AIMC Topic: Mice

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Enhancing Total Optical Throughput of Microscopy with Deep Learning for Intravital Observation.

Small methods
The significance of performing large-depth dynamic microscopic imaging in vivo for life science research cannot be overstated. However, the optical throughput of the microscope limits the available information per unit of time, i.e., it is difficult ...

FPGA-Based In-Vivo Calcium Image Decoding for Closed-Loop Feedback Applications.

IEEE transactions on biomedical circuits and systems
Miniaturized calcium imaging is an emerging neural recording technique that has been widely used for monitoring neural activity on a large scale at a specific brain region of rats or mice. Most existing calcium-image analysis pipelines operate offlin...

RPI-EDLCN: An Ensemble Deep Learning Framework Based on Capsule Network for ncRNA-Protein Interaction Prediction.

Journal of chemical information and modeling
Noncoding RNAs (ncRNAs) play crucial roles in many cellular life activities by interacting with proteins. Identification of ncRNA-protein interactions (ncRPIs) is key to understanding the function of ncRNAs. Although a number of computational methods...

A fully automated micro‑CT deep learning approach for precision preclinical investigation of lung fibrosis progression and response to therapy.

Respiratory research
Micro-computed tomography (µCT)-based imaging plays a key role in monitoring disease progression and response to candidate drugs in various animal models of human disease, but manual image processing is still highly time-consuming and prone to operat...

Genomic benchmarks: a collection of datasets for genomic sequence classification.

BMC genomic data
BACKGROUND: Recently, deep neural networks have been successfully applied in many biological fields. In 2020, a deep learning model AlphaFold won the protein folding competition with predicted structures within the error tolerance of experimental met...

Semi-Automated Data Curation from Biomedical Literature.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Data curation is a bottleneck for many informatics pipelines. A specific example of this is aggregating data from preclinical studies to identify novel genetic pathways for atherosclerosis in humans. This requires extracting data from published mouse...

The antidiabetic drug pioglitazone ameliorates betel-nut-induced carcinogenesis in mice by restoring normal lipid metabolism, reducing oxidative stress, and inducing apoptosis.

Journal of cancer research and therapeutics
CONTEXT: Oral administration (2 mg mL-1) of aqueous extract of betel nut (AEBN) for 24 weeks induced oncogenic alterations in the liver of female Swiss Albino mice concomitant with aberrant lipid metabolism, overactivation of Akt/mTOR signaling, and ...

Analysis of cardiac single-cell RNA-sequencing data can be improved by the use of artificial-intelligence-based tools.

Scientific reports
Single-cell RNA sequencing (scRNAseq) enables researchers to identify and characterize populations and subpopulations of different cell types in hearts recovering from myocardial infarction (MI) by characterizing the transcriptomes in thousands of in...

XAI-enabled neural network analysis of metabolite spatial distributions.

Analytical and bioanalytical chemistry
We used deep neural networks to process the mass spectrometry imaging (MSI) data of mouse muscle (young vs aged) and human cancer (tumor vs normal adjacent) tissues, with the aim of using explainable artificial intelligence (XAI) methods to rapidly i...

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...