<i>In vivo</i> assessments of mucus dynamics in the rat lung using a Gd‐Cy5.5‐bilabeled contrast agent for magnetic resonance and optical imaging
Bibliographic record
Abstract
Dysfunctions in mucociliary clearance are associated with the accelerated loss of lung function in several respiratory diseases. Approaches enabling the in vivo visualization of mucus dynamics in rodents at high resolution and sensitivity would be beneficial for experimental lung research. We describe the synthesis and characterization of two bilabeled amino dextran-based probes binding specifically to mucin. Labeling of secreted mucus and of mucin in goblet cells in the lungs of lipopolysaccharide-challenged rats has been demonstrated in vivo with near-infrared fluorescence and MRI and confirmed by histology. The effects of uridine triphosphate were then studied in lipopolysaccharide-challenged rats by simultaneously administering the imaging probe and the compound. The data suggest that uridine triphosphate increased the mucociliary clearance, but at the same time induced a release of mucin from goblet cells, thus not contributing to the overall reduction of mucus in the lung. The approach outlined here enables one to derive information on mucus clearance, as well as secretion. Such a global view on mucus dynamics may prove invaluable when testing new pharmacological agents aimed at improving mucociliary clearance.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".