Browse Articles

Antibacterial Effects of Copper Surfaces

Mulukutla et al. | May 19, 2020

Antibacterial Effects of Copper Surfaces

This study examined the ability of copper and copper alloy surfaces to inhibit bacterial growth, which may be help prevent healthcare-associated infections. The authors exposed two non-pathogenic strains of bacteria to different metal plates for varying degrees of time and measured bacterial growth.

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The Effects of Micro-Algae Characteristics on the Bioremediation Rate of Deepwater Horizon Crude Oil

Cao et al. | Jun 17, 2013

The Effects of Micro-Algae Characteristics on the Bioremediation Rate of Deepwater Horizon Crude Oil

Environmental disasters such as the Deepwater Horizon oil spill can be devastating to ecosystems for long periods of time. Safer, cheaper, and more effective methods of oil clean-up are needed to clean up oil spills in the future. Here, the authors investigate the ability of natural ocean algae to process crude oil into less toxic chemicals. They identify Coccochloris elabens as a particularly promising algae for future bioremediation efforts.

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Comparative analysis of CO2 emissions of electric ride-hailing vehicles over conventional gasoline personal vehicles

Raman et al. | Jan 12, 2024

Comparative analysis of CO<sub>2</sub> emissions of electric ride-hailing vehicles over conventional gasoline personal vehicles
Image credit: Paul Hanaoka

While some believe that ride-hailing services offer reduced CO2 emissions compared to individual driving, studies have found that driving without passengers on ride-hailing trips or "deadheading" prevents this. Here, with a mathematical model, the authors investigated if the use of electric vehicles as ride-hailing vehicles could offer reduced CO2 emissions. They found that the improved vehicle efficiency and cleaner generation could in fact lower emissions compared to the use of personal gas vehicles.

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Combating Insulin Resistance Using Medicinal Plants as a Supplementary Therapy to Metformin in 3T3-L1 Adipocytes: Improving Early Intervention-Based Diabetes Treatment

Jayram et al. | Apr 08, 2019

Combating Insulin Resistance Using Medicinal Plants as a Supplementary Therapy to Metformin in 3T3-L1 Adipocytes: Improving Early Intervention-Based Diabetes Treatment

A primary cause of diabetes is insulin resistance, which is caused by disruption of insulin signal transduction. The objective of this study was to maximize insulin sensitivity by creating a more effective, early intervention-based treatment to avert severe T2D. This treatment combined metformin, “the insulin sensitizer”, and medicinal plants, curcumin, fenugreek, and nettle.

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Friend or Foe: Investigating the Relationship between a Corn Crop and a Native Ragweed Population

Wainwright et al. | May 07, 2014

Friend or Foe: Investigating the Relationship between a Corn Crop and a Native Ragweed Population

Farmers will need to increase crop yields to feed the world's growing population efficiently. The authors here investigate the effects of growing corn in the presence or absence of ragweed, an invasive weed found in many fields and gardens. Surprisingly, the authors found that corn grown in the presence of weeds grew taller and were more productive than corn that had weeds removed. This may help gardeners rethink the necessity of weeding, and may point a way to improve farm yields in the future.

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Applying centrality analysis on a protein interaction network to predict colorectal cancer driver genes

Saha et al. | Nov 18, 2023

Applying centrality analysis on a protein interaction network to predict colorectal cancer driver genes

In this article the authors created an interaction map of proteins involved in colorectal cancer to look for driver vs. non-driver genes. That is they wanted to see if they could determine what genes are more likely to drive the development and progression in colorectal cancer and which are present in altered states but not necessarily driving disease progression.

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Development of a novel machine learning platform to identify structural trends among NNRTI HIV-1 reverse transcriptase inhibitors

Ashok et al. | Jun 24, 2022

Development of a novel machine learning platform to identify structural trends among NNRTI HIV-1 reverse transcriptase inhibitors

With advancements in machine learning a large data scale, high throughput virtual screening has become a more attractive method for screening drug candidates. This study compared the accuracy of molecular descriptors from two cheminformatics Mordred and PaDEL, software libraries, in characterizing the chemo-structural composition of 53 compounds from the non-nucleoside reverse transcriptase inhibitors (NNRTI) class. The classification model built with the filtered set of descriptors from Mordred was superior to the model using PaDEL descriptors. This approach can accelerate the identification of hit compounds and improve the efficiency of the drug discovery pipeline.

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