The effect of smoking on chronic inflammation, immune function and blood cell composition
Bibliographic record
Abstract
Abstract Smoking is the number one risk factor for cancer mortality but only 15–20% of heavy smokers develop lung cancer. It would, therefore, be of great benefit to identify those at high risk early on so that preventative measures can be initiated. To investigate this, we evaluated the effects of smoking on inflammatory markers, innate and adaptive immune responses to bacterial and viral challenges and blood cell composition. We found that plasma samples from 30 heavy smokers (16 men and 14 women) had significantly higher CRP, fibrinogen, IL-6 and CEA levels than 36 non-smoking controls. Whole blood samples from smokers, incubated for 7 h at 37 °C in the absence of any exogenous stimuli, secreted significantly higher levels of IL-8 and a number of other cytokines/chemokines than non-smokers. When challenged for 7 h with E. coli, whole blood samples from smokers secreted significantly lower levels of many inflammatory cytokines/chemokines. However, when stimulated with HSV-1, significantly higher levels of both PGE 2 and many cytokines/chemokines were secreted from smokers’ blood samples than from controls. In terms of blood cell composition, red blood cells, hematocrits, hemoglobin levels, MCV, MCH, MCHC, Pct and RDW levels were all elevated in smokers, in keeping with their compromised lung capacity. As well, total leukocytes were significantly higher, driven by increases in granulocytes and monocytes. In addition, smokers had lower NK cells and higher Tregs than controls, suggesting that smoking may reduce the ability to kill nascent tumor cells. Importantly, there was substantial person-to person variation amongst smokers with some showing markedly different values from controls and others showing normal levels of many parameters measured, indicating the former may be at significantly higher risk of developing lung cancer.
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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".