AIMC Topic: Mice

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Multicenter integration analysis of TRP channels revealed potential mechanisms of immunosuppressive microenvironment activation and identified a machine learning-derived signature for improving outcomes in gliomas.

CNS neuroscience & therapeutics
AIM: This study aimed to explore the mechanisms of transient receptor potential (TRP) channels on the immune microenvironment and develop a TRP-related signature for predicting prognosis, immunotherapy response, and drug sensitivity in gliomas.

Machine learning in time-lapse imaging to differentiate embryos from young vs old mice†.

Biology of reproduction
Time-lapse microscopy for embryos is a non-invasive technology used to characterize early embryo development. This study employs time-lapse microscopy and machine learning to elucidate changes in embryonic growth kinetics with maternal aging. We anal...

The cytoarchitectonic landscape revealed by deep learning method facilitated precise positioning in mouse neocortex.

Cerebral cortex (New York, N.Y. : 1991)
Neocortex is a complex structure with different cortical sublayers and regions. However, the precise positioning of cortical regions can be challenging due to the absence of distinct landmarks without special preparation. To address this challenge, w...

[An artificial neural network diagnostic model for scleroderma and immune cell infiltration analysis based on mitochondria-associated genes].

Nan fang yi ke da xue xue bao = Journal of Southern Medical University
OBJECTIVE: To establish a diagnostic model for scleroderma by combining machine learning and artificial neural network based on mitochondria-related genes.

Innovative statistical approaches: the use of neural networks reduces the sample size in the splenectomy-MCAO mouse model.

Croatian medical journal
AIM: To compare the effectiveness of artificial neural network (ANN) and traditional statistical analysis on identical data sets within the splenectomy-middle carotid artery occlusion (MCAO) mouse model.

Deep-learning based flat-fielding quantitative phase contrast microscopy.

Optics express
Quantitative phase contrast microscopy (QPCM) can realize high-quality imaging of sub-organelles inside live cells without fluorescence labeling, yet it requires at least three phase-shifted intensity images. Herein, we combine a novel convolutional ...

DeepLocRNA: an interpretable deep learning model for predicting RNA subcellular localization with domain-specific transfer-learning.

Bioinformatics (Oxford, England)
MOTIVATION: Accurate prediction of RNA subcellular localization plays an important role in understanding cellular processes and functions. Although post-transcriptional processes are governed by trans-acting RNA binding proteins (RBPs) through intera...

MyoV: a deep learning-based tool for the automated quantification of muscle fibers.

Briefings in bioinformatics
Accurate approaches for quantifying muscle fibers are essential in biomedical research and meat production. In this study, we address the limitations of existing approaches for hematoxylin and eosin-stained muscle fibers by manually and semiautomatic...

A Computational Framework for Memory Engrams.

Advances in neurobiology
Memory engrams in mice brains are potentially related to groups of concept cells in human brains. A single concept cell in human hippocampus responds, for example, not only to different images of the same object or person but also to its name written...

Spatial mapping of human hematopoiesis at single-cell resolution reveals aging-associated topographic remodeling.

Blood
The spatial anatomy of hematopoiesis in the bone marrow (BM) has been extensively studied in mice and other preclinical models, but technical challenges have precluded a commensurate exploration in humans. Institutional pathology archives contain tho...