Browse Articles

Analyzing resilience in a sample population as a novel qualifier for triage in psychological first aid

Ramesh et al. | Apr 18, 2023

Analyzing resilience in a sample population as a novel qualifier for triage in psychological first aid
Image credit: Mat Napo

While serving as an immediate address for psychological safety and stability, psychological first aid (PFA) currently lacks the incorporation of triage. Without triage, patients cannot be prioritized in correspondence to condition severity that is often called for within emergency conditions. To disentangle the relevance of a potential triage system to PFA, the authors of this paper have developed a method to quantify resilience - a prominent predictor of the capability to recover from a disaster. With this resilience index, they have quantified resilience of differing age, race, and sex demographics to better inform the practice of PFA and potential demographic prioritization via a triage system.

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The effect of adverse childhood experiences on e-cigarette usage in people aged 18–30 in the US

Bloomer et al. | Oct 06, 2022

The effect of adverse childhood experiences on e-cigarette usage in people aged 18–30 in the US

Recently, e-cigarette usage has been increasing rapidly. Previous research has found that adverse
childhood experiences (ACEs) are correlated to cigarette usage. However, there is limited data exploring if ACEs affect vaping. Therefore, in this work, we investigated the effects of ACEs on e-cigarette usage and hypothesize that witnessing vaping in the house and facing ACEs would increase e-cigarette usage while education on the dangers of vaping would decrease e-cigarette usage. We found that different types of ACEs had different correlations with e-cigarette usage and that education on the dangers of vaping had no effect on e-cigarette usage.

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The effects of Helianthus Annuus on Amyotrophic Lateral Sclerosis using Drosophila Melanogaster

Srinivasan et al. | Oct 13, 2022

The effects of <em>Helianthus Annuus</em> on Amyotrophic Lateral Sclerosis using <em>Drosophila Melanogaster</em>

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.

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The correlation between bacteria and colorectal cancer

Zhang et al. | Oct 21, 2024

The correlation between bacteria and colorectal cancer

The authors looked at abundance of bacteria in stool samples from patients with colorectal cancer compared to controls. They found different bacteria that was more prevalent in patients with colorectal cancer as well as bacteria in control patients that may indicate a beneficial gut microbiome.

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Adults’ attitudes toward non-alcoholic beer purchases and consumption by children and adolescents

An et al. | Aug 23, 2024

Adults’ attitudes toward non-alcoholic beer purchases and consumption by children and adolescents
Image credit: The authors

Consumption of non-alcoholic beverages, like non-alcoholic beer, is growing in popularity in the United States. These beverages raise important societal questions, such as whether minors should be allowed to purchase or consume non-alcoholic beer. An and An investigate this issue by surveying adults to see if they support minors purchasing and consuming non-alcoholic beer.

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Diagnosing hypertrophic cardiomyopathy using machine learning models on CMRs and EKGs of the heart

Kolluri et al. | Jul 29, 2024

Diagnosing hypertrophic cardiomyopathy using machine learning models on CMRs and EKGs of the heart
Image credit: Jesse Orrico

Here seeking to develop a method to diagnose, hypertrophic cardiomyopathy which can cause sudden cardiac death, the authors investigated the use of a convolutional neural network (CNN) and long short-term memory (LSTM) models to classify cardiac magnetic resonance and heart electrocardiogram scans. They found that the CNN model had a higher accuracy and precision and better other qualities, suggesting that machine learning models could be valuable tools to assist physicians in the diagnosis of hypertrophic cardiomyopathy.

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