AIMC Topic: Models, Animal

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A gastric retentive robotic capsule enables emergency-prepared and responsive oral drug delivery in canine models.

Science advances
Existing oral drug delivery modalities often fall short in medical emergencies due to the absence of readily deployable, internalized drug storage and delivery mechanisms that combine long-term standby with rapid activation. To address this challenge...

From mazes to automation: Modernizing working memory research in animal models.

Behavioural brain research
Working memory (WM) is a core cognitive mechanism necessary for adaptive behavior. In the last few decades, scientists have studied WM using rodent models through traditional and time-consuming approaches, such as the Radial Arm Maze and the T-Maze. ...

A Machine Learning Pipeline for Automated Bolus Segmentation and Area Measurement in Swallowing Videofluoroscopy Images of an Infant Pig Model.

Dysphagia
Feeding efficiency and safety are often driven by bolus volume, which is one of the most common clinical measures of assessing swallow performance. However, manual measurement of bolus area is time-consuming and suffers from high levels of inter-rate...

A proposal for cut marks classification using machine learning: Serrated vs. non-serrated, single vs. double-beveled knives.

Journal of forensic sciences
In tool mark identification, there is still a lack of characteristics and methodologies standardization used to analyze and describe sharp force trauma marks on skeletal remains. This study presents a classification method for cut marks on human bone...

Towards safer imaging: A comparative study of deep learning-based denoising and iterative reconstruction in intraindividual low-dose CT scans using an in-vivo large animal model.

European journal of radiology
PURPOSE: Computed tomography (CT) scans are a significant source of medically induced radiation exposure. Novel deep learning-based denoising (DLD) algorithms have been shown to enable diagnostic image quality at lower radiation doses than iterative ...

How is Big Data reshaping preclinical aging research?

Lab animal
The exponential scientific and technological progress during the past 30 years has favored the comprehensive characterization of aging processes with their multivariate nature, leading to the advent of Big Data in preclinical aging research. Spanning...

A deep-learning assisted bioluminescence tomography method to enable radiation targeting in rat glioblastoma.

Physics in medicine and biology
. A novel solution is required for accurate 3D bioluminescence tomography (BLT) based glioblastoma (GBM) targeting. The provided solution should be computationally efficient to support real-time treatment planning, thus reducing the x-ray imaging dos...

Advancing Computational Toxicology by Interpretable Machine Learning.

Environmental science & technology
Chemical toxicity evaluations for drugs, consumer products, and environmental chemicals have a critical impact on human health. Traditional animal models to evaluate chemical toxicity are expensive, time-consuming, and often fail to detect toxicants ...

High-throughput image analysis with deep learning captures heterogeneity and spatial relationships after kidney injury.

Scientific reports
Recovery from acute kidney injury can vary widely in patients and in animal models. Immunofluorescence staining can provide spatial information about heterogeneous injury responses, but often only a fraction of stained tissue is analyzed. Deep learni...