Background: The potential effects of pre-pregnancy body mass (BMI) and gestational weight gain (GWG) on pregnancy outcomes remain unclear. Thus, we investigated socio-demographic characteristics that affect pre-pregnancy BMIs and GWG and the effects of pre-pregnancy BMI and GWG on Chinese maternal and infant complications.Methods: 3172 women were enrolled in the Chinese Pregnant Women Cohort Study-Peking Union Medical College from July 25, 2017 to July 24, 2018, whose babies were delivered before December 31, 2018. Regression analysis was employed to evaluate the socio-demographic characteristics affecting pre-pregnancy BMI and GWG values and their effects on adverse maternal and infant complications.Results: Multivariate logistic regression analysis revealed that age groups < 20 years (OR: 1.97), 25–30 years (OR: 1.66), 30–35 years (OR: 2.24), 35–40 years (OR: 3.90) and ≥ 40 years (OR: 3.33) as well as elementary school or education below (OR: 3.53), middle school (OR: 1.53), high school (OR: 1.40), and living in the north (OR: 1.37) were risk factors in maintaining a normal pre-pregnancy BMI. An age range of 30–35 years (OR: 0.76), living in the north (OR: 1.32) and race of ethnic minorities (OR: 1.51) were factors affecting GWG. Overweight (OR: 2.01) and inadequate GWG (OR: 1.60) were risk factors for gestational diabetes mellitus (GDM). Overweight (OR: 2.80) and obesity (OR: 5.42) were risk factors for gestational hypertension (GHp). Overweight (OR: 1.92), obesity (OR: 2.48) and excessive GWG (OR: 1.95) were risk factors for macrosomia. Overweight and excessive GWG were risk factors for a large gestational age (LGA) and inadequate GWG was a risk factor for low birth weights.Conclusions: Overweight and obesity before pregnancy and an excessive GWG are associated with a greater risk of developing GDM, GHp, macrosomia and LGA. The control of body weight before and during the course of pregnancy is recommended to decrease adverse pregnancy outcomes, especially in pregnant women aged < 20 or > 25 years old educated below university and college levels, for ethnic minorities and those women who live in the north of China.Trial registration: Registered at Clinical Trials (NCT03403543), September 29, 2017.

Background: Artificial intelligence (AI) needs to be accepted and understood by physicians and medical students, but few have systematically assessed their attitudes. We investigated clinical AI acceptance among physicians and medical students around the world to provide implementation guidance.Materials and methods: We conducted a two-stage study, involving a foundational systematic review of physician and medical student acceptance of clinical AI. This enabled us to design a suitable web-based questionnaire which was then distributed among practitioners and trainees around the world.Results: Sixty studies were included in this systematic review, and 758 respondents from 39 countries completed the online questionnaire. Five (62.50%) of eight studies reported 65% or higher awareness regarding the application of clinical AI. Although, only 10–30% had actually used AI and 26 (74.28%) of 35 studies suggested there was a lack of AI knowledge. Our questionnaire uncovered 38% awareness rate and 20% utility rate of clinical AI, although 53% lacked basic knowledge of clinical AI. Forty-five studies mentioned attitudes toward clinical AI, and over 60% from 38 (84.44%) studies were positive about AI, although they were also concerned about the potential for unpredictable, incorrect results. Seventy-seven percent were optimistic about the prospect of clinical AI. The support rate for the statement that AI could replace physicians ranged from 6 to 78% across 40 studies which mentioned this topic. Five studies recommended that efforts should be made to increase collaboration. Our questionnaire showed 68% disagreed that AI would become a surrogate physician, but believed it should assist in clinical decision-making. Participants with different identities, experience and from different countries hold similar but subtly different attitudes.Conclusion: Most physicians and medical students appear aware of the increasing application of clinical AI, but lack practical experience and related knowledge. Overall, participants have positive but reserved attitudes about AI. In spite of the mixed opinions around clinical AI becoming a surrogate physician, there was a consensus that collaborations between the two should be strengthened. Further education should be conducted to alleviate anxieties associated with change and adopting new technologies.

Background: Medical care workers experienced unprecedented levels of workload and pressure since the outbreak of coronavirus disease 2019 (COVID-19). Little is known about its exact impact on medical care workers and related factors in China. This study aims to identify the psychological impact of COVID-19 on medical care workers in China. Methods: From February 23 to March 5, 2020, a cross-sectional survey was conducted among 863 medical care workers from seven provinces in China using standard questionnaires measuring adverse psychological outcomes including Impact of Event Scale-6 (IES-6), Depression, Anxiety and Stress Scale(DASS)and related psychosocial factors like perceived threat, social support and coping strategies. Exploratory Factor analysis was performed to identify the dimensions of perceived threat by study participants. Multivariate regression was used to examine the determinants of adverse psychological outcomes. Results: Posttraumatic stress (PTS) were prevalent in this sample of health care professionals, and 40.2% indicated positive screens for significant posttraumatic stress disorder symptoms. The proportion of having mild to extremely severe symptoms of depression, anxiety and stress were 13.6, 13.9 and 8.6%, respectively. Perceived threat and passive coping strategies were positively correlated to PTS and DASS scores, while perceived social support and active coping strategies were negatively correlated to DASS scores. Nurses were more likely to be anxious than others among medical care workers during the COVID-19 epidemic. Conclusions: Adverse psychological symptoms were prevalent among medical care workers in China during the COVID-19 epidemic. Screening for adverse psychological outcomes and developing corresponding preventive measures would be beneficial in decreasing negative psychological outcomes.

