ABSTRACT
Artificial intelligence has been a huge success and contributes to the workplace. In the digital era, the amount of data in clinical practice is increasing, which requires healthcare workers to integrate and interpret the various information generated during clinical work. With the help of artificial intelligence techniques, especially machine learning techniques, researchers in cardiovascular medicine have developed a variety of predictive models to improve the efficiency of clinical work and treatment outcomes. The types of machine learning models were introduced, and the current prediction models of cardiovascular diseases using machine learning technology were summarized. The purpose of this paper is to facilitate accurate diagnosis of cardiovascular diseases and to provide a clearer direction for future development of cardiovascular disease prediction models using machine learning techniques.
ABSTRACT
Objective To investigate the protective effects of folic acid on the oxidative damage that ox-LDL (oxi?dized low-density lipoprotein receptor 1) render to human umbilical vein endothelial cells (HUVEC). Methods HUVECs were injured by ox-LDL (120 mg/L) for 24 h while they were incubated with various concentration of folic acid (0,15, 60, 150, 225, 300, 375 nmol/L). Then HUVECs were cultured in media contains same concentration of folic acid but without ox-LDL for 72 hours. Finally, HUVECs were harvested after 24, 48, 72 and 96 h. The morphological changes were observed us?ing inverted microscope and cell viability were examined by MTT. Results Various concentrations of folic acid (0,15, 50, 100, 200 and 500 nmol/L) has no obvious promotion or inhibition effect in growth of normal HUVEC (P>0.05). However, compared with the ox-FA-def group, 150, 225, 300 and 375 nmol/L of folic acid promoted proliferation of HUVECs with 96 and 120 hours of incubations (P < 0.05). Folic acid of 60, 150, 225, 300 and 375 nmol/L promoted the proliferation of HUVECs with 72 h and 96 hours of incubation (P<0.05). Conclusion High dose folic acid can reduce the ox-LDL oxida?tive damage on HUVEC in a concentration dependent manner.
ABSTRACT
BACKGROUND AND OBJECTIVE: Oesophageal cancer is one of the most common and deadliest cancers worldwide. Our previous population-based study reported a high prevalence of oesophageal cancer in Chaoshan, Guangdong Province, China. Ancestors of the Chaoshan population migrated from the Taihang Mountain region of north-central China, which is another high-incidence area for oesophageal cancer. The purpose of the present study was to obtain evidence of inherited susceptibility to oesophageal cancer in the Chaoshan population, with reference to the Taihang Mountain population, with the eventual goal of molecular identification of the disease genes. METHODS: We conducted familial correlation, commingling, and complex segregation analyses of 224 families from the Chaoshan population and 403 families from the Taihang population using the FPMM program of S.A.G.E. version 5.3.0. A second analysis focused on specific families having large numbers of affected individuals or early onset of the disease. RESULTS: For the general population, moderate sib-sib correlation was noticed for esophageal cancer. Additionally, brother-brother correlation was even higher. Commingling analyses indicated that a three-component distribution model best accounts for the variation in age of onset of oesophageal cancer, and that a multifactorial model provides the best fit to the general population data. An autosomal dominant mode and a dominant or recessive major gene with polygenic inheritance were found to be the best models of inherited susceptibility to oesophageal cancer in some large families. CONCLUSIONS: The current results provide evidence for inherited susceptibility to oesophageal cancer in certain high-risk groups in China, and support efforts to identify the susceptibility genes.