An Acid-Controlled Method for the Regioselective Functionalization of Anilines over Aliphatic Amines.

Regioselective transformations at similar functional groups are of paramount importance in organic synthesis. Traditional strategies towards regioselective functionalization include serial protection/deprotection and sequential synthesis. Modern organic synthesis emphasizes pathway efficiency and protecting group free routes with a goal of exploiting inherent differences in reactivity. This study reports a method for the regioselective functionalization of anilines over aliphatic amines. Utilizing classic conditions for the Baeyer-Mills reaction, anilines were shown to react preferentially in the presence of aliphatic amines. Subsequently, this principle of reactivity was extended to other electrophiles and conditions.

Left ventricular hypertrophy detection using electrocardiographic signal.

Left ventricular hypertrophy (LVH) indicates subclinical organ damage, associating with the incidence of cardiovascular diseases. From the medical perspective, electrocardiogram (ECG) is a low-cost, non-invasive, and easily reproducible tool that is often used as a preliminary diagnosis for the detection of heart disease. Nowadays, there are many criteria for assessing LVH by ECG. These criteria usually include that voltage combination of RS peaks in multi-lead ECG must be greater than one or more thresholds for diagnosis. We developed a system for detecting LVH using ECG signals by two steps: firstly, the R-peak and S-valley amplitudes of the 12-lead ECG were extracted to automatically obtain a total of 24 features and ECG beats of each case (LVH or non-LVH) were segmented; secondly, a back propagation neural network (BPN) was trained using a dataset with these features. Echocardiography (ECHO) was used as the gold standard for diagnosing LVH. The number of LVH cases (of a Taiwanese population) identified was 173. As each ECG sequence generally included 8 to 13 cycles (heartbeats) due to differences in heart rate, etc., we identified 1466 ECG cycles of LVH patients after beat segmentation. Results showed that our BPN model for detecting LVH reached the testing accuracy, precision, sensitivity, and specificity of 0.961, 0.958, 0.966 and 0.956, respectively. Detection performances of our BPN model, on the whole, outperform 7 methods using ECG criteria and many ECG-based artificial intelligence (AI) models reported previously for detecting LVH.