A review of molecular mechanisms linked to potential renal injury agents in tropical rural farming communities.

The chronic kidney disease of unknown etiology (CKDu) is a global health concern primarily impacting tropical farming communities. Although the precise etiology is debated, CKDu is associated with environmental exposures including heat stress and chemical contaminants such as fluoride, heavy metals, and herbicide glyphosate. However, a comprehensive synthesis is lacking on molecular networks underpinning renal damage induced by these factors. Addressing this gap, here we present key molecular events associated with heat and chemical exposures. We identified that caspase activation and lipid peroxidation are common endpoints of glyphosate exposure, while vasopressin and polyol pathways are associated with heat stress and dehydration. Heavy metal exposure is shown to induce lipid peroxidation and endoplasmic reticulum stress from ROS activated MAPK, NFĸB, and caspase. Collectively, we identify that environmental exposure induced increased cellular oxidative stress as a common mechanism mediating renal cell inflammation, apoptosis, and necrosis, likely contributing to CKDu initiation and progression.

An improved deep learning model for hierarchical classification of protein families.

Although genes carry information, proteins are the main role player in providing all the functionalities of a living organism. Massive amounts of different proteins involve in every function that occurs in a cell. These amino acid sequences can be hierarchically classified into a set of families and subfamilies depending on their evolutionary relatedness and similarities in their structure or function. Protein characterization to identify protein structure and function is done accurately using laboratory experiments. With the rapidly increasing huge amount of novel protein sequences, these experiments have become difficult to carry out since they are expensive, time-consuming, and laborious. Therefore, many computational classification methods are introduced to classify proteins and predict their functional properties. With the progress of the performance of the computational techniques, deep learning plays a key role in many areas. Novel deep learning models such as DeepFam, ProtCNN have been presented to classify proteins into their families recently. However, these deep learning models have been used to carry out the non-hierarchical classification of proteins. In this research, we propose a deep learning neural network model named DeepHiFam with high accuracy to classify proteins hierarchically into different levels simultaneously. The model achieved an accuracy of 98.38% for protein family classification and more than 80% accuracy for the classification of protein subfamilies and sub-subfamilies. Further, DeepHiFam performed well in the non-hierarchical classification of protein families and achieved an accuracy of 98.62% and 96.14% for the popular Pfam dataset and COG dataset respectively.