The Spontaneous Adhesion of BMMC onto Self‐Assembled Peptide Nanoscaffold without Activation Inhibits Its IgE‐Mediated Degranulation
Bibliographic record
Abstract
Mast cells play a distinct role in the innate immune response. Engineered microenvironments for the express purpose of influencing mast cell activity will provide a novel means of designing biomaterials, as well as a means to systematically investigate mast cell biology in a 3D setting. Here, the effect of nanoscaffolds composed of self‐assembling peptides, namely (RADA)4, on bone‐marrow‐derived murine mast cell (BMMC) activity is reported. Unlike most studies that stimulate mast cells to induce adhesion, this results show that BMMCs spontaneously adhere to the artificial nanoscaffold without initiating their activation. It is observed that the classical immunoglobulin (IgE) antigen‐mediated degranulation of adhered BMMC is inhibited by the nanoscaffold, while non‐IgE (A23187)‐induced degranulation is unaffected. The inhibition of IgE–antigen‐mediated degranulation is likely a result of inhibited molecular diffusion within the matrix; antigen diffusion, IgE–FcεRI complex shuttling, and/or formation of multiple IgE–FcεRI clusters may be physically hindered in the presence of the polyvalent nanofiber network. Moreover, the IgE/antigen‐induced inflammatory cytokine tumor necrosis factor α release from adherent BMMCs is significantly reduced likely due to interaction with the nanofiber matrix. This work is considered the first step in quantifying mast cell activity in artificial matrices composed of self‐assembling peptides.
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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 | 0.000 |
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".