The Impact of Motor Axon Misdirection and Attrition on Behavioral Deficit Following Experimental Nerve Injuries
Bibliographic record
Abstract
Peripheral nerve transection and neuroma-in-continuity injuries are associated with permanent functional deficits, often despite successful end-organ reinnervation. Axonal misdirection with non-specific reinnervation, frustrated regeneration and axonal attrition are believed to be among the anatomical substrates that underlie the poor functional recovery associated with these devastating injuries. Yet, functional deficits associated with axonal misdirection in experimental neuroma-in-continuity injuries have not yet been studied. We hypothesized that experimental neuroma-in-continuity injuries would result in motor axon misdirection and attrition with proportional persistent functional deficits. The femoral nerve misdirection model was exploited to assess major motor pathway misdirection and axonal attrition over a spectrum of experimental nerve injuries, with neuroma-in-continuity injuries simulated by the combination of compression and traction forces in 42 male rats. Sciatic nerve injuries were employed in an additional 42 rats, to evaluate the contribution of axonal misdirection to locomotor deficits by a ladder rung task up to 12 weeks. Retrograde motor neuron labeling techniques were utilized to determine the degree of axonal misdirection and attrition. Characteristic histological neuroma-in-continuity features were demonstrated in the neuroma-in-continuity groups and poor functional recovery was seen despite successful nerve regeneration and muscle reinnervation. Good positive and negative correlations were observed respectively between axonal misdirection (p<.0001, r(2)=.67), motor neuron counts (attrition) (p<.0001, r(2)=.69) and final functional deficits. We demonstrate prominent motor axon misdirection and attrition in neuroma-in-continuity and transection injuries of mixed motor nerves that contribute to the long-term functional deficits. Although widely accepted in theory, to our knowledge, this is the first experimental evidence to convincingly demonstrate these correlations with data inclusive of the neuroma-in-continuity spectrum. This work emphasizes the need to focus on strategies that promote both robust and accurate nerve regeneration to optimize functional recovery. It also demonstrates that clinically relevant neuroma-in-continuity injuries can now also be subjected to experimental investigation.
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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 | 0.001 | 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) | 0.001 | 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".