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Testing HCN1 channel dysregulation in the prefrontal cortex using a novel piezoelectric silk neuromodulator

Mathew et al. | May 05, 2021

Testing HCN1 channel dysregulation in the prefrontal cortex using a novel piezoelectric silk neuromodulator

Although no comprehensive characterization of schizophrenia exists, there is a general consensus that patients have electrical dysfunction in the prefrontal cortex. The authors designed a novel piezoelectric silk-based implant and optimized electrical output through the addition of conductive materials zinc oxide (ZnO) and aluminum nitride (AlN). With further research and compatibility studies, this implant could rectify electrical misfiring in the infralimbic prefrontal cortex.

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Characterizing Quorum Sensing-Induced Bioluminescence in Variable Volumes With Vibrio fischeri Using Computer Processing Methods

Abdel-Azim et al. | Jun 22, 2020

Characterizing Quorum Sensing-Induced Bioluminescence in Variable Volumes With <em>Vibrio fischeri</em> Using Computer Processing Methods

Understanding how bacteria respond to other bacteria could facilitate their ability to initiate and maintain their infectiousness. The phenomenon by which bacteria signal to each other via chemical signals is called quorum sensing, which could be targeted to deter bacterial infection in some cases if better understood. In this article, the authors study how a bacterium called V. fischeri uses quorum sensing to change bioluminescence, an easy readout that facilitates studying quorum sensing in this strain.

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Machine Learning Algorithm Using Logistic Regression and an Artificial Neural Network (ANN) for Early Stage Detection of Parkinson’s Disease

Kar et al. | Oct 10, 2020

Machine Learning Algorithm Using Logistic Regression and an Artificial Neural Network (ANN) for Early Stage Detection of Parkinson’s Disease

Despite the prevalence of PD, diagnosing PD is expensive, requires specialized testing, and is often inaccurate. Moreover, diagnosis is often made late in the disease course when treatments are less effective. Using existing voice data from patients with PD and healthy controls, the authors created and trained two different algorithms: one using logistic regression and another employing an artificial neural network (ANN).

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Modeling Hartree-Fock approximations of the Schrödinger Equation for multielectron atoms from Helium to Xenon using STO-nG basis sets

Gangal et al. | Oct 05, 2023

Modeling Hartree-Fock approximations of the Schrödinger Equation for multielectron atoms from Helium to Xenon using STO-nG basis sets

The energy of an atom is extremely useful in nuclear physics and reaction mechanism pathway determination but is challenging to compute. This work aimed to synthesize regression models for Pople Gaussian expansions of Slater-type Orbitals (STO-nG) atomic energy vs. atomic number scatter plots to allow for easy approximation of atomic energies without using computational chemistry methods. The data indicated that of the regressions, sinusoidal regressions most aptly modeled the scatter plots.

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Effect of Natural Compounds Curcumin and Nicotinamide on α-synuclein Accumulation in a C. elegans Model of Parkinson’s Disease

Mehrotra et al. | Jan 29, 2018

Effect of Natural Compounds Curcumin and Nicotinamide on α-synuclein Accumulation in a C. elegans Model of Parkinson’s Disease

Parkinson's disease is a neurodegenerative disorder that affects over 10 million people worldwide. It is caused by destruction of dopamine-producing neurons, which results in severe motor and movement symptoms. In this study, the authors investigated the anti-Parkinsonian effects of two natural compounds curcumin and nicotinamide using C. elegans as a model organism.

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A novel approach for early detection of Alzheimer’s disease using deep neural networks with magnetic resonance imaging

Ganesh et al. | Mar 20, 2022

A novel approach for early detection of Alzheimer’s disease using deep neural networks with magnetic resonance imaging

In the battle against Alzheimer's disease, early detection is critical to mitigating symptoms in patients. Here, the authors use a collection of MRI scans, layering with deep learning computer modeling, to investigate early stages of AD which can be hard to catch by human eye. Their model is successful, able to outperform previous models, and detected regions of interest in the brain for further consideration.

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The effects of Helianthus Annuus on Amyotrophic Lateral Sclerosis using Drosophila Melanogaster

Srinivasan et al. | Oct 13, 2022

The effects of <em>Helianthus Annuus</em> on Amyotrophic Lateral Sclerosis using <em>Drosophila Melanogaster</em>

Amyotrophic lateral sclerosis (ALS) affects nearly 200,000 people worldwide and there is currently no cure. The purpose of the study was to determine if Helianthus annuus seeds helped reduce nerve degeneration and increase locomotion using Drosophila melanogaster as the model organism. Through this experiment, we found a general trend suggesting that H. annuus helped increase the mobility of the D. melanogaster suggesting it could be a viable supplement for patients with ALS.

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Temperature and Precipitation Responses to a Stratospheric Aerosol Geoengineering Experiment Using the Community Climate System Model 4

Anderson et al. | Aug 19, 2014

Temperature and Precipitation Responses to a Stratospheric Aerosol Geoengineering Experiment Using the Community Climate System Model 4

We are changing our environment with steadily increasing carbon dioxide emissions, but we might be able to help. The authors here use a computer program called Community Climate System Model 4 to predict the effects of spraying small particles into the atmosphere to reflect away some of the sun's rays. The software predicts that this could reduce the amount of energy the Earth's atmosphere absorbs and may limit but will not completely counteract our carbon dioxide production.

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In silico modeling of emodin’s interactions with serine/threonine kinases and chitosan derivatives

Suresh et al. | Jan 10, 2022

<i>In silico</i> modeling of emodin’s interactions with serine/threonine kinases and chitosan derivatives

Here, through protein-ligand docking, the authors investigated the effect of the interaction of emodin with serine/threonine kinases, a subclass of kinases that is overexpressed in many cancers, which is implicated in phosphorylation cascades. Through molecular dynamics theyfound that emodin forms favorable interactions with chitosan and chitosan PEG (polyethylene glycol) copolymers, which could aid in loading drugs into nanoparticles (NPs) for targeted delivery to cancerous tissue. Both polymers demonstrated reasonable entrapment efficiencies, which encourages experimental exploration of emodin through targeted drug delivery vehicles and their anticancer activity.

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