<i>In Vivo</i> Tracking of Murine Adipose Tissue-Derived Multipotent Adult Stem Cells and <i>Ex Vivo</i> Cross-Validation
Bibliographic record
Abstract
Stem cells are characterized by the ability to renew themselves and to differentiate into specialized cell types, while stem cell therapy is believed to treat a number of different human diseases through either cell regeneration or paracrine effects. Herein, an in vivo and ex vivo near infrared time domain (NIR TD) optical imaging study was undertaken to evaluate the migratory ability of murine adipose tissue-derived multipotent adult stem cells [mAT-MASC] after intramuscular injection in mice. In vivo NIR TD optical imaging data analysis showed a migration of DiD-labelled mAT-MASC in the leg opposite the injection site, which was confirmed by a fibered confocal microendoscopy system. Ex vivo NIR TD optical imaging results showed a systemic distribution of labelled cells. Considering a potential microenvironmental contamination, a cross-validation study by multimodality approaches was followed: mAT-MASC were isolated from male mice expressing constitutively eGFP, which was detectable using techniques of immunofluorescence and qPCR. Y-chromosome positive cells, injected into wild-type female recipients, were detected by FISH. Cross-validation confirmed the data obtained by in vivo/ex vivo TD optical imaging analysis. In summary, our data demonstrates the usefulness of NIR TD optical imaging in tracking delivered cells, giving insights into the migratory properties of the injected cells.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".