Hemopoietic Progenitor Cells and Hemopoietic Factors: Potential Targets for Treatment of Allergic Inflammatory Diseases
Bibliographic record
Abstract
Eosinophilic infiltration is a cardinal feature of allergic inflammation; based upon its biological actions, the eosinophil has assumed the role as the principal inflammatory cell in asthma. In assessing the mechanisms by which eosinophils are recruited to sites of inflammation, a sizeable body of evidence exists supporting the proposal that expansion of hemopoietic compartments in the bone marrow stimulates an increased turnover and traffic of mature eosinophils to the site of allergic inflammation. In addition, recent findings point to the possible egress and traffic of primitive progenitor cells to the site of inflammation where in-situ differentiation may provide a continued supply of pro-inflammatory cells. In the present article, we will review the evidence for these findings, and discuss the rationale for targeting hemopoiesis and migrational pathways of hemopoietic cells in the treatment of allergic disease. In this context, we will discuss the effect of corticosteroid treatment on hemopoietic mechanisms; the effects of therapies that inhibit the actions of cysteinyl leukotrienes (CysLTs); the effects of in vivo blockade of the eosinophil-active cytokine, interleukin (IL)-5; and, the effects of antihistamines on hemopoiesis. In addition, we will address the potential role that small molecular weight chemokine receptor antagonists may play in modulating progenitor cell trafficking to tissue sites of inflammation.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".