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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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Assessing machine learning model efficacy for brain tumor MRI classification: a multi-model approach

Dhingra et al. | Mar 14, 2026

Assessing machine learning model efficacy for brain tumor MRI classification: a multi-model approach
Image credit: Dhingra and Dhingra

This manuscript explores the performance of five different machine learning models in classifying brain tumors from a dataset of MRI scans. The authors find that several of the models showed >90% accuracy. Thus, the authors suggest that machine learning models demonstrate potential for effective implementation in clinical settings, including as a diagnostic tool that can be used to complement the expertise of neuroradiologists.

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