Intra-vital imaging of mesenchymal stromal cell kinetics in the pulmonary vasculature during infection
Bibliographic record
Abstract
Mesenchymal stem/stromal cells (MSCs) have demonstrated efficacy in pre-clinical models of inflammation and tissue injury, including in models of lung injury and infection. Rolling, adhesion and transmigration of MSCs appears to play a role during MSC kinetics in the systemic vasculature. However, a large proportion of MSCs become entrapped within the lungs after intravenous administration, while the initial kinetics and the site of arrest of MSCs in the pulmonary vasculature are unknown. We examined the kinetics of intravascularly administered MSCs in the pulmonary vasculature using a microfluidic system in vitro and intra-vital microscopy of intact mouse lung. In vitro, MSCs bound to endothelium under static conditions but not under laminar flow. VCAM-1 antibodies did not affect MSC binding. Intravital microscopy demonstrated MSC arrest at pulmonary micro-vessel bifurcations due to size obstruction. Retention of MSCs in the pulmonary microvasculature was increased in Escherichia coli-infected animals. Trapped MSCs deformed over time and appeared to release microvesicles. Labelled MSCs retained therapeutic efficacy against pneumonia. Our results suggest that MSCs are physically obstructed in pulmonary vasculature and do not display properties of rolling/adhesion, while retention of MSCs in the infected lung may require receptor interaction.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".