The Basis for Diminished Functional Recovery after Delayed Peripheral Nerve Repair
Bibliographic record
Abstract
The postsurgical period during which neurons remain without target connections (chronic axotomy) and distal nerve stumps and target muscles are denervated (chronic denervation) deleteriously affects functional recovery. An autologous nerve graft and cross-suture paradigm in Sprague Dawley rats was used to systematically and independently control time of motoneuron axotomy, denervation of distal nerve sheaths, and muscle denervation to determine relative contributions of each factor to recovery failure. Tibial (TIB) nerve was cross-sutured to common peroneal (CP) nerve via a contralateral 15 mm nerve autograft to reinnervate the tibialis anterior (TA) muscle immediately or after prolonging TIB axotomy, CP autograft denervation, or TA muscle denervation. Numbers of motoneurons that reinnervated TA muscle declined exponentially from 99 ± 15 to asymptotic mean (± SE) values of 35 ± 1, 41 ± 10, and 13 ± 5, respectively. Enlarged reinnervated motor units fully compensated for reduced motoneuron numbers after prolonged axotomy and autograft denervation, but the maximal threefold enlargement did not compensate for the severe loss of regenerating nerves through chronically denervated nerve stumps and for failure of reinnervated muscle fibers to recover from denervation atrophy. Muscle force, weight, and cross-sectional area declined. Our results demonstrate that chronic denervation of the distal stump plays a key role in reduced nerve regeneration, but the denervated muscle is also a contributing factor. That chronic Schwann cell denervation within the nerve autograft reduced regeneration less than after the denervation of both CP nerve stump and TA muscle, argues that chronic muscle denervation negatively impacts nerve regeneration.
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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".