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The main goal of this study is to determine what demographics are related to tuberculosis incidence in the United States populations, particularly if changing demographics are related to differences in tuberculosis risk over two discrete time periods. The major finding is that in the two studied time periods, tuberculosis risk factors were somewhat consistent and may be influenced by things such as immigration, healthcare access, and race or ethnicity, although the top predictor did change.

Antibiotic resistance is a growing global health threat, and one strategy to combat it is using bacteriophages to enhance the effectiveness of existing antibiotics. This study tested whether targeting the TolC protein in E. coli with the TLS bacteriophage would increase bacterial sensitivity to antibiotics.

This study investigates PFAS contamination in Santa Clarita groundwater, focusing on potential sources. The study employs statistical analysis to assess data quality and trends which allowed them to identified domestic waste, fire extinguisher materials, and food packaging as the most likely sources of contamination.

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

Studying cut-resistant socks for hockey players

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

The authors looked the ability of sound sensors to predict clogged pipes when the sound intensity data is run through a machine learning algorithm.

The authors looked at genes and pathways that are enriched in glioblastoma multiforme.

The authors looked at genes that were differentially expressed in patients who had and had not had febrile seizures to determine what differences in gene expression existed.

The authors combine fine needle aspiration biopsy and machine learning algorithms to develop a breast cancer detection method suitable for resource-constrained regions that lack access to mammograms.