Tracking of Mesenchymal Stem Cells with Fluorescence Endomicroscopy Imaging in Radiotherapy-Induced Lung Injury
Bibliographic record
Abstract
Mesenchymal stem cells (MSCs) have potential for reducing inflammation and promoting organ repair. However, limitations in available techniques to track them and assess this potential for lung repair have hindered their applicability. In this work, we proposed, implemented and evaluated the use of fluorescence endomicroscopy as a novel imaging tool to track MSCs in vivo. MSCs were fluorescently labeled and injected into a rat model of radiation-induced lung injury via endotracheal (ET) or intravascular (IV) administration. Our results show that MSCs were visible in the lungs with fluorescence endomicroscopy. Moreover, we developed an automatic cell counting algorithm to quantify the number of detected cells in each condition. We observed a significantly higher number of detected cells in ET injection compared to IV and a slight increase in the mean number of detected cells in irradiated lungs compared to control, although the latter did not reach statistical significance. Fluorescence endomicroscopy imaging is a powerful new minimally invasive and translatable tool that can be used to track and quantify MSCs in the lungs and help assess their potential in organ repair.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".