Immune Response to Silk Sericin–Fibroin Composites: Potential Immunogenic Elements and Alternatives for Immunomodulation
Bibliographic record
Abstract
The unique properties of silk proteins (SPs), particularly silk sericin (SS) and silk fibroin (SF), have attracted attention in the design of scaffolds for tissue engineering over the past decades. Since SF has good mechanical properties, while SS displays bioactivity, scaffolds combining both proteins should exhibit complementary properties enhancing the potential of these materials. Unfortunately, SS-SF composites can generate chronic immune responses and their immunogenic element is not completely clear. The potential of SS-SF composites in tissue engineering, elements which may contribute to their immunogenicity, and alternatives for their preparation and design, to modulate the immune response and take advantage of their useful properties, are discussed in this review. It is known that SS can enhance β-sheet formation in SF, which may act as hydrophobic regions with a strong affinity for adsorption proteins inducing the chronic recruitment of inflammatory cells. Therefore, tailoring the exposure of hydrophobic regions at the scaffold surface should represent a viable strategy to modulate the immune response. This can be achieved by coating SS-SF composites with SS or other hydrophilic polymers, to take advantage of their antibiofouling properties. Research is still needed to realize the full potential of these composites for tissue engineering.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".