, 3,803 respondents were considered for final analysis. Data on subjective indicators of daily life changes were collected, and individual scores on changes in anxiety, depression, and stress were tested by 8-item, 11-item, and 6-item questionnaires, respectively. RESULTS: Multivariable regression analyses showed that living in the rural areas, living in the other regions except Hubei, and higher education were independent correlates of less negative emotions. Besides, the level of attention, self-assessed infection risk, impact on the daily life and mental health help-seeking tended to be positively associated with the scores of anxiety, depression, and stress. CONCLUSIONS: City of residence, education, marital status, monthly income, level of attention, self-assessed infection risk, impact on daily life and mental health help-seeking were important correlates of anxiety, depression, and stress scores.

Analysis of blood lipid changes and influencing factors in physical examination population of a city in central China.

Purpose: The prevalence of cardiovascular diseases (CVDs) associated with lipid levels is increasing worldwide. Our purpose is to analyze the distribution level and influencing factors of lipid in the whole population and to put forward suggestions for preventing abnormal lipid levels. Methods: The study was based on a sample of 91,480 Chinese who participated in a nationwide physical examination program in Wuhan, a midland city in China, in 2018. The distribution of blood lipid in the population was observed using average, and the relationship between the influencing factors and blood lipid level was observed by quantile regression (QR). Results: A total of 91,480 people were evaluated in this study, among which 59,165 (64.68%) were female with a mean age of 51.71 ± 10.82 years. QR results showed that different physical examination indexes had different effects on lipid levels. Fasting plasma glucose (FBG) has the largest QR coefficient and BMI had positive effects on total cholesterol (TC), triglyceride (TG), and low-density lipoprotein cholesterol (LDL-C). In males, age has a positive influence on TC, LDL-C, and high-density lipoprotein cholesterol (HDL-C), while in females, age has a positive influence on all four indexes. Conclusion: We found that the TC and LDL-C levels of females were more susceptible to age than males, and the lipid levels of older females were higher than males. BMI has a greater effect on lipid levels in males than in females. Regardless of gender should pay attention to dyslipidemia caused by diabetes and abnormal liver function.

Progression of Vision in Chinese School-Aged Children Before and After COVID-19.

Objectives: To investigate the changes of vision, including the prevalence of myopia, hyperopia, poor vision, and the spherical equivalent refraction (SER), in school-aged children before and after the pandemic of Coronavirus Disease 2019 (COVID-19). Methods: A school-based vision screening study was performed on children in 133 primary schools in Wuhan. This study was conducted in 4 consecutive years (2018-2021). Results: A total of 468,094 children (936,188 eyes) were recruited, 255,863 (54.7%) were boys. The SER decreased in 2020 compared to other years after the age of 10. A positive myopia shift was found in younger children aged 6 (0.1 D), 7 (0.05D), and 8 (0.03 D) in 2020 compared to 2019. The progression of vision has improved slightly in 2021. Among the students included in the study, 33.7% were myopia. Conclusion: The vision of older children decreased significantly during the COVID-19. After the pandemic, there is still a high risk for them. In the future, the focus on vision prevention and control should move forward to preschool children.

The nomogram for the prediction of overall survival in patients with metastatic lung adenocarcinoma undergoing primary site surgery: A retrospective population-based study.

Background: Lung adenocarcinoma (LUAD) is the most common type of Non-small-cell lung cancer (NSCLC). Distant metastasis of lung adenocarcinoma reduces the survival rate. we aim to develop a nomogram in order to predict the survival of patients with metastatic lung adenocarcinoma. Methods: We retrospectively collected patients who were initially diagnosed as metastatic LUAD from 2010 to 2015 from SEER database. Based on the multivariate and univariate Cox regression analysis of the training cohorts, independent prognostic factors were assessed. The nomogram prediction model was then constructed based on these prognostic factors to predict the overall survival at 12, 24 and 36 months after surgery. Nomogram were identified and calibrated by c-index, time-dependent receiver operating characteristic curve (time-dependent AUC) and calibration curve. Decision curve analysis (DCA) was used to quantify the net benefit of the nomogram at different threshold probabilities, and to better compare with the TNM staging system, we calculated the c-index of this nomogram as well as the net reclassification improvement (NRI) and the integrated discrimination improvement (IDI). Result: A total of 1102 patients with metastatic LUAD who met the requirements were included for analysis. They were randomly divided into 774 in the training cohorts and 328 in the validation cohorts. As can be seen from the calibration plots, the predicted nomogram and the actual observations in both of the training and validation cohorts were generally consistent. The time dependent AUC values of 12 months, 24 months and 36 months were 0.707, 0.674 and 0.686 in the training cohorts and 0.690, 0.680 and 0.688 in the verification cohorts, respectively. C-indexes for the training and validation cohorts were 0.653 (95%CI 0.626-0.68)and 0.663 (95%CI 0.626-1), respectively. NRI and IDI show that the model is more clinical applicable than the existing staging system. In addition, our risk scoring system based on Kaplan Meier (K-M) survival curve can accurately divide patients into three hierarchy risk groups. Conclusion: This has led to the development and validation of a prognostic nomogram to assist clinicians in determining the prognosis of patients with metastatic lung adenocarcinoma after primary site surgery.

Effects of age, period, and cohort on the prevalence of frailty in Chinese older adults from 2002 to 2014.

