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Evaluating the antimicrobial activity of maitake mushroom extract against Staphylococcus epidermidis

Shamsudeen et al. | Oct 06, 2024

Evaluating the antimicrobial activity of maitake mushroom extract against <i>Staphylococcus epidermidis</i>
Image credit: Guido Blokker

Here, seeking to explore new antimicrobial therapies, the authors investigated the antimicrobial activity of Maitake mushroom extract against Staphylococcus epidermidis, a common cause of antibiotic resistant hospital-acquired infections. They found that Maitake extract showed potent antimicrobial activity, with higher concentrations showing inhibition comparable to tetracycline.

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Vineyard vigilance: Harnessing deep learning for grapevine disease detection

Mandal et al. | Aug 21, 2024

Vineyard vigilance: Harnessing deep learning for grapevine disease detection

Globally, the cultivation of 77.8 million tons of grapes each year underscores their significance in both diets and agriculture. However, grapevines face mounting threats from diseases such as black rot, Esca, and leaf blight. Traditional detection methods often lag, leading to reduced yields and poor fruit quality. To address this, authors used machine learning, specifically deep learning with Convolutional Neural Networks (CNNs), to enhance disease detection.

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Giving Teens a Voice: Sources of Stress for High School Students

Corson et al. | Sep 09, 2019

Giving Teens a Voice: Sources of Stress for High School Students

The authors investigate the negative effects stress has on teen mental and physical health. Through a survey, they give Virginia teens a voice in revising the Health and Physical Education curriculum to include a standards of learning (SOL). Notably they identify factors contributing to stress levels including homework level, amount of free and sleep time, parental pressure and family encouragement.

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Innovative fake health news detection: Integrating emotional features into graph neural networks

Wang et al. | Jul 03, 2026

Innovative fake health news detection: Integrating emotional features into graph neural networks
Image credit: Wang and Wang

This manuscript tackles a major social issue in the health news sector, with social media being one of the primary sources of information and a prime spot to propagate fake news. The author proposes X-HND , which is a unique architecture that combines emotional and contextual analysis in a Graph Neural Network to accurately detect fake news. This was a multi-step process which involved the creation of a custom health news dataset (HNDataset), and an emotional variant that uses RoBERTa to extract emotion. These dataset were then used to prove the hypothesis that accuracy increases when the custom dataset is used to train the model and that with the integration of emotion capture, the detection accuracy increases further.

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