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In this study, the authors were interested in developing a hydrophobic surface that will extend the lifespan of metals by reducing water exposure and other damage. The used a zinc coating on steel to pursue this effort.

In this study, the authors investigate whether antibiotic-resistant bacteria can be found in everyday locations. To do this, they collected samples from multiple high-trafficked areas in Cambridge, MA and grew them in the presence and absence of antibiotics. Interestingly, they grew bacterial colonies from many locations' samples, but not all could grow in the presence of ampicillin. These findings are intriguing and relevant given the rising concern about antibiotic-resistant bacteria.

The authors looked at the ability to use audio clips to analyze the progression of Parkinson's disease.

In this study, the authors investigate the anti-cancer effects of Annona Reticulata (Ramphal or custard apple) by testing whether its extract could inhibit HeLa cell viability.

Heating of DNA-containing solutions is a part of many experiment protocols, but it can also cause damage and degradation of the DNA molecules, potentially leading to error in the experimental results. The authors of this paper investigate whether the presence of certain cations during heating can stabilize the DNA polymer and aid the preservation of the molecule.

The authors evaluated the effect of concentration and variant of N-Acetylcysteine in hydrolyzing mucus.

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

The authors developed and tested machine learning methods to diagnose tuberculosis from pulmonary X-ray scans.

The authors use machine learning on MRI images of brain tissue to predict tumor onset as an avenue for early detection of brain cancer.

Depression affects millions globally, yet identifying symptoms remains challenging. This study explored detecting depression-related patterns in social media texts using natural language processing and machine learning algorithms, including decision trees and random forests. Our findings suggest that analyzing online text activity can serve as a viable method for screening mental disorders, potentially improving diagnosis accuracy by incorporating both physical and psychological indicators.