ReviewCell transplantation
Imaging-based tracking of stem-cell responses for 3D bioprinting optimization.
Review in Cell transplantation. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 framework for post-print evaluation and iterative optimization in 3D bioprinting, with potential to strengthen quality control, reproducibility, and biological interpretation in regenerative engineering.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.