AIMC Topic: Rats
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Multiscale biochemical mapping of the brain through deep-learning-enhanced high-throughput mass spectrometry.
Nature methods
Feb 16, 2024
Spatial omics technologies can reveal the molecular intricacy of the brain. While mass spectrometry imaging (MSI) provides spatial localization of compounds, comprehensive biochemical profiling at a brain-wide scale in three dimensions by MSI with si...
Effects of various cross-linked collagen scaffolds on wound healing in rats model by deep-learning CNN.
Computer methods in biomechanics and biomedical engineering
Feb 15, 2024
Scar tissue is connective tissue formed on the wound during the wound-healing process. The most significant distinction between scar tissue and normal tissue is the appearance of covalent cross-linking and the amount of collagen fibers in the tissue....
Early inner plexiform layer thinning and retinal nerve fiber layer thickening in excitotoxic retinal injury using deep learning-assisted optical coherence tomography.
Acta neuropathologica communications
Feb 1, 2024
Excitotoxicity from the impairment of glutamate uptake constitutes an important mechanism in neurodegenerative diseases such as Alzheimer's, multiple sclerosis, and Parkinson's disease. Within the eye, excitotoxicity is thought to play a critical rol...
A deep neural network: mechanistic hybrid model to predict pharmacokinetics in rat.
Journal of computer-aided molecular design
Jan 31, 2024
An important aspect in the development of small molecules as drugs or agrochemicals is their systemic availability after intravenous and oral administration. The prediction of the systemic availability from the chemical structure of a potential candi...
Resource-Efficient Neural Network Architectures for Classifying Nerve Cuff Recordings on Implantable Devices.
IEEE transactions on bio-medical engineering
Jan 19, 2024
BACKGROUND: Closed-loop functional electrical stimulation can use recorded nerve signals to create implantable systems that make decisions regarding nerve stimulation in real-time. Previous work demonstrated convolutional neural network (CNN) discrim...
Identification and validation of platelet-related diagnostic markers and potential drug screening in ischemic stroke by integrating comprehensive bioinformatics analysis and machine learning.
Frontiers in immunology
Jan 10, 2024
BACKGROUND: Ischemic stroke (IS), caused by blood and oxygen deprivation due to cerebral thrombosis, has links to activated and aggregated platelets. Discovering platelet-related biomarkers, developing diagnostic models, and screening antiplatelet dr...
Inferring neural activity before plasticity as a foundation for learning beyond backpropagation.
Nature neuroscience
Jan 3, 2024
For both humans and machines, the essence of learning is to pinpoint which components in its information processing pipeline are responsible for an error in its output, a challenge that is known as 'credit assignment'. It has long been assumed that c...
Mechanism of TNF- α inducing apoptosis and autophagy of chondrocytes by activating NF- κ B signal pathway.
Cellular and molecular biology (Noisy-le-Grand, France)
Dec 31, 2023
This study aimed to explore the mechanism of apoptosis and autophagy of chondrocytes induced by tumor necrosis factor α (TNA-α) by activating the NF-κB signal pathway. For this purpose, 24 SD rats were selected for feeding. The knee cartilage was cut...
Influences of microRNA-451 on the expression of HMGB1 in myocardial cells and its mechanism in ischemia-reperfusion injury.
Cellular and molecular biology (Noisy-le-Grand, France)
Dec 31, 2023
This work aimed to understand the underlying mechanism of micro-ribonucleic acid (MicroRNA) (miR)-451 in ischemia-reperfusion injury (IRI) and the influences of miR-451 on high mobility group box 1 protein (HMGB1) in myocardial cells, 30 specific pat...
Automatic liver segmentation and assessment of liver fibrosis using deep learning with MR T1-weighted images in rats.
Magnetic resonance imaging
Dec 24, 2023
OBJECTIVES: To validate the performance of nnU-Net in segmentation and CNN in classification for liver fibrosis using T1-weighted images.
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