Miljkovic, D. | Search bioRxiv

bioRxiv2025

EXPRESSION OF ANO1 IN HUMAN GASTROINTESTINAL TRACT DURING EMBRYONIC AND FETAL DEVELOPMENT

Anoctamin 1 (ANO1, TMEM16A) is a transmembrane protein belonging to the ANO family, with a role in the formation of calcium-activated chloride channels (CaCC). It is included in the regulation of physiological processes such as muscle contraction, gastrointestinal motility, secretion, and electrical excitability. Also, recent data suggest that ANO1 is a specific marker for interstitial cells of Cajal (ICC). The aim of the paper was to examine the spatial and temporal distribution of ANO1 in the stomach, small intestine, and large intestine during embryofetal development as a potential marker for the differentiation of ICC and smooth muscle cells. As a material, we used 2 human embryos and samples from 21 human fetuses. The tissue samples were routinely processed into paraffin blocks, and 5 {micro}m-thick sections were immunostained for ANO1. Our results show that ANO1 appears during embryonic development (8th week), and its expression continues through the fetal stages. Epithelial, endothelial and ICC cells consistently expressed ANO1 in all examined samples. Smooth muscle cells showed strong expression in muscularis propria, however by the 25th week this immunopositivity was absent from outer muscle layers in stomach and large intestine. In conclusion, ANO1 can be considered as a reliable marker for following the differentiation of SMC and ICC during embryonic and fetal development.

developmental biology↗

bioRxiv2020

A novel method for classification of tabular data using convolutional neural networks

Convolutional neural networks (CNNs) represent a major breakthrough in image classification. However, there has not been similar progress in applying CNNs, or neural networks of any kind, to classification of tabular data. We developed and evaluated a novel method, TAbular Convolution (TAC), for classification of such data using CNNs by transforming tabular data to images and then classifying the images using CNNs. The transformation is performed by treating each row of tabular data (i.e., vector of features) as an image filter (kernel), and applying the filter to a fixed base image. A CNN is then trained to classify the filtered images. We applied TAC to classification of gene expression data derived from blood samples of patients with bacterial or viral infections. Our results demonstrate that off-the-shelf ResNet can classify the gene expression data as accurately as the current non-CNN state-of-the-art classifiers.

bioinformatics↗