Shengfa Zhang

With the rise of data-intensive research, data literacy has become a critical capability for improving scientific data quality and achieving artificial intelligence (AI) readiness. In the biomedical domain, data are characterized by high complexity and privacy sensitivity, calling for robust and systematic data management skills. This paper reviews current trends in scientific data governance and the evolving policy landscape, highlighting persistent challenges such as inconsistent standards, semantic misalignment, and limited awareness of compliance. These issues are largely rooted in the lack of structured training and practical support for researchers. In response, this study builds on existing data literacy frameworks and integrates the specific demands of biomedical research to propose a comprehensive, lifecycle-oriented data literacy competency model with an emphasis on ethics and regulatory awareness. Furthermore, it outlines a tiered training strategy tailored to different research stages-undergraduate, graduate, and professional, offering theoretical foundations and practical pathways for universities and research institutions to advance data literacy education.

Background: The incidence of stroke is a challenge in China, as stroke imposes a heavy burden on families, national health services, social services, and the economy. The length of hospital stay (LOS) is an essential indicator of utilization of medical services and is usually used to assess the efficiency of hospital management and patient quality of care. This study established a prediction model based on a machine learning algorithm to predict ischemic stroke patients’ LOS. Methods: A total of 18,195 ischemic stroke patients’ electronic medical records and 28 attributes were extracted from electronic medical records in a large comprehensive hospital in China. The prediction of LOS was regarded as a multi classification problem, and LOS was divided into three categories: 1–7 days, 8–14 days and more than 14 days. After preprocessing the data and feature selection, the XGBoost algorithm was used to build a machine learning model. Ten fold cross-validation was used for model validation. The accuracy (ACC), recall rate (RE) and F1 measure were used to evaluate the performance of the prediction model of LOS of ischemic stroke patients. Finally, the XGBoost algorithm was used to identify and remove irrelevant features by ranking all attributes based on feature importance. Results: Compared with the naive Bayesian algorithm, logistic region algorithm, decision tree classifier algorithm and ADaBoost classifier algorithm, the XGBoot algorithm has higher ACC, RE and F1 measure. The average ACC, RE and F1 measure were 0.89, 0.89 and 0.89 under the 10-fold cross-validation. According to the analysis of the importance of features, the LOS of ischemic stroke patients was affected by demographic characteristics, past medical history, admission examination features, and operation characteristics. Finally, the features in terms of hemiplegia aphasia, MRS, NIHSS, TIA, Operation or not, coma index etc. were found to be the top features in importance in predicting the LOS of ischemic stroke patients. Conclusions: The XGBoost algorithm was an appropriate machine learning method for predicting the LOS of patients with ischemic stroke. Based on the prediction model, an intelligent medical management prediction system could be developed to predict the LOS based on ischemic stroke patients’ electronic medical records.

Background China faced the most significant challenge from stroke because it imposes a heavy burden on families, national health services, social services, and the economy. The length of hospital stay (LOS) was an essential indicator of utilization of medical services and was usually used to assess the efficiency of hospital management and patient quality of care. This study established a prediction model based on the machine learning algorithm to predict the ischemic stroke patients' LOS.MethodsA total of 18,195 ischemic stroke patients' electronic medical records and 28 attributes were extracted from electronic medical records in a large comprehensive hospital in China. After preprocessing the data and feature selection, the XGBoost algorithm was used for building a machine learning model. The 10-fold cross-validation was used for model validation. The accuracy (ACC), recall rate (RE) and F1 measure were used to evaluate the performance of the prediction model of LOS of ischemic stroke patients. Finally, the XGBoost algorithm was used to identify and remove irrelevant features by ranking all attributes based on feature importance.ResultsThe average ACC, RE and F1 measure were 0.96, 0.82 and 0.79, respectively, under the 10-fold cross-validation. According to the analysis of the importance of features, the LOS of ischemic stroke patients was affected by demographic characteristics, past medical history, admission examination features, and operation characteristics. Finally, the features, including NIHSS, MRS, Hemiplegia aphasia, age, BMI and TIA etc. were found to be the top ten features in importance in predicting the LOS of ischemic stroke patients.ConclusionsThe XGBoost algorithm was an appropriate machine learning method for predicting the LOS of patients with ischemic stroke. Based on the prediction model, an intelligent medical management prediction system could be developed to predict the LOS based on ischemic stroke patients' electronic medical records.

