Background: Predictions of cardiovascular disease risks based on health records have long attracted broad research interests. Despite extensive efforts, the prediction accuracy has remained unsatisfactory. This raises the question as to whether the data insufficiency, statistical and machine-learning methods, or intrinsic noise have hindered the performance of previous approaches, and how these issues can be alleviated. Objective: Based on a large population of patients with hypertension in Shenzhen, China, we aimed to establish a high-precision coronary heart disease (CHD) prediction model through big data and machine-learning Methods: Data from a large cohort of 42,676 patients with hypertension, including 20,156 patients with CHD onset, were investigated from electronic health records (EHRs) 1-3 years prior to CHD onset (for CHD-positive cases) or during a disease-free follow-up period of more than 3 years (for CHD-negative cases). The population was divided evenly into independent training and test datasets. Various machine-learning methods were adopted on the training set to achieve high-accuracy prediction models and the results were compared with traditional statistical methods and well-known risk scales. Comparison analyses were performed to investigate the effects of training sample size, factor sets, and modeling approaches on the prediction performance. Results: An ensemble method, XGBoost, achieved high accuracy in predicting 3-year CHD onset for the independent test dataset with an area under the receiver operating characteristic curve (AUC) value of 0.943. Comparison analysis showed that nonlinear models (K-nearest neighbor AUC 0.908, random forest AUC 0.938) outperform linear models (logistic regression AUC 0.865) on the same datasets, and machine-learning methods significantly surpassed traditional risk scales or fixed models (eg, Framingham cardiovascular disease risk models). Further analyses revealed that using time-dependent features obtained from multiple records, including both statistical variables and changing-trend variables, helped to improve the performance compared to using only static features. Subpopulation analysis showed that the impact of feature design had a more significant effect on model accuracy than the population size. Marginal effect analysis showed that both traditional and EHR factors exhibited highly nonlinear characteristics with respect to the risk scores. Conclusions: We demonstrated that accurate risk prediction of CHD from EHRs is possible given a sufficiently large population of training data. Sophisticated machine-learning methods played an important role in tackling the heterogeneity and nonlinear nature of disease prediction. Moreover, accumulated EHR data over multiple time points provided additional features that were valuable for risk prediction. Our study highlights the importance of accumulating big data from EHRs for accurate disease predictions.

Background: Although several studies have indicated that lipoprotein(a) is a useful prognostic predictor for patients following percutaneous coronary intervention ( PCI ), previous observations have somewhat been limited by either small sample size or short‐term follow‐up. Hence, this study aimed to evaluate the impact of lipoprotein(a) on long‐term outcomes in a large cohort of stable coronary artery disease patients after PCI . Methods and Results: In this multicenter and prospective study, we consecutively enrolled 4078 stable coronary artery disease patients undergoing PCI from March 2011 to March 2016. They were categorized according to both the median of lipoprotein(a) levels and lipoprotein(a) values of <15 (low), 15 to 30 (medium), and ≥30 mg/ dL (high). All patients were followed up for occurrence of cardiovascular events, including cardiovascular death, nonfatal myocardial infarction, and stroke. During an average of 4.9 years of follow‐up, 315 (7.7%) cardiovascular events occurred. The events group had significantly higher lipoprotein(a) levels than the nonevents group. Compared with the low lipoprotein(a) group, Kaplan–Meier analysis showed that the high lipoprotein(a) group had a significantly lower cumulative event‐free survival rate, and multivariate Cox regression analysis further revealed that the high lipoprotein(a) group had significantly increased cardiovascular events risk. Moreover, adding continuous or categorical lipoprotein(a) to the Cox model led to a significant improvement in C‐statistic, net reclassification, and integrated discrimination. Conclusions: With a large sample size and long‐term follow‐up, our data confirmed that high lipoprotein(a) levels could be associated with a poor prognosis after PCI in stable coronary artery disease patients, suggesting that lipoprotein(a) measurements may be useful for patient risk stratification before selective PCI .

The core of tumor cell metabolism is the management of energy metabolism due to the extremely high energy requirements of tumor cells. The purine nucleotide synthesis pathway in cells uses the purinosomes as an essential spatial structural complex. In addition to serving a crucial regulatory role in the emergence and growth of tumors, it contributes to the synthesis and metabolism of purine nucleotides. The significance of purine metabolism in tumor cells is initially addressed in this current article. The role of purinosomes as prospective therapeutic targets is then reviewed, along with a list of the signaling pathways that play in the regulation of tumor metabolism. A thorough comprehension of the function of purinosomes in the control of tumor metabolism can generate fresh suggestions for the creation of innovative cancer treatment methods.

Recent studies have uncovered the vital roles played by microRNAs (miRNAs) in regulating cardiac injury. Amongst them, the cardiac enriched microRNA-1 (miR-1) has been extensively studied and proven to be detrimental to cardiac myocytes. Hence, the current study aims to explore whether miR-1 affects myocardial ischemia/reperfusion injury (MIRI) in rats undergoing sevoflurane preconditioning and the underlying mechanism. After successful model establishment, rats with MIRI were transfected with mimics or inhibitors of miR-1, or siRNA against MAPK3, and then were injected with sevoflurane. A luciferase reporter gene assay was conducted to evaluate the targeting relationship between miR-1 and MAPK3. Reverse transcription quantitative polymerase chain reaction and Western blot analysis were employed to evaluate the expressions of miR-1, MAPK3, PI3K, and Akt. Additionally, the concentration of lactate dehydrogenase (LDH) was determined. Cell apoptosis and viability were assessed using TUNEL and CCK-8 assays, and the ischemic area at risk and infarct size were detected using Evans Blue and TTC staining. MAPK3 was found to be the target gene of miR-1. MiR-1 expressed at a high level while MAPK3 at a low level in MIRI rats. Overexpressing miR-1 or silencing MAPK3 blocked the PI3K/Akt pathway to increase cell apoptosis, ischemic area at risk and infarct area but decreased cell viability, along with increased LDH concentration. In contrast, miR-1 down-regulation abrogated the effects induced by miR-1 mimics or siRNA against MAPK3. These findings indicate that inhibition of miR-1 promotes MAPK3 to protect against MIRI in rats undergoing sevoflurane preconditioning through activation of the PI3K/Akt pathway.

In contrast to polymer bioresorbable stents (BRS) that exhibited suboptimal performance in clinical trials due to their deficient mechanical properties, metallic BRS with improved mechanical strength have made their way into the clinic and have demonstrated more promising results. In the roadmap of research and development of metallic BRS, magnesium and iron based biodegradable metal stents had been clinically used, and the zinc based biodegradable metal stents had been trailed in Mini-Pigs. In this mini-review paper, we demonstrate the current technology levels and point out the future R&D direction of metallic BRS. Magnesium based BRS should target for decreasing struct thickness meanwhile balancing with enough supporting strength. Iron based BRS should move towards high efficient absorption, conversion, metabolism, elimination of its degradation products. Zn based BRS should strive to improve mechanical stability, creep resistance and biocompatibility. Future R&D directions of metallic BRS should move towards new materials such as Molybdenum, intelligent stent integrated with degradable biosensors, and new stent with multiple biofunctions, such as NO release.