<b>Differential uptake of serum IgE in skin and serosal mast cells</b> (139.14)
Bibliographic record
Abstract
Abstract Background: Mast cells (MC) are key effector cells in helminth infection and the clinical manifestations of allergic disease. In mice, a subset of MC (Connective Tissue MC) resides at epithelial barriers, such as the skin, or at serosal surfaces, including the peritoneal cavity. These MC exert much of their effector function by virtue of IgE binding to the high affinity Fc epsilon receptor (FcεRI ). Objective: Although acquisition of serum immunoglobulin E (IgE) by skin MC is central to MC effector function, the mechanisms that govern loading of IgE on skin MC are poorly defined. We hypothesize that different anatomic compartments may exhibit differential serum IgE uptake. Methods: We used the mouse ear as a source of skin MC. To control the dose and timing of serum IgE, we used a passive, intravenous IgE transfer model with flow cytometric analysis of MC IgE uptake at time points between 1 and 96 hours after infusion. As controls, we examined serum IgE uptake by spleen basophils and peritoneal mast cells (PEC-MC). Results: We observed differences in IgE uptake by basophils and PEC-MC compared to skin MC. Basophils and PEC-MC displayed uniform loading of IgE that started within 1 hour of IgE exposure. By contrast, skin MC IgE uptake was heterogeneous with only 30% of MC showing significant acquisition of IgE. The timing of uptake was slightly delayed, and the amount of IgE on these cells was also diminished compared to basophils and PEC-MC. Conclusion and Future Directions: Our data suggest that skin and serosal MC demonstrate distinct patterns of IgE uptake and that a select population of skin MC acquires serum IgE upon exposure. Further work will focus on direct in vivo imaging of MC IgE loading within ear tissue to better understand the underlying reason for decreased skin MC uptake of serum IgE.
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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) | 0.005 | 0.002 |
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".