Immunoregulatory role of secretory leukocyte protease inhibitor in allergic asthma
Bibliographic record
Abstract
Results Allergic SLPI transgenic mice showed a significant decrease in airway resistance compared to wild-type mice (6.3 ± 1.1 vs. 8.0 ± 2.1 cm H20 × s/ml, p < 0.001), the same effect was observed with inflammatory cell infiltration, eosinophil percentage (24 ± 1.1% vs. 29 ± 2.3%, p < 0.001), goblet cells (6 ± 1.4 vs. 36 ± 4.0%, p < 0.001) in the lungs and IgE levels (2014.1 ± 309.2 vs. 4173.2 ± 685.6 ng/ml, p < 0.001) in plasma. Allergic SLPI knock-out mice displayed significantly higher values compared to wild-type mice. They include lung resistance (8.6 ± 2.7 vs. 6.6 ± 0.5 cm H20*s/ml, p < 0.001), inflammatory cell influx, eosinophils (36.0 ± 2.7 vs. 29.0 ± 1.5%, p < 0.001), goblet cells (40 ± 4.1 vs. 30 ± 1.4%, p < 0.001), cytokine levels in the lungs (p < 0.05) and plasma IgE levels (3598 ± 204.7 vs. 2763 ± 220.3 ng/ml, p < 0.001). Expression of SLPI decreased inflammation in the lungs, plasma IgE levels, and lung resistance, whereas the ablation of SLPI has the opposite effect. Treatment with resiquimod improved airway resistance and inflammation of the lungs in SLPI knockout and wild type, demonstrating that its effect is independent of the expression of SLPI. Conclusions SLPI plays an immunoregulatory role in the respiratory tract by reducing the inflammatory process and by improving lung physiology in a murine model of acute allergic asthma.
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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 | 0.001 |
| 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".