Accurate early detection of breast and cervical cancer is vital for treatment success. Here, we conduct a meta-analysis to assess the diagnostic performance of deep learning (DL) algorithms for early breast and cervical cancer identification. Four subgroups are also investigated: cancer type (breast or cervical), validation type (internal or external), imaging modalities (mammography, ultrasound, cytology, or colposcopy), and DL algorithms versus clinicians. Thirty-five studies are deemed eligible for systematic review, 20 of which are meta-analyzed, with a pooled sensitivity of 88% (95% CI 85–90%), specificity of 84% (79–87%), and AUC of 0.92 (0.90–0.94). Acceptable diagnostic performance with analogous DL algorithms was highlighted across all subgroups. Therefore, DL algorithms could be useful for detecting breast and cervical cancer using medical imaging, having equivalent performance to human clinicians. However, this tentative assertion is based on studies with relatively poor designs and reporting, which likely caused bias and overestimated algorithm performance. Evidence-based, standardized guidelines around study methods and reporting are required to improve the quality of DL research.

Hepatitis B virus (HBV) infection is a major public health threat in China for China has a hepatitis B prevalence of more than one million people in 2017 year. Disease incidence prediction may help hepatitis B prevention and control. This study intends to build and compare 2 forecasting models for hepatitis B incidence in China. Autoregressive integrated moving average (ARIMA) model and grey model GM(1,1) were adopted to fit the monthly incidence of hepatitis B in China from March 2010 to October 2017. The fitting and forecasting performances of the 2 models were evaluated. The better one was adopted to predict the incidence from November 2017 to March 2018. Database was built by Excel 2016 and statistical analysis was completed using R 3.4.3 software. Descriptive analysis showed that the incidence of hepatitis B in China has seasonal variation and has shown a downward trend from 2010 to 2017. We selected the ARIMA (3,1,1) (0,1,2)₁₂ model among all the ARIMA models for it has the lowest AIC value. Model expression of GM (1,1) was X⁽¹⁾ (k + 1) = 3386876.7478e^0.0249k − 3289206.7428. The root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of ARIMA(3,1,1)(0,1,2)₁₂ model were lower than GM(1,1) model on fitting part and forecasting part. According to the forecast results, the incidence may have a slight fluctuation during the following months. The ARIMA model showed better hepatitis B fitting and forecasting performance than GM(1,1) model. It is a potential decision supportive tool for controlling hepatitis B in China before a predictive hepatitis B outbreak.

Background: Colposcopy diagnosis and directed biopsy are the key components in cervical cancer screening programs. However, their performance is limited by the requirement for experienced colposcopists. This study aimed to develop and validate a Colposcopic Artificial Intelligence Auxiliary Diagnostic System (CAIADS) for grading colposcopic impressions and guiding biopsies.Methods: Anonymized digital records of 19,435 patients were obtained from six hospitals across China. These records included colposcopic images, clinical information, and pathological results (gold standard). The data were randomly assigned (7:1:2) to a training and a tuning set for developing CAIADS and to a validation set for evaluating performance.Results: The agreement between CAIADS-graded colposcopic impressions and pathology findings was higher than that of colposcopies interpreted by colposcopists (82.2% versus 65.9%, kappa 0.750 versus 0.516,p < 0.001). For detecting pathological high-grade squamous intraepithelial lesion or worse (HSIL+), CAIADS showed higher sensitivity than the use of colposcopies interpreted by colposcopists at either biopsy threshold (low-grade or worse 90.5%, 95% CI 88.9–91.4% versus 83.5%, 81.5–85.3%; high-grade or worse 71.9%, 69.5–74.2% versus 60.4%, 57.9–62.9%; allp < 0.001), whereas the specificities were similar (low-grade or worse 51.8%, 49.8–53.8% versus 52.0%, 50.0–54.1%; high-grade or worse 93.9%, 92.9–94.9% versus 94.9%, 93.9–95.7%; allp > 0.05). The CAIADS also demonstrated a superior ability in predicting biopsy sites, with a median mean-intersection-over-union (mIoU) of 0.758.Conclusions: The CAIADS has potential in assisting beginners and for improving the diagnostic quality of colposcopy and biopsy in the detection of cervical precancer/cancer.