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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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The Clinical Accuracy of Non-Invasive Glucose Monitoring for ex vivo Artificial Pancreas

Levy et al. | Jul 10, 2016

The Clinical Accuracy of Non-Invasive Glucose Monitoring for <i>ex vivo</i> Artificial Pancreas

Diabetes is a serious worldwide epidemic that affects a growing portion of the population. While the most common method for testing blood glucose levels involves finger pricking, it is painful and inconvenient for patients. The authors test a non-invasive method to measure glucose levels from diabetic patients, and investigate whether the method is clinically accurate and universally applicable.

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Enhancing marine debris identification with convolutional neural networks

Wahlig et al. | Apr 03, 2024

Enhancing marine debris identification with convolutional neural networks
Image credit: The authors

Plastic pollution in the ocean is a major global concern. Remotely Operated Vehicles (ROVs) have promise for removing debris from the ocean, but more research is needed to achieve full effectiveness of the ROV technology. Wahlig and Gonzales tackle this issue by developing a deep learning model to distinguish trash from the environment in ROV images.

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Transfer Learning for Small and Different Datasets: Fine-Tuning A Pre-Trained Model Affects Performance

Gupta et al. | Oct 18, 2020

Transfer Learning for Small and Different Datasets: Fine-Tuning A Pre-Trained Model Affects Performance

In this study, the authors seek to improve a machine learning algorithm used for image classification: identifying male and female images. In addition to fine-tuning the classification model, they investigate how accuracy is affected by their changes (an important task when developing and updating algorithms). To determine accuracy, a set of images is used to train the model and then a separate set of images is used for validation. They found that the validation accuracy was close to the training accuracy. This study contributes to the expanding areas of machine learning and its applications to image identification.

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A Cloud-Enabled Communication Strategy for Wildfire Alerts

Vinaithirthan et al. | Jul 19, 2020

A Cloud-Enabled Communication Strategy for Wildfire Alerts

The traditional alert system in California consists of Wireless Emergency Alerts (WEAs), which lack location specificity, and sign-up-based technology which is limited by the number of sign ups. Those who do not have phones or have a silence option on their devices are most at risk from the current alert system. Here the authors developed cloud-enabled crisis connection for disaster alerts (CRISIS-CONNECT) to mitigate problems associated with the current alert system.

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Developing a Portable, Reusable, and Inexpensive Magnesium-Air Fuel Cell

Tota et al. | Mar 28, 2019

Developing a Portable, Reusable, and Inexpensive Magnesium-Air Fuel Cell

One of the greatest challenges we face today is the sustainable production, storage, and distribution of electrical power. One emerging technology with great promise in this area is that of metal-air fuel cells—a long-term and reusable electricity storage system made from a reactive metal anode and a saline solution. In this study the authors tested several different types of metal to determine which was the most suitable for this application. They found that a fuel cell with a magnesium anode was superior to fuel cells made from aluminum or zinc, producing a voltage and current sufficient for real-world applications such as charging a mobile phone.

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Impact of daf-25 and daf-11 Mutations on Olfactory Function in C. elegans

Gardner et al. | Feb 02, 2019

Impact of daf-25 and daf-11 Mutations on Olfactory Function in C. elegans

Cilia are little hair-like protrusions on many cells in the human body, including those lining the trachea where they play a role in clearing our respiratory tract of mucous and other irritants. Genetic mutations that impair ciliary function have serious consequences on our well-being making it important to understand how ciliary function is regulated. By using a simple organism, such as the worm C. elegans that use cilia to move, the authors explore the effect of certain genetic mutations on the cilia of the worms by measuring their ability to move towards or away from certain odorants.

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