Aim: To identify the psychological change process of the registered nurses who worked in the epicenter of the COVID-19 outbreak. Background: The pandemic of COVID-19 has continued to pose an unprecedented threat and challenge to people’s health around the world. Nurses are at high risk because they work within the closest proximity to patients. Understanding nurses’ psychological change process during the care for patients with COVID-19 is imperative for healthcare leaders. Methods: This was a qualitative descriptive study that took place in a hospital in Wuhan, China, the epicenter of the COVID-19 epidemic, from February 9^th to March 15^th, 2020. Using purposive sampling, we interviewed 23 nurses. Data were analyzed using Colaizzi’s method of data analysis to find, understand, and describe nurses’ experiences. Results: The psychological change process of frontline nurses included three stages, early, middle, and later stages. The psychological characteristics of each period were ambivalence, emotional exhaustion, and energy renewal, respectively. Nurse leaders were anchors in facilitating frontline nurses’ psychological adaptation. Conclusions: In the past month, the psychological characteristics of nurses changed over time. The study indicated the necessity for nurse leaders to implement intervention programs based on nurses’ psychological characteristics in different periods to promote nurses’ health during this critical time period.

Wound healing is a dynamic and complex process in which the microenvironment at the wound site plays an important role.

Disposable medical protective clothing for 2019-nCoV mainly consists of stacked layers with nanopore films, polymer coated nonwoven fabrics and melt-blown nonwoven fabrics against anti-microbial and anti-liquid penetration. However, such structures lack moisture permeability and breathability leading to an uncomfortable, stuffy wearing experience. Here, we propose a novel medical protective clothing material with a superabsorbent layer to enhance moisture absorption. Poly(acrylic acid-co-acrylamide)/polyvinyl alcohol superabsorbent fibers (PAAAM/PVA fibers) were prepared via wet spinning. And the superabsorbent composite layer was stacked from PAAAM/PVA fibers, bamboo pulp fibers (BPF) and ethylene-propylene side by side fibers (ESF). The novel disposable medical protective composite fabric was obtained through gluing the superabsorbent layer to the inner surface of strong antistatic polypropylene nonwoven fabric. The resultant composite fabric possesses excellent absorption and retention capacity for sweat, up to 12.3 g/g and 63.8%, and a maximum hygroscopic rate of 1.04 g/h, higher than that of the conventional material (only 0.53 g/h). The moisture permeability of the novel material reached 12,638.5 g/(m² d), which was 307.6% of the conventional material. The novel material can effectively reduce the humidity inside the protective clothing and significantly improve the comfort of medical staff.

Background: Global fertility declines have become an inevitable trend, and many countries are adopting policies to drive fertility increases. Fertility intention plays an important role in predicting fertility behavior. The Chinese government has recently issued the ‘three-child’ policy, and there is still little research on the third birth intention of the childbearing-age population. Therefore, the aim of this study is to investigate the prevalence and related reasons of third birth intention in the childbearing-age population in mainland China, and analyze the sociodemographic differences. Method: A cross-sectional survey was conducted in mainland China from June to July 2021. A total of 15,332 childbearing-age participants responded and completed the Fertility Intention Questionnaire online through the Wenjuanxing Platform. Data were explored and analyzed by SPSS (version 22.0) software. Descriptive statistics were used to describe the current situation and reasons of third birth intention. Binary logistic regression analysis was applied to assess the influencing factors in the sociodemographic level. Results: The mean age of the participants was 32.9 ± 5.94 years. Only 12.2% of participants reported having third birth intention. The subjective norm of having both son and daughter (22.0%) and busy at work (29.2%) accounted for the largest proportion in the reasons of acceptance and rejection, respectively. Age has negative impact on third birth intention (OR = 0.960). Men were 2.209 times more likely to have three children than women (P < 0.001). With the improvement of education and family monthly income, the birth intention shows a downward trend. Compared with Han nationalities, first marriage and city residents, the ethnic minorities, remarriage and rural residents have stronger birth intention (all P < 0.05). And individuals with two existing children are inclined to have the third child (OR = 1.839). Conclusion: The third birth intention in the childbearing-age population in China is still low after the announcement of the three-child policy. It is necessary to create a favorable fertility context for childbearing-age group with high level of third birth intention, like younger, male, minority, remarriage, with lower education and family monthly income, living in rural and two existing children. Furthermore, removing barriers for those unintended is also prominent to ensure the impetus of policy.

Background: Gestational diabetes mellitus (GDM) is one of the critical causes of adverse perinatal outcomes. A reliable estimate of GDM in early pregnancy would facilitate intervention plans for maternal and infant health care to prevent the risk of adverse perinatal outcomes. This study aims to build an early model to predict GDM in the first trimester for the primary health care centre.Methods: Characteristics of pregnant women in the first trimester were collected from eastern China from 2017 to 2019. The univariate analysis was performed using SPSS 23.0 statistical software. Characteristics comparison was applied with Mann-Whitney U test for continuous variables and chi-square test for categorical variables. All analyses were two-sided withp< 0.05 indicating statistical significance. Thetrain_test_splitfunction in Python was used to split the data set into 70% for training and 30% for test. The Random Forest model and Logistic Regression model in Python were applied to model the training data set. The 10-fold cross-validation was used to assess the model’s performance by the areas under the ROC Curve, diagnostic accuracy, sensitivity, and specificity.Results: A total of 1,139 pregnant women (186 with GDM) were included in the final data analysis. Significant differences were observed in age (Z=−2.693,p=0.007), pre-pregnancy BMI (Z=−5.502,p<0.001), abdomen circumference in the first trimester (Z=−6.069,p<0.001), gravidity (Z=−3.210,p=0.001), PCOS (χ²=101.024,p<0.001), irregular menstruation (χ²=6.578,p=0.010), and family history of diabetes (χ²=15.266,p<0.001) between participants with GDM or without GDM. The Random Forest model achieved a higher AUC than the Logistic Regression model (0.777±0.034 vs 0.755±0.032), and had a better discrimination ability of GDM from Non-GDMs (Sensitivity: 0.651±0.087 vs 0.683±0.084, Specificity: 0.813±0.075 vs 0.736±0.087).Conclusions: This research developed a simple model to predict the risk of GDM using machine learning algorithm based on pre-pregnancy BMI, abdomen circumference in the first trimester, age, PCOS, gravidity, irregular menstruation, and family history of diabetes. The model was easy in operation, and all predictors were easily obtained in the first trimester in primary health care centres.