Kinetic analysis of pulmonary neutrophil retention in vivo using the multiple-indicator-dilution technique
Bibliographic record
Abstract
Multiple-indicator-dilution experiments were performed in the lungs of 13 anesthetized dogs by simultaneous bolus injection of 111In-labeled neutrophils, 51Cr-labeled red blood cells, and Evans blue-labeled albumin. Concomitant counts of unlabeled neutrophils were similar in pulmonary artery and aortic blood samples, demonstrating a dynamic balance across the lungs in the physiological state. Outflow profiles of labeled neutrophils were analyzed on the basis of a recirculatory pharmacokinetic model of labeled albumin. The outflow profiles of the recovered neutrophils were composed of a throughput component of circulating neutrophils and a component of reversibly marginated neutrophils. They were interpreted by a model incorporating neutrophil margination (transfer coefficient = 0.195 +/- 0.081 s-1), rapid demargination (0.054 +/- 0.027 s-1), and transfer to a slow marginated pool (0.023 +/- 0.018 s-1). It will be interesting to apply the analysis in future studies aimed at determining whether it could be a useful research tool to investigate the interactions between the pulmonary endothelium and neutrophils in physiological and diseased states.
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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 | 0.001 |
| 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) | 0.000 | 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".