Adaptation to organic dust exposure: a potential role of l-selectin shedding?
Bibliographic record
Abstract
Swine confinement workers, exposed to high levels of organic dust, present a high prevalence of respiratory symptoms but show only mild lung inflammation. This contrasts with the intense inflammatory response observed when naive subjects are exposed to the same environment. Shedding of L-selectin may regulate the recruitment of inflammatory cells and explain this discrepancy. Soluble L-selectin (sL-selectin) levels were measured in sera of 36 workers, 35 control subjects and eight healthy volunteers briefly and repeatedly exposed to swine confinement buildings. White blood cell counts (WBC) and serum interleukin (IL)-6 levels were measured as markers of systemic inflammation. Higher concentrations of sL-selectin were found in the sera of workers than in controls (1452+/-62 ng x mL(-1) and 872+/-25 ng x mL(-1), respectively) whereas no differences were detected before and after acute repeated exposures of exposed volunteers. WBC were increased after exposure in exposed volunteers but not in workers. Both workers and exposed volunteers had increased IL-6 serum levels, although it was more pronounced for the exposed volunteers. These results support the hypothesis that shedding of L-selectin may downregulate the inflammatory response to organic dust-contaminated environments and constitute one mechanism of adaptation to the farm environment.
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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 | 0.001 |
| 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".