Objective: To evaluate the value of Exercise treadmill test (ETT) in the diagnosis of coronary artery disease (CAD). Methods: Here we reviewed 142 (men, 104, mean age, 52.7±8.6) consecutive patients admitted to the 4th Clinical Hospital of Harbin Medical University for suspicion of CAD and they underwent ETT before coronary angiography (CAG). Patients were divided into four groups to see the sensitivity of different ETT criteria: group I: ETT negative, group II: ETT positive (ST-segment depression ≥0.1mv for more than 2 minutes), group III: ETT positive (exercise induced chest pain), group IV: ETT positive (exercise induced chest pain and ST-segment depression). The severity of coronary artery stenosis was assessed by CAG, only those with >50% of narrowing in at least one of the three major arteries or their first-order branches was considered CAG positive. Results: The false negative rate was 30.6% and the accuracy rate was 69.4% in group I. In group II, III and IV, the accuracy rates and false positive rates were 53.7% and 46.3%, 78.6% and 21.4%, 86.7% and 13.3%, respectively, (p<0.05). Furthermore, we analyzed the data of male patients in each group and the accuracy rates were 67.9%, 60.7%, 76.9%, 100%, respectively, (p<0.05). Multivariate logistic regression results showed that ST-segment depression together with exercise induced chest pain were the most related factors in CAD diagnosis. Conclusion: ST-segment depression and exercise induced chest pain are the strongest factors in CAD diagnosis. Chest pain combined with ST-segment depression had a much higher accuracy rate than ST-segment depression alone.

The estimation of accuracy and applicability of QSAR and QSPR models for biological and physicochemical properties represents a critical problem. The developed parameter of “distance to model” (DM) is defined as a metric of similarity between the training and test set compounds that have been subjected to QSAR/QSPR modeling. In our previous work, we demonstrated the utility and optimal performance of DM metrics that have been based on the standard deviation within an ensemble of QSAR models. The current study applies such analysis to 30 QSAR models for the Ames mutagenicity data set that were previously reported within the 2009 QSAR challenge. We demonstrate that the DMs based on an ensemble (consensus) model provide systematically better performance than other DMs. The presented approach identifies 30−60% of compounds having an accuracy of prediction similar to the interlaboratory accuracy of the Ames test, which is estimated to be 90%. Thus, the in silico predictions can be used to halve the cost of experimental measurements by providing a similar prediction accuracy. The developed model has been made publicly available at http://ochem.eu/models/1.