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String analysis of exon 10 of the CFTR gene and the use of Bioinformatics in determination of the most accurate DNA indicator for CF prediction

Carroll et al. | Jul 12, 2020

String analysis of exon 10 of the CFTR gene and the use of Bioinformatics in determination of the most accurate DNA indicator for CF prediction

Cystic fibrosis is a genetic disease caused by mutations in the CFTR gene. In this paper, the authors attempt to identify variations in stretches of up to 8 nucleotides in the protein-coding portions of the CFTR gene that are associated with disease development. This would allow screening of newborns or even fetuses in utero to determine the likelihood they develop cystic fibrosis.

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A Study on the Coagulating Properties of the M. oleifera Seed

Lakshmanan et al. | Feb 14, 2020

A Study on the Coagulating Properties of the <em>M. oleifera</em> Seed

In this study, the authors investigate whether Moringa Oleifera seeds can serve as material to aid in purifying water. M. oleifera seeds have coagulating properties and the authors hypothesized that including it in a water filtration system would reduce particles, specifically bacteria, in water. Their results show that this system removed the largest percent of bacteria. When used in combination with cilantro, it was actually more efficient than the other techniques! These findings have important implications for creating better and more economical water purification systems.

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Effects of Wi-Fi EMF on Drosophila melanogaster

Anand et al. | Jan 29, 2020

Effects of Wi-Fi EMF on <em>Drosophila melanogaster</em>

While increased access to Wi-Fi has been a great advancement, we have a limited understanding if there are any health effects on animals. In this study, Anand and Anand exposed fruit flies (Drosophila melanogaster) to different concentrations of Wi-Fi electromagnetic fields, and observed effects on their reproduction and survivability.

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Determining viability of image processing models for forensic analysis of hair for related individuals

Wang et al. | Feb 04, 2025

Determining viability of image processing models for forensic analysis of hair for related individuals
Image credit: Taylor Smith

Here, the authors used machine learning to analyze microscopic images of hair, quantifying various features to distinguish individuals, even within families where traditional DNA analysis is limited. The Discriminant Analysis (DA) model achieved the highest accuracy (88.89%) in identifying individuals, demonstrating its potential to improve the reliability of hair evidence in forensic investigations.

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Country-level relationship of OTC medicine consumption and frequency of GP consultation

Hirai et al. | Dec 09, 2024

Country-level relationship of OTC medicine consumption and frequency of GP consultation

The discussion surrounding self-medication with non-prescription medicines has gained significance in healthcare and public health, particularly given the global increase in consumption of non-prescription drugs. This study aimed to examine the association between the frequency of general practitioner (GP) consultations and the proportion of economic resources spent on OTC medicine.

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An exploration of western mosquitofish as the animal component in an aquaponic farming system

Medina et al. | Dec 03, 2024

An exploration of western mosquitofish as the animal component in an aquaponic farming system
Image credit: The authors

Aquaponics (the combination of aquatic plant farming with fish production) is an innovative farming practice, but the fish that are typically used, like tilapia, are expensive and space-consuming to cultivate. Medina and Alvarez explore other options test if mosquitofish are a viable option in the aquaponic cultivation of herbs and microgreens.

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Using broad health-related survey questions to predict the presence of coronary heart disease

Chavda et al. | Aug 23, 2024

Using broad health-related survey questions to predict the presence of coronary heart disease

Coronary heart disease (CHD) is the leading cause of death in the U.S., responsible for nearly 700,000 deaths in 2021, and is marked by artery clogging that can lead to heart attacks. Traditional prediction methods require expensive clinical tests, but a new study explores using machine learning on demographic, clinical, and behavioral survey data to predict CHD.

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