AIMC Topic: Animals

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A data-driven approach to establishing cell motility patterns as predictors of macrophage subtypes and their relation to cell morphology.

PloS one
The motility of macrophages in response to microenvironment stimuli is a hallmark of innate immunity, where macrophages play pro-inflammatory or pro-reparatory roles depending on their activation status during wound healing. Cell size and shape have ...

Rapid identification of horse oil adulteration based on deep learning infrared spectroscopy detection method.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
As a natural oil, horse oil has unique biological activity ingredients and therapeutic characteristics, which has important application value and market potential in healthcare, food, skin care and other fields. However, fraud is rampant in the horse...

Machine learning and confirmatory factor analysis show that buprenorphine alters motor and anxiety-like behaviors in male, female, and obese C57BL/6J mice.

Journal of neurophysiology
Buprenorphine is an opioid approved for medication-assisted treatment of opioid use disorder. Used off-label, buprenorphine has been reported to contribute to the clinical management of anxiety. Although human anxiety is a highly prevalent disorder, ...

Trans-m5C: A transformer-based model for predicting 5-methylcytosine (m5C) sites.

Methods (San Diego, Calif.)
5-Methylcytosine (m5C) plays a pivotal role in various RNA metabolic processes, including RNA localization, stability, and translation. Current high-throughput sequencing technologies for m5C site identification are resource-intensive in terms of cos...

Automatic ovarian follicle detection using object detection models.

Scientific reports
Ovaries are of paramount importance in reproduction as they produce female gametes through a complex developmental process known as folliculogenesis. In the prospect of better understanding the mechanisms of folliculogenesis and of developing novel p...

Artificial intelligence-driven rational design of ionizable lipids for mRNA delivery.

Nature communications
Lipid nanoparticles (LNPs) have proven effective in mRNA delivery, as evidenced by COVID-19 vaccines. Its key ingredient, ionizable lipids, is traditionally optimized by inefficient and costly experimental screening. This study leverages artificial i...

Sub-millimeter fiberscopic robot with integrated maneuvering, imaging, and biomedical operation abilities.

Nature communications
Small-scale continuum robots hold promise for interventional diagnosis and treatment, yet existing models struggle to achieve small size, precise steering, and visualized functional treatment simultaneously, termed an "impossible trinity". This study...

Sex identification in rainbow trout using genomic information and machine learning.

Genetics, selection, evolution : GSE
Sex identification in farmed fish is important for the management of fish stocks and breeding programs, but identification based on visual characteristics is typically difficult or impossible in juvenile or premature fish. The amount of genomic data ...

NeuTox 2.0: A hybrid deep learning architecture for screening potential neurotoxicity of chemicals based on multimodal feature fusion.

Environment international
Chemically induced neurotoxicity is a critical aspect of chemical safety assessment. Traditional and costly experimental methods call for the development of high-throughput virtual screening. However, the small datasets of neurotoxicity have limited ...

Application of deep learning models on single-cell RNA sequencing analysis uncovers novel markers of double negative T cells.

Scientific reports
Double negative T (DNT) cells are a unique subset of CD3 + TCRαβ + T lymphocytes that lack CD4, CD8, or NK1.1 expression and constitute 3-5% of the total T cell population in C57BL/6 mice. They have increasingly gained recognition for their novel rol...