Secretory Leukocyte Protease Inhibitor Plays an Important Role in the Regulation of Allergic Asthma in Mice
Bibliographic record
Abstract
Secretory leukocyte protease inhibitor (SLPI) is an anti-inflammatory protein that is observed at high levels in asthma patients. Resiquimod, a TLR7/8 ligand, is protective against acute and chronic asthma, and it increases SLPI expression of macrophages in vitro. However, the protective role played by SLPI and the interactions between the SLPI and resiquimod pathways in the immune response occurring in allergic asthma have not been fully elucidated. To evaluate the role of SLPI in the development of asthma phenotypes and the effect of resiquimod treatment on SLPI, we assessed airway resistance and inflammatory parameters in the lungs of OVA-induced asthmatic SLPI transgenic and knockout mice and in mice treated with resiquimod. Compared with wild-type mice, allergic SLPI transgenic mice showed a decrease in lung resistance (p < 0.001), airway eosinophilia (p < 0.001), goblet cell hyperplasia (p < 0.001), and plasma IgE levels (p < 0.001). Allergic SLPI knockout mice displayed phenotype changes significantly more severe compared with wild-type mice. These phenotypes included lung resistance (p < 0.001), airway eosinophilia (p < 0.001), goblet cell hyperplasia (p < 0.001), cytokine levels in the lungs (p < 0.05), and plasma IgE levels (p < 0.001). Treatment of asthmatic transgenic mice with resiquimod increased the expression of SLPI and decreased inflammation in the lungs; resiquimod treatment was still effective in asthmatic SLPI knockout mice. Taken together, our study showed that the expression of SLPI protects against allergic asthma phenotypes, and treatment by resiquimod is independent of SLPI expression, displayed through the use of transgenic and knockout SLPI mice.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".