Non-mulberry silk fibroin functionalization enhances charge-transfer efficiency in aligned polypyrrole-silk composites for electrically stimulated neurite outgrowth
Bibliographic record
Abstract
Abstract Electroconductive biomaterials (ECBs) replicate the natural bioelectrical environment of nerve tissue, promoting action potential propagation after injury and enhancing nerve regeneration through therapeutic electrical stimulation (ES). We present a highly electroactive Faradaic ECB with exceptional electrical conductivity and charge density, alongside low electrochemical impedance. These ECBs trigger action potentials at low stimulation voltages by regulating redox reactions through their intrinsic reversible behavior, thereby preventing electrode degradation and tissue damage. Our biohybrid scaffold consists of aligned microfibrous matrices of polypyrrole (PPy) and Bombyx mori silk fibroin (BmSF), functionalized with Antheraea assamensis silk fibroin (AaSF) rich in the cell-affinitive RGD tripeptide. Serving as an anionic dopant for PPy, AaSF significantly enhances the scaffold’s electrical properties (∼9.18 mS cm -1 ) and charge-transfer efficiency (∼25.27 Ω). The scaffolds exhibit superior charge injection capacity at low potentials compared to conventional bioelectrodes (e.g., 0.46 mC cm -2 at 50 mV). Under pulsed ES at 50 mV cm -1 , these scaffolds support remarkable neurite outgrowth of dorsal root ganglion (DRG) neurons up to 830 μm (7 days). Notably, higher current densities and voltages decrease the rate of neurite outgrowth, highlighting the importance of optimizing ES parameters to effectively evoke functional action potentials without causing any neuronal damage. Biocompatibility assessments reveal that AaSF functionalization improves cellular behavior while minimizing immunomodulatory responses. Enhanced neuronal and glial differentiation is attributed to better cell communication facilitated by excellent adhesion and increased conductivity. In essence, this study provides a strategy for selecting optimal ES parameters for electrically excitable tissues using established electrochemical techniques. The fabricated biohybrid scaffolds hold significant promise as smart nerve guidance channels (NGCs) for future nerve regeneration therapies.
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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".