AIMC Topic: Mice

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Quantitative analysis of metastatic breast cancer in mice using deep learning on cryo-image data.

Scientific reports
Cryo-imaging sections and images a whole mouse and provides ~ 120-GBytes of microscopic 3D color anatomy and fluorescence images, making fully manual analysis of metastases an onerous task. A convolutional neural network (CNN)-based metastases segmen...

Mapping single-cell data to reference atlases by transfer learning.

Nature biotechnology
Large single-cell atlases are now routinely generated to serve as references for analysis of smaller-scale studies. Yet learning from reference data is complicated by batch effects between datasets, limited availability of computational resources and...

CXCL1: A new diagnostic biomarker for human tuberculosis discovered using Diversity Outbred mice.

PLoS pathogens
More humans have died of tuberculosis (TB) than any other infectious disease and millions still die each year. Experts advocate for blood-based, serum protein biomarkers to help diagnose TB, which afflicts millions of people in high-burden countries....

An Improved Stacked Autoencoder for Metabolomic Data Classification.

Computational intelligence and neuroscience
Naru3 (NR) is a traditional Mongolian medicine with high clinical efficacy and low incidence of side effects. Metabolomics is an approach that can facilitate the development of traditional drugs. However, metabolomic data have a high throughput, spar...

Vesseg: An Open-Source Tool for Deep Learning-Based Atherosclerotic Plaque Quantification in Histopathology Images-Brief Report.

Arteriosclerosis, thrombosis, and vascular biology
Objective: Manual plaque segmentation in microscopy images is a time-consuming process in atherosclerosis research and potentially subject to unacceptable user-to-user variability and observer bias. We address this by releasing Vesseg a tool that inc...

Optimization of C-to-G base editors with sequence context preference predictable by machine learning methods.

Nature communications
Efficient and precise base editors (BEs) for C-to-G transversion are highly desirable. However, the sequence context affecting editing outcome largely remains unclear. Here we report engineered C-to-G BEs of high efficiency and fidelity, with the seq...

Robust Phase Unwrapping via Deep Image Prior for Quantitative Phase Imaging.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Quantitative phase imaging (QPI) is an emerging label-free technique that produces images containing morphological and dynamical information without contrast agents. Unfortunately, the phase is wrapped in most imaging system. Phase unwrapping is the ...

Engineered Extracellular Matrices with Integrated Wireless Microactuators to Study Mechanobiology.

Advanced materials (Deerfield Beach, Fla.)
Mechanobiology explores how forces regulate cell behaviors and what molecular machinery are responsible for the sensing, transduction, and modulation of mechanical cues. To this end, probing of cells cultured on planar substrates has served as a prim...

Detection of Lung Nodules in Micro-CT Imaging Using Deep Learning.

Tomography (Ann Arbor, Mich.)
We are developing imaging methods for a co-clinical trial investigating synergy between immunotherapy and radiotherapy. We perform longitudinal micro-computed tomography (micro-CT) of mice to detect lung metastasis after treatment. This work explores...

A concise review: the synergy between artificial intelligence and biomedical nanomaterials that empowers nanomedicine.

Biomedical materials (Bristol, England)
Nanomedicine has recently experienced unprecedented growth and development. However, the complexity of operations at the nanoscale introduces a layer of difficulty in the clinical translation of nanodrugs and biomedical nanotechnology. This problem i...