Browse Articles

Here the authors investigated a combination therapy to target the Kirsten rat sarcoma viral oncogene homolog mutation in lung cancer, by analyzing publicly available data. Their findings indicate that the combination therapy of CA170 and Kvax enhances helper T cell function and improves cytotoxic T lymphocyte infiltration, while Kvax alone drives plasma and memory B cell proliferation.

The authors looked at fertilizer derived from coffee ground tea leaves, measuring the effectiveness by measuring the height, weight, and number of leaves on basil plants.

Here, the authors chose to investigate the efficacy of zinc oxide nanoparticles (ZnO NPs) and cisplatin or zinc ions in inducing cancer apoptosis. While both treatments were found to reduce the proliferation of lung cancer cells, the authors suggest that further studies to identify the mechanism are necessary.

The authors looked at how different blends of synthetic and non-synthetic fabrics protected against UV radiation.

The authors used RNA-seq datasets to assess the effect of CDK7 inhibition on transcriptional pathways in castration-resistant prostate cancer cells.

The authors looked at the ability of berberine in combination with melatonin to have anticancer effects when tested in an in vitro cell model.

Here the authors present an autonomous drone air filtration system that uses a novel algorithm, the gradient ascent ML particle filter (GA/MLPF), to efficiently locate and mitigate outdoor air pollution. They demonstrate that their GA/MLPF algorithm is significantly more efficient than the conventional gradient ascent algorithm, reducing both the time and number of waypoints needed to find the source of pollution.

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

The authors test the effectiveness of machine learning to predict onset of cardiovascular disease.

Here, recognizing the difficulty associated with tracking the progression of dementia, the authors used machine learning models to predict between the presence of cognitive normalcy, mild cognitive impairment, and Alzheimer's Disease, based on blood DNA methylation levels, sex, and age. With four machine learning models and two dataset dimensionality reduction methods they achieved an accuracy of 53.33%.