Browse Articles

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.

Oxidative damage and neuro-inflammation were the key pathways implicated in the pathogenesis of Alzheimer’s disease. In this study, 30 natural extracts from plant roots and leaves with extensive anti-inflammatory and anti-oxidative properties were consumed by Drosophila melanogaster. Several assays were performed to evaluate the efficacy of these combinational extracts on delaying the progression of Alzheimer’s disease. The experimental group showed increased motor activity, improved associative memory, and decreased lifespan decline relative to the control group.

In this study, the authors investigate whether Eisenia Fetida nerve signal speed correlates with Withania somnifera ingestion, a possible way to protect against demyelination.

In the battle against Alzheimer's disease, early detection is critical to mitigating symptoms in patients. Here, the authors use a collection of MRI scans, layering with deep learning computer modeling, to investigate early stages of AD which can be hard to catch by human eye. Their model is successful, able to outperform previous models, and detected regions of interest in the brain for further consideration.

In this study, the authors created a survey to assess misconceptions and knowledge deficits regarding cardiovascular diseases exist among US adults and minors.

Despite the prevalence of PD, diagnosing PD is expensive, requires specialized testing, and is often inaccurate. Moreover, diagnosis is often made late in the disease course when treatments are less effective. Using existing voice data from patients with PD and healthy controls, the authors created and trained two different algorithms: one using logistic regression and another employing an artificial neural network (ANN).

In this study, the authors ask whether a Tau immunotherapy treatment, Hsp70 protein treatment, or dual treatment approach of both the Tau imunotherapy treatment and Hsp70 protein treatment leads to a greater reduction in Tau protein concentration in Alzheimer's disease. Overall, they conclude that the effectiveness of the treatment ultimately relies on the stage of Alzheimer’s.

Nonalcoholic Fatty Liver Disease (NAFLD) is a condition where a surplus of triglycerides or fat are present in the liver. In this study, ezetimibe, a cholesterol lowering drug, was used to treat flies modeling NAFLD. Compared to the coconut oil fed flies that were transferred to the control medium, the flies transferred to the control medium treated with ezetimibe showed a decrease in their triglyceride and alanine transaminase level.

Studies show an age-related link between Alzheimer’s Disease and Type 2 Diabetes Mellitus with oxidative stress a characteristic of both. Here, methanolic fractionations and extracts of four Ayurvedic plants were assessed for their protective abilities using a number of in vitro assays. Extracts inhibited oxidative stress and reduced activity of key enzymes involved in the pathogenesis of both diseases in neuroblastoma cells.