Self-Assembling Peptide Nanoscaffold That Activates Human Mast Cells
Bibliographic record
Abstract
Engineering biomaterials to manipulate the immune response to elicit specific therapeutic outcomes is a burgeoning field of research. Mast cells play a distinct and central role in the innate immune response, and are characterized by their rapid release of a myriad of proinflammatory mediators in response to stimulation. These mediators are central to protective actions such as wound healing, angiogenesis, and host defense against pathogens and animal venoms. Considering that mast cells are widely distributed in tissues that interface with the external environment, and are loaded with large amounts of preformed protective compounds, they are ideal targets for novel immunotherapies. Here we report that, by using an engineered nanoscaffold, human mast cells can be contact activated in cell and primary human skin tissue culture using a specific receptor–ligand mechanism. The IgE independent PAMP-12 peptide activates human mast cells through the recently identified Mas-related G-protein coupled receptor member X2 (MRGPRX 2 ) receptor. The PAMP-12 motif was conjugated, via a glycine spacer, with the self-assembling peptide (RADA) 4 and mixed with unmodified (RADA) 4 to form a nanofiber matrix; mast cell activation was influenced directly by this ratio. Moreover, conjugating the PAMP-12 motif within the matrix was shown to only activate local, tissue-resident mast cells. The result of ex vivo human skin tissue tests confirmed that the engineered nanoscaffold successfully activated skin-resident mast cells by contact. Thus, this nanoscaffold design may provide a new platform to modulate localized mast cell functions thereby facilitating their protective role in the skin.
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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) | 0.000 | 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".