A Machine Learning Model for the Prediction of No-Reflow Phenomenon in Acute Myocardial Infarction Using the CALLY Index.

Background: Acute myocardial infarction (AMI) constitutes a major health problem with high mortality rates worldwide. In patients with ST-segment elevation myocardial infarction (STEMI), no-reflow phenomenon is a condition that adversely affects response to therapy. Previous studies have demonstrated that the CALLY index, calculated using C-reactive protein (CRP), albumin, and lymphocytes, is a reliable indicator of mortality in patients with non-cardiac diseases.

Slip safety risk analysis of surface properties using the coefficients of friction of rocks.

This study was conducted to determine the most appropriate surface processing techniques (SPT), environmental conditions (EC) and surface roughness (SR) to minimize the risk of slipping when pedestrians walk on a floor covering of rocks barefoot and with shoes. Coefficients of friction (COFs) and values of SR were found using five different types of rocks, four SPT and two (ramp and pendulum) tests. Results indicate that the parameters which affect the COF values of rocks include SR, EC and SPT.