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The authors tested the impact of various additives on biopolymer degradation.

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 looked at the correlation between C reactive protein levels and neurological deficits in patients who had suffered an ischemic stroke.

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

The authors looked at how soil temperature changes with fire to develop a sensor system that could aid in earlier detection of fires.

Due to a critical shortage of donor hearts, researchers are exploring cardiac xenotransplantation—transplanting animal hearts into humans—as a potential solution. This study synthesized nearly two decades of preclinical research to evaluate multiple factors affecting xenograft survival.

Kalirin is a guanine nucleotide exchange factor (GEF) for the GTPase RAC1, linked to schizophrenia and Alzheimer’s Disease. It plays a crucial role in synaptic plasticity by regulating dendritic spine formation and actin cytoskeleton remodeling, which are essential for creating new synapses. Authors developed two novel compounds targeting kalirin, confirming that predictive modeling can indicate biological activity.

The authors looked at the effects of commonly used anesthetics in rodents on brain activity (specifically quasi-periodic patterns). Understanding effects on brain activity is important for researchers to understand when choosing rodent models for disease.

The study discusses the relationship between bacterial species and the human gut microbiome, emphasizing the role of quorum sensing molecules in bacterial communication and its implications for health. Authors investigated the impact of bacterial supernatants from Escherichia coli (E. coli) on the growth of new E. coli and Enterobacter aerogenes (E. aerogenes) cultures.

Here, in an effort to develop a model to predict future groundwater levels, the authors tested a tree-based automated artificial intelligence (AI) model against other methods. Through their analysis they found that groundwater levels in Texas aquifers are down significantly, and found that tree-based AI models most accurately predicted future levels.