Toward Spinal Cord Injury Repair Strategies: Peptide Surface Modification of Expanded Poly(Tetrafluoroethylene) Fibers for Guided Neurite Outgrowth In Vitro
Bibliographic record
Abstract
Expanded poly(tetrafluoroethylene) fibers were surface modified using an ultraviolet-activated mercury/ammonia reaction to yield amine-functional groups for the coupling of laminin-derived cell adhesive peptides CYIGSR, CDPGYIGSR, CIKVAV, and CQAASIKVAV. Surface elemental composition, determined by X-ray photoelectron spectroscopy, and radiolabeling data indicated that the amount of peptide introduced was approximately equivalent regardless of peptide type, yet mixed peptide surfaces had approximately 60% YIGSR and 40% IKVAV. The peptide-modified surfaces were compared in terms of the response of dorsal root ganglia with neurite length and number of cells attached to each fiber measured. All peptide-functionalized surfaces had a greater cellular response than the aminated ePTFE and ePTFE controls. Surfaces modified with extended peptide sequences CDPGYIGSR and CQAASIKVAV demonstrated a greater cellular response than those modified with the shorter peptide sequences CYIGSR and CIKVAV, respectively, likely because the extended peptides more closely mimic the three-dimensional conformation that the peptides maintain in laminin. Differences in neurite extension were evident among the peptide-functionalized surfaces, with the longest neurites observed on surfaces modified with both CQAASIKVAV and CDPGYIGSR. The "guidance capacity" of the fibers as a function of fiber diameter was investigated in terms of length and directionality of neurite outgrowth. As fiber diameter decreased (from 100+ to 10 microm), the neurites tended to grow to a greater degree down the length of the fiber. The thinnest fibers (with diameters <20 microm) extended shorter neurites than the fibers with a wider diameter. Combining neurite length with guidance indicated that of the fiber diameters investigated, the optimal fiber diameter for neurite guidance was between 30 and 50 microm.
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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".