Artificial intelligence (AI) for science (AI4S) technology, the AI technology for scientific research, has shown tremendous potential and influence in the field of healthcare, redefining the research paradigm of medical science under the guidance of computational medicine. We reviewed the main technological trends of AI4S in reshaping healthcare paradigm: knowledge-driven AI, leveraging extensive literature mining and data integration, emerges an important tool for understanding disease mechanisms and facilitating novel drug development; data-driven AI, delving into clinical and human-related omics data, unveils individual variances and disease mechanisms, and further establishes patient-centric digital twins to guide drug development and personalized medicine. Meanwhile, based on authentic patient digital twin models, adaptable strategies are employed to further propel the development of "e-drugs" that mimic the authentic mechanisms. These digital twins of drugs are evaluated for drug efficacy and safety through large-scale cloud-based virtual clinical trials, and followed by rationally designed real-world clinical trials, thus notably reducing drug development costs and enhancing success rates. Despite encountering challenges such as data scale, quality control, model interpretability, the transition from science insights to engineering solutions, and regulatory hurdles, we anticipate the integration of AI4S technology to revolutionize drug development and clinical practices. This transformation brings revolutionary changes to the medical field, offering novel opportunities and challenges for the development of medical science, and more importantly, providing necessary but personalized healthcare solutions for humankind.
The innovative behavior of clinical nurses is of great significance for the professional development of nurses and the improvement of nursing service quality. This research topic has received continuous attention from domestic and foreign scholars. There is still significant room for improvement in the level of innovative behavior among clinical nurses in China. Constructing effective interventions to enhance innovative behavior among clinical nurses in China is an urgent requirement to promote the development of nursing informatization and nursing quality. This article reviews the intervention forms, theoretical support, effectiveness, and limitations of innovative behaviors among clinical nurses both domestically and internationally. It proposes prospects for future intervention plans, aiming to provide ideas and references for nursing managers to develop tailored, scientific, and effective intervention strategies.
Objective To examine the associations of cumulative serum uric acid to high-density lipoprotein cholesterol ratio (cumUHR) and the trajectories of uric acid to high-density lipoprotein cholesterol ratio (UHR) with the risk of new-onset cardiometabolic multimorbidity (CMM) in middle-aged and older Chinese adults. Methods This study utilized data from the China Health and Retirement Longitudinal Study. A total of 2597 participants who were not diagnosed with CMM at baseline were included. First, K-means clustering analysis was applied to identify UHR trajectories over the 2012–2015 period. In addition, multivariable logistic regression models were used to assess the associations of cumUHR and UHR trajectories with CMM risk. Restricted cubic spline (RCS) model was performed to explore the dose-response relationship between cumUHR and CMM risk. Moreover, subgroup analyses and interaction tests were conducted to examine potential effect modifications by age and sex, etc. Finally, the predictive performance of cumUHR and UHR trajectories for CMM was assessed using receiver operating characteristic (ROC) curve analysis. Results The cumulative incidence of CMM was 3.31%, with 86 cases developing during the follow-up period until 2020. K-means clustering identified three groups of cumUHR trajectories. Multivariable logistic regression found a significant positive association between cumUHR and CMM risk [odds ratio (OR)=43.41, 95% confidence interval (CI) (4.32, 412.06), P=0.001]. Compared to the low-stable trajectory group, individuals in the sustained-high trajectory group had a significantly higher risk of developing CMM [OR=2.48, 95%CI (1.22, 4.99), P=0.011]. RCS analysis demonstrated a linear dose-response relationship between cumUHR and CMM risk (Poverall=0.004, Pnon-linear=0.727). The ROC analysis demonstrated area under the curve values of 0.755 and 0.759 for cumUHR and UHR trajectories, respectively. Conclusions Both cumUHR exposure and a sustained-high UHR trajectory are significantly associated with an increased risk of CMM in middle-aged and older Chinese adults. Long-term dynamic monitoring of UHR is of significant importance for the early prevention of CMM.
