Patterned poly(chlorotrifluoroethylene) guides primary nerve cell adhesion and neurite outgrowth
Bibliographic record
Abstract
Central nervous system (CNS) neurons, unlike those of the peripheral nervous system, do not spontaneously regenerate following injury. Recently it has been shown that in the developing CNS, a combination of cell-adhesive and cell-repulsive cues guide growing axons to their targets. We hypothesized that by mimicking these guidance signals, we could guide nerve cell adhesion and neurite outgrowth in vitro. Our objective was to direct primary nerve cell adhesion and neurite outgrowth on poly(chlorotrifluoroethylene) (PCTFE) surfaces by incorporating alternating patterns of cell-adhesive (peptide) and nonadhesive (polyethylene glycol; PEG) regions. PCTFE was surface-modified with lithium PEG-alkoxide, demonstrating the first report of metal-halogen exchange with an alkoxide and PCTFE. Titanium and then gold were sputtered onto PEG-modified films, using a shadow-masking technique that creates alternating patterns on the micrometer scale. PCTFE-Au regions then were modified with one of two cysteine-terminated laminin-derived peptides, C-GYIGSR or C-SIKVAV. Hippocampal neuron cell-surface interactions on homogeneously modified surfaces showed that neuron adhesion was decreased significantly on PEG-modified surfaces and was increased significantly on peptide-modified surfaces. Cell adhesion was greatest on CGYIGSR surfaces while neurite length was greatest on CSIKVAV surfaces and PLL/laminin positive controls, indicating the promise of peptides for enhanced cellular interactions. On patterned surfaces, hippocampal neurons adhered and extended neurites preferentially on peptide regions. By incorporating PEG and peptide molecules on the surface, we were able to simultaneously mimic cell-repulsive and cell-adhesive cues, respectively, and maintain the biopatterning of primary CNS neurons for over 1 week in culture.
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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".