Successful retrograde regeneration using a sensory branch for motor nerve transfer
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to test whether regenerating motor axons from a donor nerve can travel in a retrograde fashion using sensory branches to successfully reinnervate a motor nerve end organ. METHODS: This study has two parts. In part I, rats (n = 30) were assigned to one of five groups for obturator nerve (ON)-to-femoral nerve transfer: group 1, ON-to-saphenous nerve (SN) distal stump; group 2, ON-to-SN proximal stump without femoral nerve proper (FNP) injury; group 3, ON-to-SN proximal stump with FNP crush injury; group 4, ON-to-SN proximal stump with FNP transection injury; and group 5, gold standard transfer, ON-to-motor femoral nerve (MFN) branch. At 8 weeks, retrograde labeling was done from the distal MFN, and the spinal cords were examined to assess the degree of obturator motor axon regeneration across the five groups. In part II, only group 4 was examined (n = 8). Through use of immunostaining and optical tissue clearing methods, the nerve transfer networks were cleared and imaged using light-sheet fluorescence microscopy to visualize the regeneration pathways in 2D and 3D models at 2- and 8-week time points. RESULTS: Proximal FNP transection (group 4) enabled a significantly higher number of retrogradely regenerated motor axons compared with control groups 1-3. Moreover, group 4 had modest, but nonsignificant, superiority of motor neuron counts compared with the positive control group, group 5. Optical tissue clearing demonstrated that the axons traveled in a retrograde fashion from the recipient sensory branch to the FNP mixed stump, then through complex turns, down to distal branches. Immunostaining confirmed the tissue clearing findings and suggested perineurium disruption as a means by which axons could traverse across fascicular boundaries. CONCLUSIONS: Sensory branches can transmit regenerating axons from donor nerves back to main mixed recipient nerves, then distally toward target organs. The extent of retrograde regeneration is markedly influenced by the type and severity of injury sustained by the recipient nerve. Using a sensory branch as a bridge for retrogradely regenerating axons can open new potential horizons in nerve repair surgery for severely injured mixed nerves.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".