The authors test the effects of pH level on different kinds of dental cement to model the long-term effects of reflux-induced stomach acid exposure.
Read More...Investigation of the impact of acid reflux on dental cements
The authors test the effects of pH level on different kinds of dental cement to model the long-term effects of reflux-induced stomach acid exposure.
Read More...Quantifying right atrial dilation relative to atrial septal defect size using an experimental model
To address the limitations in predicting the severity of Atrial Septal Defect (ASD), here the authors utilized a fluid-filled chamber model to quantify the relationship between defect size and right atrial fluid output. The findings confirmed that larger ASD diameters result in a linear increase in fluid output, validating a cost-effective model that can improve clinical prognosis and treatment planning for heart failure risks.
Read More...Relationship between p62 and learning behavior in male and female mice deficient in hippocampal folliculin
Here the authors hypothesized that reducing folliculin (FLCN) might affect p62 protein levels in the dorsal hippocampus of mice, given their potential functional connection and p62's role in neurodegenerative diseases. Their study, using western blots and a two-way ANOVA on young wild-type mice, found that p62 levels correlated with FLCN expression, but ultimately concluded there's no evidence of a functional connection between FLCN and p62 in this specific model.
Read More...A HOG feature extraction and CNN approach to Parkinson’s spiral drawing diagnosis
Parkinson’s disease (PD) is a prevalent neurodegenerative disorder in the U.S., second only to Alzheimer’s disease. Current diagnostic methods are often inefficient and dependent on clinical exams. This study explored using machine and deep learning to enhance PD diagnosis by analyzing spiral drawings affected by hand tremors, a common PD symptom.
Read More...De novo design of a dual-target inhibitor against tau phosphorylation and acetylation for Alzheimer's therapy
The authors use computational methods to compare tau acetylation to the better studied tau phosphorylation in Alzheimer's disease and then design and computationally test a new drug to prevent abnormal post-translational modifications of tau.
Read More...Environmental contributors of asthma via explainable AI: Green spaces, climate, traffic & air quality
This study explored how green spaces, climate, traffic, and air quality (GCTA) collectively influence asthma-related emergency department visits in the U.S using machine learning models and explainable AI.
Read More...Towards multimodal longitudinal analysis for predicting cognitive decline
Understanding and predicting cognitive decline in Alzheimer's disease
Read More...The effects of Helianthus Annuus on Amyotrophic Lateral Sclerosis using Drosophila Melanogaster
Amyotrophic lateral sclerosis (ALS) affects nearly 200,000 people worldwide and there is currently no cure. The purpose of the study was to determine if Helianthus annuus seeds helped reduce nerve degeneration and increase locomotion using Drosophila melanogaster as the model organism. Through this experiment, we found a general trend suggesting that H. annuus helped increase the mobility of the D. melanogaster suggesting it could be a viable supplement for patients with ALS.
Read More...The effect of nicotine and lead on neuron morphology, function, and ɑ-Synuclein levels in a C. elegans model
E-cigarettes are often considered a healthier alternative to traditional cigarettes. This team of high school authors investigated the impact of common e-cigarette compounds on C. elegans, and found a number of harmful effects ultimately resulting in injury and neuronal damage.
Read More...Machine learning for the diagnosis of malaria: a pilot study of transfer learning techniques
The diagnosis of malaria remains one of the major hurdles to eradicating the disease, especially among poorer populations. Here, the authors use machine learning to improve the accuracy of deep learning algorithms that automate the diagnosis of malaria using images of blood smears from patients, which could make diagnosis easier and faster for many.
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