ArticleScientific reports2024
Machine-learning-guided recognition of α and β cells from label-free infrared micrographs of living human islets of Langerhans.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Label-free optical fingerprints of hyperoxia-induced lung alterations in preterm rabbits.Respiratory research · 2026Article
- Integrating New Approach Methodologies to Address Environmental Pancreatic Toxicity and Metabolic Disorders.Biology · 2025Article
- Immune-evasive beta cells in type 1 diabetes: innovations in genetic engineering, biomaterials, and computational modeling.Frontiers in immunology · 2025Review
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8 authors.
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Abstract
Human islets of Langerhans are composed mostly of glucagon-secreting α cells and insulin-secreting β cells closely intermingled one another. Current methods for identifying α and β cells involve either fixing islets and using immunostaining or disaggregating islets and employing flow cytometry for classifying α and β cells based on their size and autofluorescence. Neither approach, however, allows investigating the dynamic behavior of α and β cells in a living and intact islet. To tackle this issue, we present a machine-learning-based strategy for identification α and β cells in label-free infrared micrographs of living human islets without immunostaining. Intrinsic autofluorescence is stimulated by infrared light and collected both in intensity and lifetime in the visible range, dominated by NAD(P)H and lipofuscin signals. Descriptive parameters are derived from micrographs for ~ 10
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