Autosomal Recessive Nonsyndromic Arrhythmogenic Right Ventricular Cardiomyopathy without Cutaneous Involvements: A Novel Mutation

The arrhythmogenic right ventricular dysplasia/cardiomyopathy (ARVD/C) is a genetic disease frequently associated with desmosomal mutations, mainly attributed to dominant mutations in the Plakophilin‐2 (PKP2) gene. Naxos and Carvajal are the syndromic forms of ARVD/C due to recessive mutations. Herein, we report an autosomal recessive form of nonsyndromic ARVD/C caused by a mutation in the PKP2 gene. After examination and implementation of diagnostic modalities, the definite diagnosis of ARVD/C was confirmed by detection of ventricular tachycardia with a left bundle branch configuration and a superior axis, T‐wave inversion in right precordial leads (i.e., V1‐V3) in a 12‐lead electrocardiogram, and a right ventricle outflow tract dilatation. Neither cutaneous involvement nor other abnormalities were observed. Genetic testing was performed during which an intronic mutation of c.2577+1G>T in the PKP2 gene was observed homozygously. The c.2577+1G>T disrupts PKP2 mRNA splicing and causes a nonsyndromic form of ARVD/C.     

Direct selenosulfonylation of unsaturated compounds: a review

In this review, we have discussed recent developments on the direct selenosulfonylation of unsaturated compounds which lead to the formation of two new carbon-sulfur and carbon-selenium bonds in a single operation. The reactions were classified based on the type of starting unsaturated compound and product. Thus, the review is divided into three major sections. The first describes the current literature on selenosulfonylation of alkenes. The second section covers the available literature on selenosulfonylation of alkynes. The third focuses exclusively on selenosulfonylation of allenes.

In this review, we have discussed recent developments on the direct selenosulfonylation of unsaturated compounds which lead to the formation of two new carbon-sulfur and carbon-selenium bonds in a single operation.     

MF-AV-Net: an open-source deep learning network with multimodal fusion options for artery-vein segmentation in OCT angiography

This study is to demonstrate the effect of multimodal fusion on the performance of deep learning artery-vein (AV) segmentation in optical coherence tomography (OCT) and OCT angiography (OCTA); and to explore OCT/OCTA characteristics used in the deep learning AV segmentation. We quantitatively evaluated multimodal architectures with early and late OCT-OCTA fusions, compared to the unimodal architectures with OCT-only and OCTA-only inputs. The OCTA-only architecture, early OCT-OCTA fusion architecture, and late OCT-OCTA fusion architecture yielded competitive performances. For the 6 mm×6 mm and 3 mm×3 mm datasets, the late fusion architecture achieved an overall accuracy of 96.02% and 94.00%, slightly better than the OCTA-only architecture which achieved an overall accuracy of 95.76% and 93.79%. 6 mm×6 mm OCTA images show AV information at pre-capillary level structure, while 3 mm×3 mm OCTA images reveal AV information at capillary level detail. In order to interpret the deep learning performance, saliency maps were produced to identify OCT/OCTA image characteristics for AV segmentation. Comparative OCT and OCTA saliency maps support the capillary-free zone as one of the possible features for AV segmentation in OCTA. The deep learning network MF-AV-Net used in this study is available on GitHub for open access.