Fucoidan and microtopography on polyvinyl alcohol hydrogels guided axons and enhanced neuritogenesis of pheochromocytoma 12 (PC12) cells
Bibliographic record
Abstract
Artificial nerve grafts that support axon growth hold promises in promoting nerve regeneration and function recovery. However, current artificial nerve grafts are insufficient to regenerate axons across long nerve gaps. Specific biochemical and biophysical cues are required to be incorporated to artificial nerve grafts to promote neural cell adhesion and guide neurite outgrowth. Polyvinyl alcohol (PVA) nerve conduits have been clinically approved, but the applicability of PVA nerve conduits is limited to short injuries due to low cell binding. In this study, we explored the incorporation of biochemical cues and topographical cues for promoting neuritogenesis and axon guidance. PVA was conjugated with extracellular matrix proteins and fucoidan, a bioactive sulfated polysaccharide, to improve cell adhesion. Micro-sized topographies, including 1.8 μm convex lenses, 2 μm gratings, and 10 μm gratings were successfully fabricated on PVA by nanofabrication, and the synergistic effects of topography and biochemical molecules on pheochromocytoma 12 (PC12) neuritogenesis and neurite alignment were studied. Conjugated fucoidan promoted the percentage of PC12 with neurite outgrowth from 0% to 2.8% and further increased to 5% by presenting laminin on the surface. Additionally, fucoidan was able to bind nerve growth factor (NGF) on the surface and allow for PC12 to extend neurites in NGF-free media. The incorporation of 2 μm gratings could double the percentage of PC12 with neurite outgrowth and neurite length, and guided the neurites to extend along the grating axis. The work presents a promising strategy to enhance neurite formation and axon guidance, presenting significant value in promoting 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) | 0.000 | 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".