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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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The effects of plasticizers on the mechanical properties and chemical composition of a gelatin biopolymer

Ip et al. | Jul 28, 2024

The effects of plasticizers on the mechanical properties and chemical composition of a gelatin biopolymer

Here, in an effort to identify alternatives to oil-based plastic, the authors sought to investigate the effects of plasticizers on the mechanical properties and chemical composition of gelatin bioplastic matrices. Through measurements of their tensile strength and elongation at break, along with FTIR spectroscopy, they identified 3% w/v polyethylene glycol film as having the best performance in their study..

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Transcriptomic profiling identifies differential gene expression associated with childhood abuse

Li et al. | Jul 23, 2024

Transcriptomic profiling identifies differential gene expression associated with childhood abuse
Image credit: The authors

Childhood abuse has severe and lasting effects throughout an individual's life, and may even have long-term biological effects on individuals who suffer it. To learn more about the effects of abuse in childhood, Li and Yearwood analyze gene expression data to look for genes differentially expressed genes in individuals with a history of childhood abuse.

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Using Gravitational Waves to Determine if Primordial Black Holes are Sources of Dark Matter

Sivakumar et al. | Jul 15, 2024

Using Gravitational Waves to Determine if Primordial Black Holes are Sources of Dark Matter

In the quest to understand dark matter, scientists face a profound mystery. Two compelling candidates, Massive Compact Halo Objects (MACHOs) and Weakly Interacting Massive Particles (WIMPs), have emerged as potential sources. By analyzing gravitational waves from binary mergers involving these black holes, authors sought to determine if MACHOs could be the elusive dark matter.

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The effects of age on quality of mental health during the COVID-19 pandemic

Bui et al. | Jul 15, 2024

The effects of age on quality of mental health during the COVID-19 pandemic

The impact of age on mental health is a crucial yet understudied aspect of public health. While mental health is gaining recognition as a vital component of overall well-being, its correlation with age remains largely unexplored. In Canada, where the median age has risen significantly over the past half-century, understanding this relationship becomes increasingly pertinent. Researchers hypothesized that older adults would exhibit lower rates of mental health disorders and report better perceived mental health due to increased emotional stability and maturity.

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Mechanistic deconvolution of autoreduction in tetrazolium-based cell viability assays

Tran et al. | Jul 12, 2024

Mechanistic deconvolution of autoreduction in tetrazolium-based cell viability assays

Optical reporters like tetrazolium dyes, exemplified by 5-diphenyl tetrazolium bromide (MTT), are effective tools for quantifying cellular responses under experimental conditions. These dyes assess cell viability by producing brightly-colored formazan dyes when reduced inside active cells. However, certain small molecules, including reducing agents like ascorbic acid, cysteine, and glutathione (GSH), can interfere with MTT assays, potentially compromising accuracy.

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