Lung inflammation by fungus, Bjerkandera adusta isolated from Asian sand dust (ASD) aerosol and enhancement of ovalbumin-induced lung eosinophilia by ASD and the fungus in mice
Bibliographic record
Abstract
BACKGROUND: Bjerkandera adusta (B. adusta) is one of the most important etiological fungi associated with chronic cough. However, precise details of the inflammatory response to exposure are not well understood yet. B. adusta was recently identified in Asian sand dust (ASD) aerosol. Therefore, in the present study the exacerbating effects of ASD on B. adusta-induced lung inflammation and B. adusta + ASD on ovalbumin (OVA)-induced murine lung eosinophilia were investigated using experimental mice. METHODS: In order to prepare testing samples, B. adusta obtained from ASD aerosol was inactivated by formalin and ASD collected from the atmosphere was heated to remove toxic organic substances (H-ASD). CD-1 mice were instilled intratracheally with 12 different samples prepared with various combinations of B. adusta, H-ASD, and OVA in a normal saline solution. The lung pathology, cytological profiles in bronchoalveolar lavage fluid (BALF), and the levels of inflammatory cytokines/chemokines in BALF were investigated. RESULTS: H-ASD aggravated the lung eosinophilia induced by B. adusta alone, which also aggravated the lung eosinophilia induced by OVA. The mixture of OVA, H-ASD, and B. adusta caused serious fibrous thickening of the subepithelial layer, eosinophil infiltration, and proliferation of goblet cells in the airways along with remarkable increases of IL-13, eotaxin, IL-5, and MCP-3 in BALF. CONCLUSIONS: The results of the present study demonstrated that B. adusta isolated from ASD aerosol induces allergic lung diseases. H-ASD enhanced allergic reactions caused by OVA or B. adusta. A mixture of B. adusta, H-ASD, and OVA caused the most remarkable exacerbation to the allergic airway inflammation via remarkable increases of pro-inflammatory mediators.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".