Haemopoietic mechanisms in murine allergic upper and lower airway inflammation
Bibliographic record
Abstract
Eosinophil recruitment to the airways, including involvement of haemopoietic eosinophil-basophil progenitors (Eo/B-CFU), is primarily regulated by interleukin-5 (IL-5) and eotaxin. In this study, we investigated the haemopoietic mechanisms in upper and lower airway eosinophilic inflammation. Ovalbumin (OVA) sensitized and challenged BALB/c mice were used to establish isolated upper (UAC), isolated lower (LAC), or combined upper and lower airway (ULAC) inflammation. Airway, blood and bone marrow responses were evaluated in each model. Numbers of airway eosinophils and CD4(+) cells were increased significantly in the nasal mucosa in UAC and ULAC mice, and in the lung tissue in LAC and ULAC groups. Levels of IL-5 and eotaxin were increased significantly in the nasal lavage fluid (NL) in UAC and ULAC mice, and in the bronchoalveolar lavage fluid (BAL) in LAC and ULAC groups. The proportion of IL-5-responsive bone marrow Eo/B-CFU was significantly higher than the control in all treatment groups, but peaked much earlier in the ULAC group. Kinetic studies revealed that IL-5 and eotaxin in NL, BAL and serum peaked between 2 and 12 hr after OVA challenge in ULAC mice, and at 24 hr in UAC mice, related to the timing of maximal progenitor responses. These data support the concept that the systemic mechanisms linking rhinitis to asthma depend on the location and extent of airway allergen exposure.
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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".