ObjectiveThe COVID-19 pandemic has become a major public health concern over the past 3 years, leading to adverse effects on front-line healthcare workers. This study aimed to develop a Body Mass Index (BMI) change prediction model among doctors and nurses in North China during the COVID-19 pandemic, and further identified the predicting effects of lifestyles, sleep quality, work-related conditions, and personality traits on BMI change.MethodsThe present study was a cross-sectional study conducted in North China, during May-August 2022. A total of 5,400 doctors and nurses were randomly recruited from 39 COVID-19 designated hospitals and 5,271 participants provided valid responses. Participants’ data related to social-demographics, dietary behavior, lifestyle, sleep, personality, and work-related conflicts were collected with questionnaires. Deep Neural Network (DNN) was applied to develop a BMI change prediction model among doctors and nurses during the COVID-19 pandemic.ResultsOf participants, only 2,216 (42.0%) individuals kept a stable BMI. Results showed that personality traits, dietary behaviors, lifestyles, sleep quality, burnout, and work-related conditions had effects on the BMI change among doctors and nurses. The prediction model for BMI change was developed with a 33-26-20-1 network framework. The DNN model achieved high prediction efficacy, and values of R2, MAE, MSE, and RMSE for the model were 0.940, 0.027, 0.002, and 0.038, respectively. Among doctors and nurses, the top five predictors in the BMI change prediction model were unbalanced nutritional diet, poor sleep quality, work-family conflict, lack of exercise, and soft drinks consumption.ConclusionDuring the COVID-19 pandemic, BMI change was highly prevalent among doctors and nurses in North China. Machine learning models can provide an automated identification mechanism for the prediction of BMI change. Personality traits, dietary behaviors, lifestyles, sleep quality, burnout, and work-related conditions have contributed to the BMI change prediction. Integrated treatment measures should be taken in the management of weight and BMI by policymakers, hospital administrators, and healthcare workers.

ObjectiveTo provide the most recent national estimates for the consumption of aquatic products and meats among Chinese residents.MethodsThis study was conducted in 14 provinces of China, using a multi-stage stratified random cluster sampling method and a population-proportional sampling procedure. Aquatic products and meats consumption was measured by a 3-day, 24-h dietary recall. Chinese residents aged 3 years and above (n = 24,106) completed a face-to-face dietary interview.ResultsThe average daily consumption of meat and aquatic products for the all-aged population was 70.9 g and 48.0 g, respectively, which aligned with Dietary Guidelines (40–75 g/d) for Chinese Residents (2016). On the one hand, intake of aquatic products among Chinese people was relatively insufficient, especially for adolescents and elder people (<40 g/d). On the other hand, males, mainly aged 19–60, generally consumed too much meat (>80 g/d), and 19–44 grouping consumed more than 70 g/d of red meat. Besides, urban residents and individuals with higher socioeconomic status (SES) have exhibited comprehensively healthy dietary preferences than rural ones and those with a lower SES do. Women and the higher SES group tend to be closer to the dietary guidelines for the Chinese.ConclusionsThe consumption of meat and aquatic products varied with age, gender, region and SES. Detecting patterns in consumption is particularly relevant for policy makers, researchers and health professionals in the formulation of dietary recommendations and estimating potential health outcomes.

Women are twice as likely as men to develop depression. Few studies have explored gender difference in cognitive function of patients with MDD. The gender difference in the pre-attentive information processing of MDD patients is still poorly understood. To examine the gender differences in change detection, 30 medication-free MDD patients (15 women) and 30 age and education matched controls (15 women) were recruited. The deviant-standard reverse oddball paradigm (50 ms/150 ms) was used to obtain the visual mismatch negativity (vMMN) in first episode MDD patients. Compared to men with MDD, women with MDD showed a significantly decreased increment vMMN, while no gender difference in decrement vMMN was found. The increment vMMN amplitude in MDD women was smaller than in healthy women, whereas no difference was found in decrement vMMN. Neither increment nor decrement vMMN differed between MDD men and healthy men. The mean amplitude of increment vMMN was not correlated with symptoms of MDD in MDD patients and MDD women. To conclude, the dysfunction of visual information processing existed at pre-attentive stage in MDD women.

The present study aimed to detect and analyze the concentrations of 12 trace elements in the sera and placental tissues of pregnant women with gestational diabetes mellitus (GDM) in Beijing, China using inductively coupled plasma mass spectrometry. Thirty pregnant women that participated in this study, and 20 matched normal controls were recruited in the study; the concentrations of trace elements were compared between these groups. The trace elements iron (Fe), copper (Cu), zinc (Zn), selenium (Se), chromium (Cr), manganese (Mn), nickel (Ni), strontium (Sr), lead (Pb), aluminum (Al), arsenic (As), and palladium (Pd) were detected in the sera and placental tissues of the pregnant women. Using a double antibody sandwich ELISA, the activity of glutathione peroxidases (GPXs), including GPX1-4, were assessed in the placental tissues. Compared with healthy pregnant women, the concentrations of Fe, Cu, Se, and Cr in the sera of pregnant women with GDM were significantly increased (P ˂ 0.05), whereas no significant differences in Al, Mn, Ni, As, Sr, Pd, and Pb sera concentrations were detected between these groups(P > 0.05); And Zn concentrations did not quite achieve acceptable levels of statistical significance (P = 0.047). Also in comparison to healthy pregnant women, Fe concentrations in the placental tissues of pregnant women with GDM were significantly increased (P ˂ 0.05), whereas Zn, Mn, Al, and As concentrations were significantly decreased (P ˂ 0.05); however, the concentrations of Cr, Ni, Se, Sr, Pd, Pb, and Cu in placental tissues did not differ between the groups. In addition, GPX1-4 activity did not differ between the GDM and healthy groups. The differences in trace element concentrations detected in pregnant patients with GDM and healthy pregnant patients suggest that some elements, such as Fe, Cu, Zn, and Se may play important roles in the disease and could be considered potential biomarkers.

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