International hospital-based studies have indicated a high risk of cognitive impairment after stroke, evidence from community-based studies in China is scarce. To determine the prevalence of post-stroke cognitive impairment (PSCI) and its subtypes in stroke survivors residing in selected rural and urban Chinese communities, we conducted a community-based, cross-sectional study in 599 patients accounting for 48% of all stroke survivors registered in the 4 communities, who had suffered confirmed strokes and had undergone cognitive assessments via the Montreal Cognitive Assessment (MoCA), Mini-Mental State Examination (MMSE), and Hachinski Ischemia Scale (HIS). Detection of PSCI was based on scores in these neuropsychological scales. Factors potentially impacting on occurrence of PSCI were explored by comparing demographic characteristics, stroke features, and cardiovascular risk factors between patients with and without PSCI. The overall prevalence of PSCI was 80.97% (95%CI: 77.82%-84.11%), while that of non-dementia PSCI (PSCI-ND) and post-stroke vascular dementia (PSD) was 48.91% (95%CI: 44.91%-52.92%) and 32.05% (95%CI: 28.32%-35.79%), respectively. Prior stroke and complications during the acute phase were independent risk factors for PSCI. The risk of recurrent stroke survivors having PSCI was 2.7 times higher than for first-episode survivors, and it was 3 times higher for those with complications during the acute phase than for those without. The higher prevalence of PSCI in this study compared with previous Chinese studies was possibly due to the combined effects of including rural stroke survivors, a longer period from stroke onset, and different assessment methods. There is an urgent need to recognize and prevent PSCI in stroke patients, especially those with recurrent stroke and complications during the acute phase.

Data to date is far from sufficient to describe the recent epidemiology of ventilator-associated pneumonia (VAP) in mainland China. This study aimed to estimate the overall incidence of VAP, with a special focus on its temporal trend and associated factors. Meta-analyses of 195 studies published from 2010 to 2015 were conducted, followed by subgroup analyses by methodological quality, pre-defined setting characteristics and attributes of populations. The overall cumulative VAP incidence in mainland China was 23.8% (95% confidence interval (CI) 20.6–27.2%), with the results showing high heterogeneity. The pooled incidence densities were 24.14 (95% CI 21.19–27.51) episodes and 22.83 (95% CI 19.88–26.23) patients per 1000 ventilator-days. A decline in the cumulative incidence was observed from 2006 (49.5%, 95% CI 40.0–59.0%) to 2014 (19.6%, 95% CI 10.4–31.0%); differences in the incidence rates were also documented according to Chinese provinces and diagnostic criteria (p < 0.001). Older age (≥60 years), coma, re-intubation, tracheotomy and prolonged ventilation were the factors significantly associated with the occurrence of VAP. The incidence of VAP remains high in mainland China but has decreased since 2006. The reported rates vary considerably across individual studies, probably due to variations in diagnosis and geographical region. More studies using standard definitions and cut-off points are needed to better clarify the epidemiology of VAP across the country.

Background: Biomedical named entity recognition (BioNER) is a basic and important medical information extraction task to extract medical entities with special meaning from medical texts. In recent years, deep learning has become the main research direction of BioNER due to its excellent data-driven context coding ability. However, in BioNER task, deep learning has the problem of poor generalization and instability. Results: we propose the hierarchical shared transfer learning, which combines multi-task learning and fine-tuning, and realizes the multi-level information fusion between the underlying entity features and the upper data features. We select 14 datasets containing 4 types of entities for training and evaluate the model. The experimental results showed that the F1-scores of the five gold standard datasets BC5CDR-chemical, BC5CDR-disease, BC2GM, BC4CHEMD, NCBI-disease and LINNAEUS were increased by 0.57, 0.90, 0.42, 0.77, 0.98 and − 2.16 compared to the single-task XLNet-CRF model. BC5CDR-chemical, BC5CDR-disease and BC4CHEMD achieved state-of-the-art results.The reasons why LINNAEUS’s multi-task results are lower than single-task results are discussed at the dataset level. Conclusion: Compared with using multi-task learning and fine-tuning alone, the model has more accurate recognition ability of medical entities, and has higher generalization and stability.

Heavy menstrual bleeding (HMB) has been shown to have a profound negative impact on women’s quality of life and lead to increases in health care costs; however, data on HMB among Chinese population is still rather limited. The present study therefore aimed to determine the current prevalence and risk factors of subjectively experienced HMB in a community sample of Chinese reproductive-age women, and to evaluate its effect on daily life. We conducted a questionnaire survey in 2356 women aged 18–50 years living in Beijing, China, from October 2014–July 2015. A multivariate logistic regression model was used to identify risk factors for HMB. Overall, 429 women experienced HMB, giving a prevalence of 18.2%. Risk factors associated with HMB included uterine fibroids (adjusted odds ratio [OR] =2.12, 95% confidence interval [CI] = 1.42–3.16, P < 0.001) and multiple abortions (≥3) (adjusted OR = 3.44, 95% CI = 1.82–6.49, P < 0.001). Moreover, women in the younger age groups (≤24 and 25–29 years) showed higher risks for HMB, and those who drink regularly were more likely to report heavy periods compared with never drinkers (adjusted OR = 2.78, 95% CI = 1.20–6.46, P = 0.017). In general, women experiencing HMB felt more practical discomforts and limited life activities while only 81 (18.9%) of them had sought health care for their heavy bleeding. HMB was highly prevalent among Chinese women and those reporting heavy periods suffered from greater menstrual interference with daily lives. More information and health education programs are urgently needed to raise awareness of the consequences of HMB, encourage women to seek medical assistance and thus improve their quality of life.