The response of human bone marrow to chronic cigarette smoking
Bibliographic record
Abstract
Chronic cigarette smoking in humans causes leukocytosis. Animal studies show that chronic smoking shortens the transit time of polymorphonuclear leukocytes (PMNLs) through the bone marrow. The present study examines the response of human bone marrow to chronic cigarette smoking. Three characteristics of peripheral blood PMNLs that indicate active bone marrow release (band cell counts, surface L-selectin expression and myeloperoxidase (MPO) content), were measured in 38 healthy chronic smokers (23+/-5 pack-yrs) and 15 age- and sex-matched nonsmoking controls. The total white cell (6.8+/-0.3x10(9) versus 5.3+/-0.2x10(9) cells x L(-1), p<0.0001) and PMNL (4.2+/-0.18x10(9) versus 3.2+/-0.1x10(9) cells x L(-1), p<0.003) counts were higher in smokers as were the percentage (4.8+/-0.6 versus 1.1+/-0.2, p<0.0001) and total number (0.21+/-0.04x10(9) versus 0.03+/-0.001x10(9) cells x L(-1), p<0.01) of band cells. Flow cytometry showed that the mean fluorescence intensity (MFI) of L-selectin (3.2+/-0.2 versus 2.6+/-0.1, p<0.05) on PMNLs was higher in smokers. There was no difference in the baseline or N-formyl-methionyl-leucyl-phenylalanine-stimulated expression of CD63 or CD18/CD11b (surface markers of PMNL activation) between smokers and controls. The MPO content of PMNLs was higher in smokers (3.4+/-0.3 versus 1.7+/-0.2 MFI, p<0.05). Smokers with a low (<75% of the predicted value) diffusing capacity of the lung for carbon monoxide had higher PMNL MPO levels than smokers with a diffusing capacity of >75% pred (p<0.05). In conclusion, chronic smoking causes phenotypic changes in circulating polymorphonuclear leukocytes that are characteristic of chronic stimulation of the bone marrow and it is speculated that the increased number of immature polymorphonuclear leukocytes contributes to the chronic lung inflammation associated with cigarette smoking.
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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) | 0.002 | 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".