AIMC Topic: Rats
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Use of Machine Learning to Re-Assess Patterns of Multivariate Functional Recovery after Fluid Percussion Injury: Operation Brain Trauma Therapy.
Journal of neurotrauma
Jan 13, 2021
Traumatic brain injury (TBI) is a leading cause of death and disability. Yet, despite immense research efforts, treatment options remain elusive. Translational failures in TBI are often attributed to the heterogeneity of the TBI population and limite...
An efficient method for building a database of diatom populations for drowning site inference using a deep learning algorithm.
International journal of legal medicine
Jan 3, 2021
Seasonal or monthly databases of the diatom populations in specific bodies of water are needed to infer the drowning site of a drowned body. However, existing diatom testing methods are laborious, time-consuming, and costly and usually require specif...
Deep Learning in Toxicologic Pathology: A New Approach to Evaluate Rodent Retinal Atrophy.
Toxicologic pathology
Dec 29, 2020
Quantification of retinal atrophy, caused by therapeutics and/or light, by manual measurement of retinal layers is labor intensive and time-consuming. In this study, we explored the role of deep learning (DL) in automating the assessment of retinal a...
Bacterial endosymbiont inhabiting leaves and their antioxidant and antidiabetic potential.
Journal of complementary & integrative medicine
Dec 22, 2020
OBJECTIVES: Research on endosymbionts is emerging globally and is considered as a potential source of bioactive phytochemicals. The present study examines the antioxidant and antidiabetic of the endophytic crude extract isolated from leaves.
Using Artificial Intelligence to Detect, Classify, and Objectively Score Severity of Rodent Cardiomyopathy.
Toxicologic pathology
Dec 8, 2020
Rodent progressive cardiomyopathy (PCM) encompasses a constellation of microscopic findings commonly seen as a spontaneous background change in rat and mouse hearts. Primary histologic features of PCM include varying degrees of cardiomyocyte degenera...
A Workflow for the Performance of the Differential Ovarian Follicle Count Using Deep Neuronal Networks.
Toxicologic pathology
Dec 8, 2020
In order to automate the counting of ovarian follicles required in multigeneration reproductive studies performed in the rat according to Organization for Economic Co-operation and Development guidelines 443 and 416, the application of deep neural ne...
Deep Learning-Based Spermatogenic Staging Assessment for Hematoxylin and Eosin-Stained Sections of Rat Testes.
Toxicologic pathology
Nov 28, 2020
In preclinical toxicology studies, a "stage-aware" histopathological evaluation of testes is recognized as the most sensitive method to detect effects on spermatogenesis. A stage-aware evaluation requires the pathologist to be able to identify the di...
A Machine Learning Enabled Wireless Intracranial Brain Deformation Sensing System.
IEEE transactions on bio-medical engineering
Nov 19, 2020
A leading cause of traumatic brain injury (TBI) is intracranial brain deformation due to mechanical impact. This deformation is viscoelastic and differs from a traditional rigid transformation. In this paper, we describe a machine learning enabled wi...
Comprehensive evaluation of the combined extracts of Epimedii Folium and Ligustri Lucidi Fructus for PMOP in ovariectomized rats based on MLP-ANN methods.
Journal of ethnopharmacology
Nov 8, 2020
ETHNOPHARMACOLOGICAL RELEVANCE: Kidney deficiency is the main pathogenesis of osteoporosis based on the theory of "kidney governing bones" in traditional Chinese medicine (TCM). Osteoporosis is a systemic disease; kidney deficiency influences the gro...
Identification of early liver toxicity gene biomarkers using comparative supervised machine learning.
Scientific reports
Nov 5, 2020
Screening agrochemicals and pharmaceuticals for potential liver toxicity is required for regulatory approval and is an expensive and time-consuming process. The identification and utilization of early exposure gene signatures and robust predictive mo...
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