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The Effects of Micro-Algae Characteristics on the Bioremediation Rate of Deepwater Horizon Crude Oil

Cao et al. | Jun 17, 2013

The Effects of Micro-Algae Characteristics on the Bioremediation Rate of Deepwater Horizon Crude Oil

Environmental disasters such as the Deepwater Horizon oil spill can be devastating to ecosystems for long periods of time. Safer, cheaper, and more effective methods of oil clean-up are needed to clean up oil spills in the future. Here, the authors investigate the ability of natural ocean algae to process crude oil into less toxic chemicals. They identify Coccochloris elabens as a particularly promising algae for future bioremediation efforts.

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Examining effects of E. muscae on olfactory function in D. melanogaster

Friedman et al. | Jul 08, 2021

Examining effects of <em>E. muscae</em> on olfactory function in <em>D. melanogaster</em>

In this article, the authors investigate the effects of fungus E. muscae on fruit fly behavior. More specifically, they investigate whether this fungus affects olfaction. Their findings contribute to a broader set of studies seeking to understand how host's central nervous systems can be affected by infections.

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The role of CYP46A1 and its metabolic product, 24S-hydroxycholesterol, in Neuro 2A cell death

Ni et al. | May 11, 2021

The role of CYP46A1 and its metabolic product, 24S-hydroxycholesterol, in Neuro 2A cell death

Cholesterol is a major component of neuronal cell membrane and myelin sheath. In this study, the authors either transfected Neuro 2A cells with CYP46A1 cDNA or treated the cells with 24SHC. Cells expressing CYP46A1 had significantly less viability compared to the negative control. Up to 55% reduction in cell viability was also observed in 24S-HC-treated cells. This work supports that CYP46A1 and 24S-HC could directly trigger cell death. The direct involvement of 24S-HC in cell death provides further evidence that 24S-HC can be a promising biomarker for diagnosing brain damage severity.

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Improving Wound Healing by Breaking Down Biofilm Formation and Reducing Nosocomial Infections

DiStefano et al. | Jul 09, 2019

Improving Wound Healing by Breaking Down Biofilm Formation and Reducing Nosocomial Infections

In a 10-year period in the early 2000’s, hospital-based (nosocomial) infections increased by 123%, and this number is increasing as time goes on. The purpose of this experiment was to use hyaluronic acid, silver nanoparticles, and a bacteriophage cocktail to create a hydrogel that promotes wound healing by increasing cell proliferation while simultaneously disrupting biofilm formation and breaking down Staphylococcus aureus and Pseudomonas aeruginosa, which are two strains of bacteria that attribute to nosocomial infections and are increasing in antibiotic resistance.

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Using explainable artificial intelligence to identify patient-specific breast cancer subtypes

Suresh et al. | Jan 12, 2024

Using explainable artificial intelligence to identify patient-specific breast cancer subtypes

Breast cancer is the most common cancer in women, with approximately 300,000 diagnosed with breast cancer in 2023. It ranks second in cancer-related deaths for women, after lung cancer with nearly 50,000 deaths. Scientists have identified important genetic mutations in genes like BRCA1 and BRCA2 that lead to the development of breast cancer, but previous studies were limited as they focused on specific populations. To overcome limitations, diverse populations and powerful statistical methods like genome-wide association studies and whole-genome sequencing are needed. Explainable artificial intelligence (XAI) can be used in oncology and breast cancer research to overcome these limitations of specificity as it can analyze datasets of diagnosed patients by providing interpretable explanations for identified patterns and predictions. This project aims to achieve technological and medicinal goals by using advanced algorithms to identify breast cancer subtypes for faster diagnoses. Multiple methods were utilized to develop an efficient algorithm. We hypothesized that an XAI approach would be best as it can assign scores to genes, specifically with a 90% success rate. To test that, we ran multiple trials utilizing XAI methods through the identification of class-specific and patient-specific key genes. We found that the study demonstrated a pipeline that combines multiple XAI techniques to identify potential biomarker genes for breast cancer with a 95% success rate.

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Predicting the factors involved in orthopedic patient hospital stay

D’Souza et al. | Dec 13, 2023

Predicting the factors involved in orthopedic patient hospital stay
Image credit: Pixabay

Long hospital stays can be stressful for the patient for many reasons. We hypothesized that age would be the greatest predictor of hospital stay among patients who underwent orthopedic surgery. Through our models, we found that severity of illness was indeed the highest factor that contributed to determining patient length of stay. The other two factors that followed were the facility that the patient was staying in and the type of procedure that they underwent.

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