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Human cytomegalovirus (HCMV) causes serious infections in immunocompromised patients and therapies to inhibit latent HCMV are not developed. Using CRISPR/Cas9, the authors were able to delete an important promoter region in HCMV.

In this study, the authors studied the ability for bacteria to develop antibiotic resistance over successive generations and modeled the trajectory to predict how antibiotic resistance is developed.

This study sought to determine if there is an association between the single nucleotide polymorphism rs7528684 of the Fc receptor-like-3 (FCRL3) gene and asthma or allergic rhinitis (AR). Based on previous studies in an Asian population, we hypothesized that participants with an AA genotype of FCRL3 would be more likely to have asthma and/or allergic rhinitis. To test the hypothesis, surveys were administered to participants, and genotyping was performed on spit samples via PCR, restriction digest, and gel electrophoresis.

Nonalcoholic Fatty Liver Disease (NAFLD) is a condition where a surplus of triglycerides or fat are present in the liver. In this study, ezetimibe, a cholesterol lowering drug, was used to treat flies modeling NAFLD. Compared to the coconut oil fed flies that were transferred to the control medium, the flies transferred to the control medium treated with ezetimibe showed a decrease in their triglyceride and alanine transaminase level.

Antibiotics are oftentimes used to treat mastitis (infection of the mammary gland) in dairy cows. Regulations require that milk from these cows be discarded until the infection has cleared and antibiotic residues are no longer detectable in the cow's milk. These regulations are in place to protect consumers and to help prevent the rise of antibiotic resistant bacteria. In this study, the authors test milk samples from 10 milk suppliers in the Greensboro, NC to see if they contain detectable levels of antibiotic residues.

The authors demonstrate computationally that two types of nanoplastics can bind to estrogen receptor beta and potentially disrupt endocrine systems.

This study explored how green spaces, climate, traffic, and air quality (GCTA) collectively influence asthma-related emergency department visits in the U.S using machine learning models and explainable AI.

Here, seeking to better understand how facial features provide important visual cues to help convey emotions, the authors evaluated the accuracy and reaction time of participants in regards to experimental photographs where a person's eyebrows were obscured and ones where they were not. Their findings revealed that removing eyebrows resulted in a significant decrease in a participant's ability to recognize anger, with adolescents most likely to misidentify emotions.

Pediatric cancers pose unique challenges due to their rarity and distinct biological factors, emphasizing the need for accurate survival prediction to guide treatment. This study integrated generative AI and machine learning, including synthetic data, to analyze 9,184 pediatric cancer patients, identifying age at diagnosis, cancer types, and anatomical sites as significant survival predictors. The findings highlight the potential of AI-driven approaches to improve survival prediction and inform personalized treatment strategies, with broader implications for innovative healthcare applications.

Here the authors sought to investigate whether and how cerebral stroke and other health-related variables are influenced together and amongst each other by using statistical analyses. Their analysis suggested relations between nearly all variables considered, with the strongest association between having heart disease and a cerebral stroke.