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The Effect of Ethanol Concentration on Beta-Cell Development in Zebrafish

Payne et al. | Jan 15, 2014

The Effect of Ethanol Concentration on Beta-Cell Development in Zebrafish

Alcohol is known to cause various developmental diseases including Fetal Alcohol Syndrome. Here the authors investigate the effect of ethanol on the development of zebrafish beta cells, the part of the pancreas associated with Type 1 Diabetes. They find that exposure to ethanol does adversely affect beta-cell development, suggesting that alcohol ingestion during pregnancy may be linked to diabetes in newborns.

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Herbal Extracts Alter Amyloid Beta Levels in SH-SY5Y Neuroblastoma Cells

Xu et al. | Feb 25, 2020

Herbal Extracts Alter Amyloid Beta Levels in SH-SY5Y Neuroblastoma Cells

Alzheimer’s disease (AD) is a type of dementia that affects more than 5.5 million Americans, and there are no approved treatments that can delay the advancement of the disease. In this work, Xu and Mitchell test the effects of various herbal extracts (bugleweed, hops, sassafras, and white camphor) on Aβ1-40 peptide levels in human neuroblastoma cells. Their results suggest that bugleweed may have the potential to reduce Aβ1-40 levels through its anti-inflammatory properties.

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Modulation of VEGF and TGF beta in 5-FU induced inflammation in MCF-7 using an herbal formulation

Vinay Nair et al. | Jun 03, 2022

Modulation of VEGF and TGF beta in 5-FU induced inflammation in MCF-7 using an herbal formulation

Acquired drug resistance is an increasing challenge in treating cancer with chemotherapy. One mechanism
behind this resistance is the increased inflammation that supports the progression and development of
cancer that arises because of the drug’s presence. Integrative oncology is the field that focuses on including natural products alongside traditional therapy to create a treatment that focuses on holistic patient well-being.
In this study, the authors demonstrate that the use of an herbal formulation, consisting of turmeric and green tea, alongside a traditional chemotherapeutic drug, 5-fluorouracil (FU) significantly decreases the level of cytokines produced in breast cancer cells when compared to the levels produced when exposed solely to the chemo drug. The authors conclude that this combination of treatment, based on the principle of integrative oncology, shows potential for reducing the resistance against treatment conferred through increased inflammation. Consequently, this suggests a prospective way forward in improving the efficacy of cancer treatment.

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The Effect of Wind Mitigation Devices on Gabled Roofs

Kaufman et al. | Feb 20, 2021

The Effect of Wind Mitigation Devices on Gabled Roofs

The purpose of this study was to test devices installed on a gabled roof to see which reduced the actual uplift forces best. Three gabled birdhouse roofs were each modified with different mitigation devices: a rounded edge, a barrier shape, or an airfoil. The barrier edge had no significant effect on the time for the roof to blow off. The addition of airfoil devices on roofs, specifically in areas that are prone to hurricanes such as Florida, could keep roofs in place during hurricanes, thus reducing insurance bills, overall damage costs, and the loss of lives.

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Assessing CDK5 as a Nanomotor for Chemotactic Drug Delivery

Jiang et al. | Sep 08, 2022

Assessing CDK5 as a Nanomotor for Chemotactic Drug Delivery

Enzyme chemotaxis is a thermodynamic phenomenon in which enzymes move along a substrate concentration gradient towards regions with higher substrate concentrations and can be used to steer nanovehicles towards targets along natural substrate concentrations. In patients with Alzheimer’s disease, a gradient of tau protein forms in the bloodstream. Tau protein is a substrate of the enzyme CDK5, which catalyzes the phosphorylation of tau protein and can travel using chemotaxis along tau protein gradients to increasing concentrations of tau and amyloid-beta proteins. The authors hypothesized that CDK5 would be able to overcome these barriers of Brownian motion and developed a quantitative model using Michaelis-Menten kinetics to define the necessary parameters to confirm and characterize CDK5’s chemotactic behavior to establish its utility in drug delivery and other applications.

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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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Prediction of preclinical Aβ deposit in Alzheimer’s disease mice using EEG and machine learning

Igarashi et al. | Nov 29, 2022

Prediction of preclinical Aβ deposit in Alzheimer’s disease mice using EEG and machine learning

Alzheimer’s disease (AD) is a common disease affecting 6 million people in the U.S., but no cure exists. To create therapy for AD, it is critical to detect amyloid-β protein in the brain at the early stage of AD because the accumulation of amyloid-β over 20 years is believed to cause memory impairment. However, it is difficult to examine amyloid-β in patients’ brains. In this study, we hypothesized that we could accurately predict the presence of amyloid-β using EEG data and machine learning.

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