The nerve conditioning lesion: A strategy to enhance nerve regeneration
Bibliographic record
Abstract
By altering the intrinsic metabolism of the cell, including the upregulation of regeneration-associated genes (RAGs) and the production of structural proteins for axonal outgrowth, the conditioning lesion sets up an environment highly conducive to regeneration. In this review, we assess 40 years of research to provide a comprehensive overview of the conditioning lesion literature, directed at (1) discussing the mechanisms of and barriers to nerve regeneration that can be mitigated by the conditioning lesion, (2) describing the cellular and molecular pathways implicated in the conditioning lesion effect, and (3) deliberating on how these insights might be applied clinically. The consequential impact on regeneration is profound, with a conditioned nerve demonstrating longer neurite extensions in vitro, enhanced expression of RAGs within the dorsal root ganglia, early assembly and transportation of cytoskeletal elements, accelerated axonal growth, and improved functional recovery in vivo. Although this promising technique is not yet feasible to be performed in humans, there are potential strategies, such as conditioning electrical stimulation that may be explored to allow nerve conditioning in a clinically safe and well-tolerated manner. Ann Neurol 2018;83:691-702.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".