The Lung Responds to Zymosan in a Unique Manner Independent of Toll-Like Receptors, Complement, and Dectin-1
Bibliographic record
Abstract
In vitro studies indicate that the inflammatory response to zymosan, a fungal wall preparation, is dependent on Toll-like receptor (TLR) 2, and that this response is enhanced by the dectin-1 receptor. Complement may also play an important role in this inflammatory response. However, the relevance of these molecules within the in vivo pulmonary environment remains unknown. To examine pulmonary in vivo inflammatory responses of the lung to zymosan, zymosan was administered by intratracheal aerosolization to C57BL/6, TLR2- TLR4-, MyD88-, and complement-deficient mice. Outcomes included bronchoalveolar fluid cell counts. We next examined effects of dectin-1 inhibition on response to zymosan in alveolar macrophages in vitro and in lungs of C57BL/6, TLR2-, and complement-deficient mice. Finally, the effect of alveolar macrophage depletion on in vivo pulmonary responses was assessed. Marked zymosan-induced neutrophil responses were unaltered in TLR2-deficient mice despite a TLR2-dependent response seen with synthetic TLR2 agonists. TLR4, MyD88, and complement activation were not required for the inflammatory response to zymosan. Although dectin-1 receptor inhibition blocked the inflammatory response of alveolar macrophages to zymosan in vitro, in vivo pulmonary leukocyte recruitment was not altered even in the absence of TLR2 or complement. Depletion of alveolar macrophages did not affect the response to zymosan. Neither complement, macrophages, nor TLR2, TLR4, MyD88, and/or dectin-1 receptors were involved in the pulmonary in vivo inflammatory response to zymosan.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".