The neurotrophin receptors, trkB and p75, differentially regulate motor axonal regeneration
Bibliographic record
Abstract
Neurotrophic factors that support neuronal survival are implicated in axonal regeneration after injury. Specifically, a strong role for BDNF in motor axonal regeneration has been suggested based on its pattern of expression after injury, as well as the expression of its receptors, trkB and p75. Despite considerable in vitro evidence, which demonstrate specific and distinct physiological responses elicited following trkB and p75 activation, relatively little is known about the function of these receptors in vivo. To investigate the roles of the trkB and p75 receptors in motor axonal regeneration, we have used a tibial (TIB)- common peroneal (CP) cross suture paradigm in p75 homozygous (-/-) knockout mice, trkB heterozygous (+/-) knockout mice, as well as in their wild-type controls. Contralateral intact TIB motoneurons, and axotomized TIB motoneurons that regenerated their axons 10 mm into the CP distal nerve stump were identified by fluorescent retrograde tracers and counted in the T11-L1 spinal segments. Regeneration was evaluated 2, 3, 4, 6, and 8 weeks after nerve repair. Compared to wild-type animals, there are significantly fewer intact TIB motoneurons in p75 (-/-), but not trkB (+/-) mice. The number of motoneurons that regenerated their axons was significantly increased in the p75 (-/-) knockout mice, but significantly attenuated in the trkB (+/-) mice compared to wild-type controls. These results suggest that p75 is important for motoneuronal survival during development, but p75 expression after injury serves to inhibit motor axonal regeneration. In addition, full expression of trkB is critical for complete axonal regeneration to proceed.
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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 | 0.001 | 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 | 0.000 |
| 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".