The influence of tobacco smoking on adhesion molecule profiles
Bibliographic record
Abstract
Sequential interactions between several adhesion molecules and their ligands regulate lymphocyte circulation and leukocyte recruitment to inflammatory foci. Adhesion molecules are, therefore, central and critical components of the immune and inflammatory system. We review the evidence that tobacco smoking dysregulates specific components of the adhesion cascade, which may be a common factor in several smoking-induced diseases. Smoking causes inappropriate leukocyte activation, leukocyte-endothelial adhesion, and neutrophil entrapment in the microvasculature, which may help initiate local tissue destruction. Appropriate inflammatory reactions may thus be compromised. In addition to smoke-induced alterations to membrane bound endothelial and leukocyte adhesion molecule expression, which may help explain the above phenomena, smoking has a profound influence on circulating adhesion molecule profiles, most notably sICAM-1 and specific sCD44 variants. Elevated concentrations of soluble adhesion molecules may simply reflect ongoing inflammatory processes. However, increasing evidence suggests that specific soluble adhesion molecules are immunomodulatory, and that alterations to soluble adhesion molecule profiles may represent a significant risk factor for several diverse diseases. This evidence is discussed herein.
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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) | 0.001 | 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".