Background: Several studies have demonstrated that smoke-free legislation is associated with a reduced risk of mortality from acute myocardial infarction (AMI). This study aimed to examine and quantify the potential effect of smoke-free legislation on AMI mortality rate in different countries. Methods: Studies were identified using a systematic search of the scientific literature from electronic databases, including PubMed, Web of Science, ScienceDirect, Embase, Google Scholar, and China National Knowledge Infrastructure (CNKI), from their inception through September 30, 2017. A random effects model was employed to estimate the overall effects of smoke-free legislation on the AMI mortality rate. Subgroup analysis was performed to explore the possible causes of heterogeneity in risk estimates based on sex and age. The results of meta-analysis after excluding the studies with a high risk of bias were reported in this study. Results: A total of 10 eligible studies with 16 estimates of effect size were included in this meta-analysis. Significant heterogeneity in the risk estimates was identified (overall I² = 94.6%, p < 0.001). Therefore, a random effects model was utilized to estimate the overall effect of smoke-free legislation. There was an 8% decline in AMI mortality after introducing smoke-free legislation (RR = 0.92, 95% confidence interval (CI): 0.90–0.94). The results of subgroup analyses showed that smoke-free legislation was significantly associated with lower rates of mortality for the following 5 diagnostic subgroups: smoke-free in workplaces, restaurants and bars (RR = 0.92, 95% CI: 0.90–0.95), smaller sample size (RR = 0.92, 95% CI: 0.89–0.95), study location in Europe (RR = 0.90, 95% CI: 0.85–0.94), regional study area (RR = 0.92, 95% CI: 0.89–0.94), and no previous local smoke-free legislation (RR = 0.91, 95% CI: 0.90–0.93). However, there was not much difference in AMI mortality rates after the legislation between the longer (RR = 0.92, 95% CI: 0.86–0.98) and shorter follow-up duration subgroups (RR = 0.92, 95% CI: 0.89–0.94). Conclusion: Smoke-free legislation could significantly reduce the AMI mortality rate by 8%. The reduction in the AMI mortality rate was more significant in studies with more comprehensive laws, without prior smoke-free bans, with a smaller sample size, at the regional level, and with a location in Europe.

Early distinguishing the cognitive impairment from healthy population is crucial to delay the progression of mild cognitive impairment (MCI) and Alzheimer disease (AD). Test Your Memory (TYM) has been proved to be a valid and reliable screening instrument for AD and MCI. This study aimed to develop a culturally appropriate and functional Standard Mandarin Chinese translation of the TYM, and to evaluate its reliability and validity in detecting AD and MCI in Chinese. 182 subjects with AD/MCI and 55 healthy controls were recruited to participate in this study, and everyone undergo the test of Standard Mandarin Chinese version of the TYM (TYM-CN), Mini-mental State Examination (MMSE), Montreal cognitive assessment (MoCA-BJ), and Clinical Dementia Rating (CDR) Scale. Concurrently, all the subjects with AD/MCI received the general physical and neurologic examinations, extensive laboratory tests, and brain computed tomography/magnetic resonance imaging (MRI). Of which, 90 subjects were asked to complete the re-test of TYM-CN at 3 weeks after the initial visit. Intra-class correlation coefficient (ICC) and Cronbach’s alpha was used to assess the test–retest reliability and the internal consistency. The validity, sensitivity and specificity were also analyzed. One-way analysis of variance, χ2 test, correlation analysis, and receiver operating characteristic curve (ROC) analysis were employed, as needed. The total scores of TYM-CN was 43.89 ± 3.44, 40.88 ± 4.38, and 29.12 ± 7.44 (p < 0.01) for healthy controls group, MCI group, and AD group, respectively. The ICC for 11 items of TYM-CN ranged from 0.863 (copying) to 0.994 (anterograde), and that of the total scale was 0.993, suggesting an excellent reliability. Furthermore, the significant correlation was also found between TYM-CN and MMSE (r = 0.76), MoCA-BJ (r = 0.74), and CDR scores (r = 0.76), indicating a good validity. A TYM-CN scores ≤ 39.5 had 95% sensitivity and 95% specificity in differentiating AD from healthy controls, and that ≤ 43.5 had 75% sensitivity and 91% specificity in distinguishing MCI from healthy controls, respectively. The reliability and validity of the TYM-CN are statistically acceptable for the evaluation of cognitive impairment, which may contribute to neuropsychological tests for the diagnosis of AD and MCI from healthy controls in China.

