Human Upper Airway Epithelial Cell-Derived Granulocyte-Macrophage Colony-Stimulating Factor Induces Histamine-Containing Cell Differentiation of Human Progenitor Cells
Bibliographic record
Abstract
Nasal polyps and allergic rhinitis are upper airway inflammatory conditions characterized by increased numbers of eosinophils and metachromatic cells in the epithelial layer of the nasal mucosa. The objective of the current studies was to investigate the potential contribution of epithelial cells to the accumulation of inflammatory cells in the tissue. We have established pure cultures of human upper airway epithelial cells from normal and inflamed nasal polyps and allergic rhinitis tissue and examined the ability of conditioned medium from these cells (EpCM) to induce differentiation of human hemopoietic progenitors in vitro. We show that, under appropriate culture conditions, EpCMs, particularly those from cells derived from inflamed tissues, induce histamine-containing cell differentiation of cells of the human HL-60 myeloid leukemia cell line. These EpCMs also induce the emergence of both eosinophil/basophil and granulocyte/macrophage colonies in methylcellulose cultures of human peripheral blood mononuclear cells. We also show that CMs from epithelial cells derived from inflamed tissues contain greater amounts of granulocyte-macrophage colony-stimulating factor (GM-CSF) compared to CMs from normal epithelial cells. Finally, we show that the histamine-containing cell differentiation of HL-60 cells as well as the colony growth induced by EpCM can be fully inhibited by preincubating this CM with a monoclonal neutralizing antibody to human GM-CSF. These studies: (a) illustrate the ability of human upper airway epithelial cells to secrete GM-CSF in vitro; (b) demonstrate differences between normal and inflamed tissue-derived epithelial cells.(ABSTRACT TRUNCATED AT 250 WORDS)
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".