Browse Articles

The authors looked at the ability to use audio clips to analyze the progression of Parkinson's disease.

Here the authors investigated the use of an affordable gas sensor to detect volatile organic compound (VOC) emissions as an early indicator of tree disease, finding statistically significant differences in VOCs between diseased and non-diseased ash, beech, and maple trees. They suggest this sensor has potential for widespread early disease detection, but call for further research with larger sample sizes and diverse locations.

The authors use known Parkinson's disease-associated genetic variants to compare the prevalence of sleep dysfunction between males and females diagnosed with Parkinson's disease.

The authors surveyed individuals diagnosed with coronary artery disease about their mental health to study a potential connection between coronary artery disease and depression.

Enzymes that metabolize carbohydrates and lipids play a key role in our health, including global health challenges like cardiovascular diseases and diabetes. To learn more about these important enzymes, Gandhi and Gandhi test whether various natural substances (ginger, Aloe vera, lemon, and mint leaves) affect the activity of α-amylase and lipase enzymes.

Coronary heart disease (CHD) is the leading cause of death in the U.S., responsible for nearly 700,000 deaths in 2021, and is marked by artery clogging that can lead to heart attacks. Traditional prediction methods require expensive clinical tests, but a new study explores using machine learning on demographic, clinical, and behavioral survey data to predict CHD.

The authors use molecular modeling to test analogs of the stearoyl-coenzyme A desaturase 1 (SCD1) inhibitor MF-438 with implications for future development of Parkinson's disease therapeutics.

The authors compare neuroimaging datasets to identify potential new biomarkers for earlier detection of Alzheimer's disease.

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%.

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.