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

Over the last few decades, childhood stunting has persisted as a major global challenge. This study hypothesized that TPTO (Tree-based Pipeline Optimization Tool), an AutoML (automated machine learning) tool, would outperform all pre-existing machine learning models and reveal the positive impact of economic prosperity, strong familial traits, and resource attainability on reducing stunting risk. Feature correlation plots revealed that maternal height, wealth indicators, and parental education were universally important features for determining stunting outcomes approximately two years after birth. These results help inform future research by highlighting how demographic, familial, and socio-economic conditions influence stunting and providing medical professionals with a deployable risk assessment tool for predicting childhood stunting.

Here, the authors investigated the integration of large language models (LLMs) with drug target affinity predictors (DTAPs) to improve drug repurposing, demonstrating a significant increase in prediction accuracy, particularly with GPT-4, for psychotropic drugs and the sigma-1 receptor. This novel approach offers to potentially accelerate and reduce the cost of drug discovery by efficiently identifying new therapeutic uses for existing drugs.

Here the authors investigated the natural recovery of stress-induced dopamine-related gene deficits in C. elegans by measuring the expression of cat-2 (dopamine biosynthesis) and sod-2 (oxidative stress) following exposure to starvation or hydrocortisone. They found that the reversibility of sod-2 and the expression of cat-2 were highly dependent on the type and severity of the stressor, suggesting that the body's natural ability to recover from dopamine dysfunction has biological limitations.

The authors looked at the feasibility to predict wind speeds that will have less reliance on using historical data.

The authors seek to accelerate wound healing by reducing inflammation with a cocktail containing growth factors and bioactive modulators.

The authors looked at how the presence of difference antibiotic resistance genes would influence antibiotic resistance in Staphylococcus aureus.

The authors looked at how pharmacokinetics changed depending on the use of an in vitro or an in vivo model.

The study explored converting Gracilaria seaweed waste—known for releasing toxic hydrogen sulfide when decomposed—into biochar as a sustainable solution for waste management and soil improvement.

The authors train a neural network to detect text-based emotions including joy, sadness, anger, fear, love, and surprise.