Flow cytometric method for enumeration and characterization of newly released polymorphonuclear leukocytes from the bone marrow using 5′-bromo-2′-deoxyuridine
Bibliographic record
Abstract
Inflammation accelerates polymorphonuclear leukocyte (PMN) release from the bone marrow, and these PMNs are implicated in inappropriate tissue injury. We have previously developed a method using 5'-bromo-2'-deoxyuridine (BrdU) to study PMN kinetics using an immunocytochemical grading system of PMN on cytospin slides. The aim of this study was to develop a flow cytometric method to quantify the number of positively stained PMN and grade the intensity of staining for the transit time calculation of PMN through the marrow. Dividing myeloid progenitors in the marrow of rabbits were labeled with a pulse dosage of intravenous BrdU. BrdU-labeled PMN (PMN(BrdU)) were detected in the circulation using a FITC-conjugated anti-BrdU monoclonal antibody. The PMN(BrdU) were assigned to five groups according to their FITC intensity, and the transit times of PMN at different stages of development in the marrow were calculated. Results were compared using parallel immunocytochemical analysis of the same samples. In control animals, PMN(BrdU) in the circulation peaked at 72 h after BrdU labeling with 36.0% of PMN labeled. In normal rabbits, the transit times of PMN through the mitotic pool (49.5 +/- 4.2 h) and maturation pool (65.5 +/- 3.1 h) correlated well with immunocytochemical analysis and previously published values. Using this method, we demonstrated that exposure to air pollution particles accelerates the release of PMN(BrdU) from the marrow. We conclude that a flow cytometric approach for identifying BrdU-labeled leukocytes provides an objective and accurate method for studying leukocyte kinetics and behavior.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".