The influence of nicotine on granulocytic differentiation – Inhibition of the oxidative burst and bacterial killing and increased matrix metalloproteinase-9 release
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Neutrophils leave the bone marrow as terminally differentiated cells, yet little is known of the influence of nicotine or other tobacco smoke components on neutrophil differentiation. Therefore, promyelocytic HL-60 cells were differentiated into neutrophils using dimethylsulfoxide in the presence and absence of nicotine (3-(1-methyl-2-pyrrolidinyl) pyridine). Differentiation was evaluated over 5 days by monitoring terminal differentiation markers (CD11b expression and formazan deposition); cell viability, growth phase, kinetics, and apoptosis; assessing cellular morphology and ultrastructure; and conformational changes to major cellular components. Key neutrophil effector functions (oxidative burst, bacterial killing, matrix metalloproteinase release) were also examined. RESULTS: Nicotine increased the percentage of cells in late differentiation phases (metamyelocytes, banded neutrophils and segmented neutrophils) compared to DMSO alone (p < 0.05), but did not affect any other marker of neutrophil differentiation examined. However, nicotine exposure during differentiation suppressed the oxidative burst in HL-60 cells (p < 0.001); inhibited bacterial killing (p < 0.01); and increased the LPS-induced release of MMP-9, but not MMP-2 (p < 0.05). These phenomena may be alpha-7-acetylcholine nicotinic receptor-dependent. Furthermore, smokers exhibited an increased MMP-9 burden compared to non-smokers in vivo (p < 0.05). CONCLUSION: These findings may partially explain the known increase in susceptibility to bacterial infection and neutrophil-associated destructive inflammatory diseases in individuals chronically exposed to nicotine.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it