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Effects of common supplements on human platelet aggregation in vitro

Prabhakar et al. | Apr 16, 2025

Effects of common supplements on human platelet aggregation in vitro
Image credit: The authors

There is a need for safe and effective therapies to prevent platelet aggregation associated with cardiovascular diseases. Prabhakar and Prabhakar test to see whether dietary supplements claiming to reduce cardiovascular disease risk will affect aggregation of human platelets.

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Unit-price anchoring affects consumer purchasing behavior

James et al. | Jan 15, 2025

Unit-price anchoring affects consumer purchasing behavior

This study examines how anchoring—providing numerical suggestions like "2 for $4"—can influence consumer purchasing decisions and increase revenue. The researchers tested three types of price anchors on 29 high school students shopping in a mock store.

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Genetic Bioaugmentation of Oryza sativa to Facilitate Self-Detoxification of Arsenic In-Situ

Bhat et al. | Dec 03, 2024

Genetic Bioaugmentation of Oryza sativa to Facilitate Self-Detoxification of Arsenic In-Situ

Arsenic contamination in rice, caused by the use of arsenic-laden groundwater for irrigation, is a growing global concern, affecting over 150 million people. To address this, researchers hypothesized that genetically modifying rice plants with arsenic-resistant genes could reduce arsenic uptake and allow the plants to detoxify arsenic, making them safer to consume.

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Machine learning predictions of additively manufactured alloy crack susceptibilities

Gowda et al. | Nov 12, 2024

Machine learning predictions of additively manufactured alloy crack susceptibilities

Additive manufacturing (AM) is transforming the production of complex metal parts, but challenges like internal cracking can arise, particularly in critical sectors such as aerospace and automotive. Traditional methods to assess cracking susceptibility are costly and time-consuming, prompting the use of machine learning (ML) for more efficient predictions. This study developed a multi-model ML pipeline that predicts solidification cracking susceptibility (SCS) more accurately by considering secondary alloy properties alongside composition, with Random Forest models showing the best performance, highlighting a promising direction for future research into SCS quantification.

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Monitoring drought using explainable statistical machine learning models

Cheung et al. | Oct 28, 2024

Monitoring drought using explainable statistical machine learning models

Droughts have a wide range of effects, from ecosystems failing and crops dying, to increased illness and decreased water quality. Drought prediction is important because it can help communities, businesses, and governments plan and prepare for these detrimental effects. This study predicts drought conditions by using predictable weather patterns in machine learning models.

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Large Language Models are Good Translators

Zeng et al. | Oct 16, 2024

Large Language Models are Good Translators

Machine translation remains a challenging area in artificial intelligence, with neural machine translation (NMT) making significant strides over the past decade but still facing hurdles, particularly in translation quality due to the reliance on expensive bilingual training data. This study explores whether large language models (LLMs), like GPT-4, can be effectively adapted for translation tasks and outperform traditional NMT systems.

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The effect of bioenhancers on ampicillin-sulbactam as a treatment against A. baumannii

Balaji et al. | Sep 21, 2024

The effect of bioenhancers on ampicillin-sulbactam as a treatment against <i>A. baumannii<i>

This article explores the potential of piperine, a bioenhancer from black pepper, to improve antibiotic efficacy against antibiotic-resistant Acinetobacter baumannii. By combining piperine with ampicillin-sulbactam, the study demonstrated a significant reduction in bacterial growth for most strains tested, showcasing the promise of bioenhancers in combating resistant pathogens. This research highlights the possibility of reducing the required antibiotic dosage, potentially offering a new strategy in the fight against drug-resistant bacteria.

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