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A novel approach for predicting Alzheimer’s disease using machine learning on DNA methylation in blood

Adami et al. | Sep 20, 2023

A novel approach for predicting Alzheimer’s disease using machine learning on DNA methylation in blood
Image credit: National Cancer Institute

Here, recognizing the difficulty associated with tracking the progression of dementia, the authors used machine learning models to predict between the presence of cognitive normalcy, mild cognitive impairment, and Alzheimer's Disease, based on blood DNA methylation levels, sex, and age. With four machine learning models and two dataset dimensionality reduction methods they achieved an accuracy of 53.33%.

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The Emergence of Tetracycline Resistance in Rumen Bacteria

Memili et al. | Sep 16, 2016

The Emergence of Tetracycline Resistance in Rumen Bacteria

The emergence of antibiotic-resistant pathogenic bacteria is a major concern for human health, rendering some antibiotics ineffective in treating diseases. The authors of this study tested the hypothesis that exposing rumen bacteria to tetracycline will gradually lead to the development of tetracycline-resistant bacteria, some of which will develop multidrug resistance.

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An Analysis of Soil Microhabitats in Revolutionary War, Civil War, and Modern Graveyards on Long Island, NY

Caputo et al. | May 05, 2019

An Analysis of Soil Microhabitats in Revolutionary War, Civil War, and Modern Graveyards on Long Island, NY

Previously established data indicate that cemeteries have contributed to groundwater and soil pollution, as embalming fluids can impact the microbiomes that exist in decomposing remains. In this study, Caputo et al hypothesized that microbial variation would be high between cemeteries from different eras due to dissimilarities between embalming techniques employed, and furthermore, that specific microbes would act as an indication for certain contaminants. Overall, they found that there is a variation in the microbiomes of the different eras’ cemeteries according to the concentrations of the phyla and their more specific taxa.

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The Clinical Accuracy of Non-Invasive Glucose Monitoring for ex vivo Artificial Pancreas

Levy et al. | Jul 10, 2016

The Clinical Accuracy of Non-Invasive Glucose Monitoring for <i>ex vivo</i> Artificial Pancreas

Diabetes is a serious worldwide epidemic that affects a growing portion of the population. While the most common method for testing blood glucose levels involves finger pricking, it is painful and inconvenient for patients. The authors test a non-invasive method to measure glucose levels from diabetic patients, and investigate whether the method is clinically accurate and universally applicable.

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