AIMC Topic: Rats

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Comprehensive analysis and validation of TP73 as a biomarker for calcium oxalate nephrolithiasis using machine learning and in vivo and in vitro experiments.

Urolithiasis
Calcium oxalate (CaOx) nephrolithiasis constitutes approximately 75% of nephrolithiasis cases, resulting from the supersaturation and deposition of CaOx crystals in renal tissues. Despite their prevalence, precise biomarkers for CaOx nephrolithiasis ...

Deep learning algorithms reveal increased social activity in rats at the onset of the dark phase of the light/dark cycle.

PloS one
The rapid decrease of light intensity is a potent stimulus of rats' activity. The nature of this activity, including the character of social behavior and the composition of concomitant ultrasonic vocalizations (USVs), is unknown. Using deep learning ...

Estrogen-mediated modulation of sterile inflammatory markers and baroreflex sensitivity in ovariectomized female Wistar rats.

Archives of endocrinology and metabolism
OBJECTIVE: This study aims to explore the role of estrogen in providing cardioprotective benefits to premenopausal women, examining how hormonal differences between sexes influence the prevalence of cardiovascular diseases (CVDs) in women.

Characterization of Brain Abnormalities in Lactational Neurodevelopmental Poly I:C Rat Model of Schizophrenia and Depression Using Machine-Learning and Quantitative MRI.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: A recent neurodevelopmental rat model, utilizing lactational exposure to polyriboinosinic-polyribocytidilic acid (Poly I:C) leads to mimics of behavioral phenotypes resembling schizophrenia-like symptoms in male offspring and depression-l...

Using the super-learner to predict the chemical acute toxicity on rats.

Journal of hazardous materials
With the rapid increase in the number of commercial chemicals, testing methods regarding on median lethal dose (LD) relying animal experiments face challenges such as high costs and ethical concerns. Classical quantitative structure-activity relation...

High-throughput prediction of oral acute toxicity in Rat and Mouse of over 100,000 polychlorinated persistent organic pollutants (PC-POPs) by interpretable data fusion-driven machine learning global models.

Journal of hazardous materials
This study utilized available oral acute toxicity data in Rat and Mouse for polychlorinated persistent organic pollutants (PC-POPs) to construct data fusion-driven machine learning (ML) global models. Based on atom-centered fragments (ACFs), the coll...

Machine Learning-Based Prediction of the Inhibitory Activity of Chemical Substances Against Rat and Human Cytochrome P450s.

Chemical research in toxicology
The prediction of cytochrome P450 inhibition by a computational (quantitative) structure-activity relationship approach using chemical structure information and machine learning would be useful for toxicity research as a simple and rapid tool. Howev...

Chemical composition alterations in rat brain hypothalamus induced by irisin administration using spectroscopic and machine learning techniques.

Analytical biochemistry
This study employed Fourier transform infrared (FTIR) spectroscopy to determine the chemical composition of brain tissues and the changes induced by irisin at doses of 50 mg and 100 mg. Brain tissues were collected from control rats and those adminis...

The Bioprotective Effects of Marigold Tea Polyphenols on Obesity and Oxidative Stress Biomarkers in High-Fat-Sugar Diet-Fed Rats.

Cardiovascular therapeutics
The research is aimed at exploring the potential of marigold petal tea (MPT), rich in polyphenol contents, against oxidative stress and obesity in a rat model following a high-fat-sugar diet (HFSD). The MPT was prepared through the customary method...

Graphene Microelectrode Arrays, 4D Structured Illumination Microscopy, and a Machine Learning Spike Sorting Algorithm Permit the Analysis of Ultrastructural Neuronal Changes During Neuronal Signaling in a Model of Niemann-Pick Disease Type C.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Simultaneously recording network activity and ultrastructural changes of the synapse is essential for advancing understanding of the basis of neuronal functions. However, the rapid millisecond-scale fluctuations in neuronal activity and the subtle su...