Co-exposure to zymosan A and heat-inactivated Asian sand dust exacerbates ovalbumin-induced murine lung eosinophilia
Bibliographic record
Abstract
Epidemiological studies have implicated Asian sand dust (ASD) in the increased prevalence of respiratory disorders, including asthma. It has been observed that fungal elements such as β-glucan can be adsorbed onto ASD. In the present study, the exacerbating effect of the combined exposure to zymosan A (ZymA) containing yeast β-glucan and heat-inactivated ASD on ovalbumin (OVA)-induced murine lung eosinophilia was investigated. BALB/c mice were repeatedly instilled intratracheally with one of eight immunogenic formulations consisting of various combinations of (1) ZymA, (2) ASD that was briefly heated to remove organic substances (H-ASD), and (3) OVA in normal saline, or each of the above alone. Pathologic changes, cytological alterations in bronchoalveolar lavage fluid (BALF), changes in inflammatory cytokines and chemokines in BALF, and OVA-specific IgE and IgG 1 antibodies in serum were investigated. Exposure to ZymA with or without OVA had no effect on most indicators of lung inflammation. Exposure to H-ASD with OVA increased the recruitment of inflammatory cells to the lungs and the serum levels of OVA-specific IgE and IgG 1 . The combination OVA + ZymA + H-ASD induced a marked recruitment of eosinophils and upregulation of T helper 2 (Th2) cytokines (interleukin [IL]-4 and IL-13), IL-6, eotaxin/CCL11, and monocyte chemotactic protein (MCP)-3/CCL7 in BALF and OVA-specific IgE in serum. This treatment also induced the most severe pathological changes in the lungs of mice. ZymA was found to boost the effects of H-ASD, thereby exacerbating the OVA-induced allergic inflammation, even though ZymA alone did not have such effect. The results suggest that fungal elements such as β-1,3-glucan aggravate the allergic inflammation caused by ASD. Our findings may facilitate prophylaxis of some allergic diseases in Asia.
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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".