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The authors computationally screened potential ALDH1 inhibitors, for use as potential cancer therapeutics.

The authors looked factors that could lead to earlier diagnosis of cardiovascular disease thereby improving patient outcomes. They found that advances in imaging and electrocardiography contribute to earlier detection of cardiovascular disease.

This paper investigates the potential anticancer properties of Uvularia perfoliata by testing its effects on the viability of uveal melanoma cells.

This study compares the performance of two deep learning models, U-Net and DeconvNet, for segmenting gliomas from MRI scans.

Arsenic contamination in rice, caused by the use of arsenic-laden groundwater for irrigation, is a growing global concern, affecting over 150 million people. To address this, researchers hypothesized that genetically modifying rice plants with arsenic-resistant genes could reduce arsenic uptake and allow the plants to detoxify arsenic, making them safer to consume.

Sodium dodecyl sulfate (SDS), a detergent component, can harm plant growth when it contaminates soil and waterways. Authors explored the potential of lactic acid bacteria (LAB) to mitigate SDS-induced stress on plants.

The authors looked at developing a PMMA nanoparticle fabric dye that would be more sustainable compared to traditional fabric dyes. They were able to create PMMA based dyes in different colors that were also durable (i.e., did not fade quickly on fabric).

The authors test how the size-segregation theory applies to the behavior of hollow and irregular-shaped objects.

The authors develop a new method for training machine learning algorithms to differentiate between hate speech and cultural speech in online platforms.

In this study the authors looked at the extract of Stephania tetrandra and its impact on symptoms related to obsessive compulsive disorder in fruit flies.