An <i>in vitro</i> study of peptide-loaded alginate nanospheres for antagonizing the inhibitory effect of Nogo-A protein on axonal growth
Bibliographic record
Abstract
The adult mammalian central nervous system has limited ability to regenerate after injury. This is due, in part, to the presence of myelin-associated axon growth inhibitory proteins such as Nogo-A that bind and activate the Nogo receptor, leading to profound inhibition of actin-based motility within the growing axon tip. This paper presents an in vitro study of the use of a Nogo receptor-blocking peptide to antagonize the inhibitory effect of Nogo-A on axon growth. Alginate nanospheres were fabricated using an emulsion technique and loaded with Nogo receptor-blocking peptide, or with other model proteins. Protein release profiles were studied, and retention of the bioactivity of released proteins was verified. Primary dorsal root ganglion neurons were cultured and their ability to grow neurites was challenged with Nogo-A chimeric protein in the absence or presence of Nogo receptor antagonist peptide-loaded alginate nanospheres. Our results demonstrate that peptide released from alginate nanospheres could overcome the growth inhibitory effect of Nogo-A, suggesting that a similar peptide delivery strategy using alginate nanospheres might be used to improve axon regeneration within the injured central nervous system.
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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".