AIMC Topic: Mice
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From data to discovery: AI-guided analysis of disease-relevant molecules in spinal muscular atrophy (SMA).
Human molecular genetics
Jul 22, 2024
Spinal Muscular Atrophy is caused by partial loss of survival of motoneuron (SMN) protein expression. The numerous interaction partners and mechanisms influenced by SMN loss result in a complex disease. Current treatments restore SMN protein levels t...
A Pan-Cancer Patient-Derived Xenograft Histology Image Repository with Genomic and Pathologic Annotations Enables Deep Learning Analysis.
Cancer research
Jul 2, 2024
Patient-derived xenografts (PDX) model human intra- and intertumoral heterogeneity in the context of the intact tissue of immunocompromised mice. Histologic imaging via hematoxylin and eosin (H&E) staining is routinely performed on PDX samples, which...
A CNN-GNN Approach for Polarity Vectors Prediction in 3D Microscopy Images.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2024
The polarity between nuclei and Golgi is an important aspect of cellular division, migration and signaling. For example, nucleus-Golgi polarity significantly impacts angiogenesis, the physiological process in which new blood vessels develop from pre-...
Metabolomic profiling and accurate diagnosis of basal cell carcinoma by MALDI imaging and machine learning.
Experimental dermatology
Jul 1, 2024
Basal cell carcinoma (BCC), the most common keratinocyte cancer, presents a substantial public health challenge due to its high prevalence. Traditional diagnostic methods, which rely on visual examination and histopathological analysis, do not includ...
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
Jul 1, 2024
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
Jun 12, 2024
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)
Jun 4, 2024
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
May 20, 2024
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
Apr 30, 2024
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
Mar 25, 2024
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 ...
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