Nerve Growth Factor‐Binding Engineered Silk Films Promote Neuronal Attachment and Neurite Outgrowth
Bibliographic record
Abstract
Abstract Spider silks have outstanding potential as biomaterials due to their sought‐after mechanical properties and low immunogenicity. The toughest spider silk is aciniform silk, which is used by spiders to wrap prey and produce egg sacs. A variety of recombinant aciniform silk constructs are now been developed, including hybrid silks with domains from multiple spider silk proteins fused together. In this study, an engineered aciniform silk construct, termed NBSilk, fused both N‐ and C‐terminally to heptapeptide motifs that bind the neurotrophic factor and nerve growth factor‐β (NGF) is introduced. NBSilk is shown to be amenable to casting into robust films that remain intact while sequestering and maintaining bioactive NGF at the film surface for at least 7 days. These films support cell survival while enhancing differentiation and neurite density and outgrowth in neuron‐like PC12 cells, with elevation of signaling through both the mitogen‐activated protein kinase (MAPK) and protein kinase B (AKT) signaling pathways. Strikingly, preloading of NBSilk films with NGF enhances neuritogenesis even over conditions where cells are grown with NGF supplementation in the culture medium. NBSilk scaffolds, thus, warrant future development and evaluation as biomaterials for nerve regeneration.
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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".