The Impact of Biomaterial Surface Properties on Engineering Neural Tissue for Spinal Cord Regeneration
Bibliographic record
Abstract
Tissue engineering for spinal cord injury (SCI) remains a complex and challenging task. Biomaterial scaffolds have been suggested as a potential solution for supporting cell survival and differentiation at the injury site. However, different biomaterials display multiple properties that significantly impact neural tissue at a cellular level. Here, we evaluated the behavior of different cell lines seeded on chitosan (CHI), poly (ε-caprolactone) (PCL), and poly (L-lactic acid) (PLLA) scaffolds. We demonstrated that the surface properties of a material play a crucial role in cell morphology and differentiation. While the direct contact of a polymer with the cells did not cause cytotoxicity or inhibit the spread of neural progenitor cells derived from neurospheres (NPCdn), neonatal rat spinal cord cells (SCC) and NPCdn only attached and matured on PCL and PLLA surfaces. Scanning electron microscopy and computational analysis suggested that cells attached to the material's surface emerged into distinct morphological populations. Flow cytometry revealed a higher differentiation of neural progenitor cells derived from human induced pluripotent stem cells (hiPSC-NPC) into glial cells on all biomaterials. Immunofluorescence assays demonstrated that PCL and PLLA guided neuronal differentiation and network development in SCC. Our data emphasize the importance of selecting appropriate biomaterials for tissue engineering in SCI treatment.
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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 | 0.001 |
| 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 | 0.001 | 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".