Background: Currently, longitudinal studies on frailty are in an early stage, particularly in low- and middle-income countries. Only one study was conducted in Hong Kong to examine age-period-cohort effects on the prevalence of frailty among Chinese older adults. Objectives: This study aims to shed light on the prevalence trajectory of frailty among older adults in mainland China through the APC model and to analyze the effects of age, period, and cohort on the prevalence trajectory. Methods: The sample for this study was older adults aged 65-109 years old from the 2002 to 2014 Chinese Longitudinal Healthy Longevity Survey (CLHLS). Frailty status was measured by Rockwood FI. An age-period-cohort model was used to describe the effects of age, period, and cohort on the prevalence trajectory of frailty. Results: The prevalence of frailty among Chinese older adults changed significantly with age, period, and cohort. Furthermore, the effect of age was much stronger than the effect of period and cohort. The prevalence of frailty in the 101-103 and 104-106 age groups was 8.998 (95% CI 13.667-5.924) and 8.699 (95% CI 13.037-5.805) times higher than the in the 65-67 age group, respectively. The sensitivity analysis based on Fried's frailty phenotype showed similar results, confirming the robustness of our findings. Conclusion: All of the age effect reflecting the individual aging process, period effect reflecting change in the social environment, and birth cohort effect reflecting different generations could influence the prevalence of frailty at the population level. In contrast, the age effect was the main effect.

A Latent Class Analysis of Student Eye Care Behavior: Evidence From a Sample of 6-17 Years Old in China.

Purpose: To understand the latent classes and distribution of an adolescent eye care behavior, and to provide a basis for the formulation of appropriate adolescent vision health management interventions. Methods: Information on eye behavior and eye health of primary and secondary school students in Wuhan was collected by multistage stratified cluster sampling. The latent class analysis (LCA) method was used to analyze the students' eye care behavior, and the latent class model (LCM) was built. Results: A total of 6,130 students were enrolled in this study, of which 53.56% were males, aged from 6 to 17 years old, with an average age of 10.33 ± 2.60. The latent class results classified the adolescents' eye care behaviors into bad behaviors, moderate behaviors, and healthy behaviors. The model fitting results were as follows: Akaike Information Criterion (AIC) was 36,698.216, Bayesian Information Criterion (BIC) was 36,906.565, Adjusted Bayesian Information Criterion (aBIC) was 36,808.056, and entropy was 0.838.Compared with the healthy behaviors class, the bad behaviors class was more prevalent in high schools (p = 0.003), non-demonstration schools (p = 0.001), and most of this group had astigmatism (p = 0.002). The moderate behaviors class predominately consisted of females (p = 0.001), 15-17 years old (p = 0.005, 6~8 years old as the reference), from non-demonstration schools (p < 0.001), and most had myopia (p = 0.009). Conclusion: There were differences in basic demographic characteristics, visual acuity development level, and family visual environment among different classes. In the management and intervention of an adolescent vision health, we should continue to promote the visual health management of adolescents based on visual monitoring and realize the early intervention and guidance of individuals in bad behaviors class.

Frailty Risk Prediction Model among Older Adults: A Chinese Nation-Wide Cross-Sectional Study.

OBJECTIVES: Numerous studies have been performed on frailty, but rarely do studies explore the integrated impact of socio-demographic, behavioural and social support factors on frailty. This study aims to establish a comprehensive frailty risk prediction model including multiple risk factors. METHODS: The 2018 wave of the Chinese Longevity and Health Longitudinal Survey was used. Univariate and multivariate logistic regressions were performed to identify the relationship between frailty and multiple risk factors and establish the frailty risk prediction model. A nomogram was utilized to illustrate the prediction model. The area under the receiver operating characteristic curve (AUC), Hosmer-Lemeshow test and calibration curve were used to appraise the prediction model. RESULTS: Variables from socio-demographic, social support and behavioural dimensions were included in the final frailty risk prediction model. Risk factors include older age, working as professionals and technicians before 60 years old, poor economic condition and poor oral hygiene. Protective factors include eating rice as a staple food, regular exercise, having a spouse as the first person to share thoughts with, doing physical examination once a year and not needing a caregiver when ill. The AUC (0.881), Hosmer-Lemeshow test (p = 0.618), and calibration curve showed that the risk prediction model was valid. CONCLUSION: Risk factors from socio-demographic, behavioural and social support dimensions had a comprehensive effect on frailty, further supporting that a comprehensive and individualized intervention is necessary to prevent frailty.

Risk Factors Analysis of Bone Mineral Density Based on Lasso and Quantile Regression in America during 2015-2018.

This study aimed to explore the risk factors of bone mineral density (BMD) in American residents and further analyse the extent of effects, to provide preventive guidance for maintenance of bone health. A cross-sectional study analysis was carried out in this study, of which data validity was identified and ethics approval was exempted based on the National Health and Nutrition Examination Survey (NHANES) database. Candidates' demographics, physical examination, laboratory indicators and part of questionnaire information were collected and merged from NHANES in 2015-2016 and 2017-2018. The least absolute shrinkage selection operator (lasso) was used to select initial variables with "glmnet" package of R, quantile regression model to analyze influence factors of BMD and their effects in different sites with "qreg" code in Stata. Among 2937 candidates, 17 covariates were selected by lasso regression (λ = 0.00032) in left arm BMD, with 16 covariates in left leg BMD (λ = 0.00052) and 14 covariates in total BMD (λ = 0.00065). Quantile regression results displayed several factors with different coefficients in separate sites and quantiles: gender, age, educational status, race, high-density lipoprotein (HDL), total cholesterol (TC), lead, manganese, ethyl mercury, smoking, alcohol use and body mass index (BMI) (p < 0.05). We constructed robust regression models to conclude that some demographic characteristics, nutritional factors (especially lipid levels, heavy metals) and unhealthy behaviors affected BMD in varying degrees. Gender and race differences, Low-fat food intake and low exposure to heavy metals (mostly lead, manganese and mercury) should be considered by both clinical doctors and people. There is still no consensus on the impact of smoking and alcohol use on bone mineral density in our study.