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Evaluating Biomarkers and Treatments for Acute Kidney Injury in a Zebrafish Model

Mathew et al. | Aug 11, 2019

Evaluating Biomarkers and Treatments for Acute Kidney Injury in a Zebrafish Model

Coronary Artery Disease (CAD) is the leading cause of death in the United States, and 81% of Acute Kidney Injury (AKI) patients in the renal fibrosis stage later develop CAD. In this study, Mathew and Joykutty aimed to create a cost-effective strategy to treat AKI and thus prevent CAD using a model of the zebrafish, Danio rerio. They first tested whether AKI is induced in Danio rerio upon exposure to environmental toxins, then evaluated nitrotyrosine as an early biomarker for toxin-induced AKI. Finally, they evaluated 4 treatments of renal fibrosis, the last stage of AKI, and found that the compound SB431542 was the most effective treatment (reduced fibrosis by 99.97%). Their approach to treating AKI patients, and potentially prevent CAD, is economically feasible for translation into the clinic in both developing and developed countries.

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A Retrospective Study of Research Data on End Stage Renal Disease

Ponnaluri et al. | Mar 09, 2016

A Retrospective Study of Research Data on End Stage Renal Disease

End Stage Renal Disease (ESRD) is a growing health concern in the United States. The authors of this study present a study of ESRD incidence over a 32-year period, providing an in-depth look at the contributions of age, race, gender, and underlying medical factors to this disease.

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Potential Multifunctional Agents for Dual Therapy of Age-Related and Associated Diseases: Alzheimer’s Disease and Type 2 Diabetes Mellitus

Kumar et al. | Nov 13, 2019

Potential Multifunctional Agents for Dual Therapy of Age-Related and Associated Diseases: Alzheimer’s Disease and Type 2 Diabetes Mellitus

Studies show an age-related link between Alzheimer’s Disease and Type 2 Diabetes Mellitus with oxidative stress a characteristic of both. Here, methanolic fractionations and extracts of four Ayurvedic plants were assessed for their protective abilities using a number of in vitro assays. Extracts inhibited oxidative stress and reduced activity of key enzymes involved in the pathogenesis of both diseases in neuroblastoma cells.

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Machine learning on crowd-sourced data to highlight coral disease

Narayan et al. | Jul 26, 2021

Machine learning on crowd-sourced data to highlight coral disease

Triggered largely by the warming and pollution of oceans, corals are experiencing bleaching and a variety of diseases caused by the spread of bacteria, fungi, and viruses. Identification of bleached/diseased corals enables implementation of measures to halt or retard disease. Benthic cover analysis, a standard metric used in large databases to assess live coral cover, as a standalone measure of reef health is insufficient for identification of coral bleaching/disease. Proposed herein is a solution that couples machine learning with crowd-sourced data – images from government archives, citizen science projects, and personal images collected by tourists – to build a model capable of identifying healthy, bleached, and/or diseased coral.

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Molecular Alterations in a High-Fat Mouse Model Before the Onset of Diet–Induced Nonalcoholic Fatty Liver Disease

Lee et al. | Sep 20, 2016

Molecular Alterations in a High-Fat Mouse Model Before the Onset of Diet–Induced Nonalcoholic Fatty Liver Disease

Nonalcoholic fatty liver disease (NAFLD) is one of the most prevalent chronic liver diseases worldwide, but there are few studied warning signs for early detection of the disease. Here, researchers study alterations that occur in a mouse model of NAFLD, which indicate the onset of NAFLD sooner. Earlier detection of diseases can lead to better prevention and treatment.

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Investigating Lymphocytic Involvement in Minimal Change Nephrotic Syndrome

Muncan et al. | Jan 27, 2016

Investigating Lymphocytic Involvement in Minimal Change Nephrotic Syndrome

Minimal Change Disease (MCD) is a degenerative kidney disease. Researchers know very little about the cause of this disorder, however some research has suggested that T lymphocytes may be involved. In this study, the authors measure CD4 and CD8 T cell subpopulations in patients with MCD to investigate whether irregular T lymphocyte populations may be involved in MCD pathogenesis.

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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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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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