Imaging-based tracking of stem-cell responses for 3D bioprinting optimization
Karabinskyi B., Alkhimova S., Holembiovska O.
Narrative Review, published in Cell Transplant (2026) — summary generated from the PubMed abstract.
Evidence from laboratory and animal studies provides groundwork for understanding mechanisms and potential before human studies continue.
- Level A · Stronger Clinical Evidence
- Level B · Emerging clinical evidence with positive signals
- Level C · Early human research exploring benefits
- Level D · Scientific groundwork from lab and animal studies
- Emerging · Emerging topic under active research
This page is generated from the PubMed record. The Thai description is an automated summary of bibliographic fields and the abstract, not a full translation, and is not medical advice.
- Study type
- Narrative Review
- Journal
- Cell Transplant (2026)
- Reported sample size
- —
- Source database
- Europe PMC
- PMID
- 42179090
- PMCID
- PMC13201929
- DOI
- 10.1177/09636897261445006
Abstract (original English)
This review examines how mechanical and chemical stimuli shape stem-cell behavior and how live-cell imaging combined with artificial intelligence can support the evaluation and optimization of 3D bioprinting workflows. Current evidence indicates that substrate stiffness, stretch, shear stress, compression, and soluble factors such as transforming growth factor beta (TGF-β), bone morphogenetic protein-2 (BMP-2), vascular endothelial growth factor (VEGF), and basic fibroblast growth factor (FGF-2) influence viability, migration, morphology, and lineage commitment in biopolymeric constructs. Imaging modalities including phase-contrast, fluorescence, confocal, two-photon, and light-sheet microscopy enable dynamic observation of these responses, while computational pipelines for segmentation, tracking, and feature extraction improve scalability and reproducibility of analysis. Across the reviewed studies, image-derived readouts such as cell distribution, motility, adhesion patterns, and early morphological changes emerge as promising indicators of construct quality and maturation potential. At the same time, broader application remains limited by phototoxicity, imaging depth constraints, data-processing demands, incomplete standardization of metrics, and restricted model generalizability. Overall, imaging-based cell tracking integrated with AI-assisted analysis offers a practical fr
What this study does not prove
- • This study does not prove SVF is an approved treatment or a replacement for standard care.
- • This is a narrative review: it collects no new patient data and does not systematically appraise evidence quality.
Evidence level
Evidence from laboratory and animal studies provides groundwork for understanding mechanisms and potential before human studies continue.
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