In order to better understand the respective roles of the nuclear retinoic acid receptors (RARs) and the cytosolic retinoic acid binding protein (CRABP) in the mode of action of retinoic acid (RA), several types of RA analogs have been synthesized. Representative compounds have been radiolabeled to a high specific activity and their binding (direct and competition) to RARs and CRABP was determined. Their biological activity on F9 embryonal carcinoma cell differentiation has been determined by a quantitative assay of plasminogen activator (PA). All biologically active analogs studied in this work bound to RARs. A good correlation was found between PA induction and affinity for the RARs, with the exception of RA itself which was a good ligand but a moderate inducer of F9 differentiation. Two biologically active analogs (compounds II and III) did not bind to the CRABP. One biologically inactive analog (compound VIII) bound to CRABP. These results strongly suggest that retinoids must bind to RARs but not necessarily to CRABP in order to induce cell differentiation in F9 cells.
Automatic analysis of electrocardiograms with adequate explainability is a challenging task. Many deep learning based methods have been proposed for automatic classification of electrocardiograms. However, very few of them provide detailed explainable classification evidence. In our study, we explore explainable ECG classification through explicit decomposition of single-beat (median-beat) ECG signal. In particular, every single-beat ECG sample is decomposed into five subwaves and each subwave is parameterised by a Frequency Modulated Moebius. Those parameters have explicit meanings for ECG interpretation. In stead of solving the optimisation problem iteratively which is timeconsuming, we make use of an Cascaded CNN network to estimate the parameters for each single-beat ECG signal. Our preliminary results show that with appropriate position regularisation strategy, our neural network is able to estimate the subwave for P, Q, R, S, T events and maintain a good reconstruction accuracy (with R2 score 0.94 on test dataset of PTB-XL) in a unsupervised manner. Using the estimated parameters, we achieve very good classification and generalisation performance on myocardial infarction detection on four different datasets. The features of high importance are in accordance with clinical interpretations.
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