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Using a convolution neural network, these authors show machine learning can clinically diagnose breast cancer with high accuracy.

In this study, a deep learning model is used to classify post-traumatic stress disorder patients through novel markers to assist in finding candidate biomarkers for the disorder.

This study is centered around developing biofortification methods: the authors test whether the amount of calcium available to growing crops translates into more calcium present in the crops.

In this study, the authors assess the factors that allow some speedcubers to solve Rubik's Cubes faster than others.

In this study, the authors investigate whether Eisenia Fetida nerve signal speed correlates with Withania somnifera ingestion, a possible way to protect against demyelination.

In this study, the effects of different sources of serum on growing mesenchymal stem cells are compared with the goal of identifying one more suitable for clinical use.

This study applies Taft linear free-energy relationships to study kinetic trends in the enzymatic hydrolysis of sterically hindered substrates.

Inefficient penetration of cancer drugs into the interior of the three-dimensional (3D) tumor tissue limits drugs' delivery. The authors hypothesized that the addition of phospholipase A2 (PLA2) would increase the permeability of the drug doxorubicin for efficient drug penetration. They found that 1 mM PLA2 had the highest permeability. Increased efficiency in drug delivery would allow lower concentrations of drugs to be used, minimizing damage to normal cells.

The sequence of nitrogenous bases that make up the DNA of organisms can contain hidden mathematical sequences. Here the authors used BioPython, a programming tool, to find an organism that displays Gijswijt’s Sequence in its genome. In this manner they found that the common carp best displays Gijswijt’s Sequence in its genome.

In this article, the authors investigate whether stock selection across various sectors is efficient enough to outperform an overall market. Stocks from 2006 to 2020 were selected across sectors to calculate beta values using the Capital Asset Pricing Model.