Existing studies in developed countries show that social participation has beneficial effects on the functional ability of older adults, but research on Chinese older people is limited. This study examined the effects of participating in different types of social activities on the onset of functional disability and the underlying behavioral and psychosocial mechanisms among older adults aged 65 and older in China. The 2005, 2008, and 2011 waves of the Chinese Longitudinal Health Longevity Study were used. Life table analysis and discrete time hazard models were adopted to examine the relationship between social participation and functional disability. Social participation was defined as the frequencies of engaging in group leisure-time activities (i.e., playing cards/mahjong) and organized social activities, involving in informal social interactions (i.e., number of siblings frequently visited), and participating in paid jobs. Extensive social participation was measured by a composite index by adding up the four types of social activities that an older person was engaged in. After controlling for the effect of socio-demographic characteristics, health status, and health behavioral factors, extensive social participation is associated with a significant reduced risk for the onset of functional disability (hazard ratio [HR] = 0.92, p < 0.001). Different types of social participation affect the risk of functional decline through different mechanisms. Frequent playing of cards/mahjong is a protective factor for functional decline (HR = 0.78, p < 0.001), and the relationship is partially mediated by cognitive ability and positive emotions (accounting for 18.9% and 7.0% of the association, respectively). Frequent participation in organized social activities is significantly related to a reduced risk of functional decline (HR = 0.78, p < 0.001), and the association is mediated by physical exercises and cognitive ability (accounting for 25.7% and 17.7% of the association, respectively). Frequent visits from siblings has a strong inverse relationship with functional decline (HR = 0.75, p < 0.001). However, no significant association between paid job and functional decline is observed. Extensive social participation, regular engagement in group leisure-time activities, organized social activities, and informal social interactions in particular may have beneficial effects on the functional health of older adults through behavioral and psychosocial pathways. The findings shed light for the importance of promoting social participation among older adults.

The “empty-nest” elderly family has become increasingly prevalent among old people in China. This study aimed to explore the causality between empty nests and elders’ health using effective instrumental variables, including “whether old parents talk with their families when they are upset” and “ownership of housing”. The results showed that empty nests had a significantly adverse influence on elders’ physical health, cognitive ability and psychological health. Furthermore, urban elders’ cognitive ability was more influenced by empty nests than that of rural elders. Additionally, the effects of an empty nest on elders” health were more significant among female, single elders and senior rural elders. “Living resources”, “availability of medical treatment” and “social activity engagement” were found to be significant mediators between empty nests and elders’ health, accounting for 35% of the total effect.

In 2009, health-care reform was launched to achieve universal health coverage in China. A good understanding of how China’s health reforms are influencing village doctors’ income structure will assist authorities to adjust related polices and ensure that village doctors employment conditions enable them to remain motivated and productive. This study aimed to investigate the village doctors’ income structure and analyse how these health policies influenced it. Based on a review of the previous literature and qualitative study, village doctors’ income structure was depicted and analysed. A qualitative study was conducted in six counties of six provinces in China from August 2013 to January 2014. Forty-nine village doctors participated in in-depth interviews designed to document their income structure and its influencing factors. The themes and subthemes of key factors influencing village doctors’ income structure were analysed and determined by a thematic analysis approach and group discussion. Several policies launched during China’s 2009 health-care reform had major impact on village doctors. The National Essential Medicines System cancelled drug mark-ups, removing their primary source of income. The government implemented a series of measures to compensate, including paying them to implement public health activities and provide services covered by social health insurance, but these have also changed the village doctors’ role. Moreover, integrated management of village doctors’ activities by township-level staff has reduced their independence, and different counties’ economic status and health reform processes have also led to inconsistencies in village doctors’ payment. These changes have dramatically reduced village doctors’ income and employment satisfaction. The health-care reform policies have had lasting impacts on village doctors’ income structure since the policies’ implementation in 2009. The village doctors have to rely on the salaries and subsidies from the government after the drug mark-up was cancelled. China’s national health reforms are attempting to draw village doctors into the national health workforce, but the policies have impacted their income and independence. Further research into these concerns and monitoring of measures to adequately compensate village doctors should be undertaken. Reasonable compensation strategies should be established, and sufficient subsidies should be allocated in a timely manner.