TrkA‐expressing trigeminal sensory neurons display both neurochemical and structural plasticity despite a loss of p75<sup>NTR</sup> function: responses to normal and elevated levels of nerve growth factor
Bibliographic record
Abstract
In neural crest-derived sensory ganglia, approximately half of the neuronal population expresses the transmembrane trkA receptor that is required for neuronal binding of target-derived nerve growth factor (NGF). These same neurons also express the p75 neurotrophin receptor (NTR) that increases the affinity of trkA for NGF. Depleting p75NTR expression reduces both the survival of trkA-positive sensory neurons and their afferent innervation of peripheral targets. In this investigation, we assessed the neurochemical and structural plasticity of trigeminal sensory neurons in p75NTR-deficient mice in response to either normal or elevated levels of NGF during postnatal development and into adulthood. Although p75NTR-deficient mice have 30% fewer trigeminal neurons, levels of trkA mRNA expression are modestly elevated in these mutant mice as compared to control mice. The density of central afferent axons and local levels of NGF are, however, comparable between mutant and control animals. Thus, despite the survival of fewer trigeminal neurons, neither ganglionic levels of trkA mRNA expression nor the density of central afferent projections are depleted in p75NTR-deficient mice. In response to elevated levels of NGF protein, transgenic mice with and without p75NTR expression display both increased levels of trkA mRNA expression and a greater density of trigeminal central afferent axons as compared to control mice. These data further reveal that an absence of p75NTR function in trigeminal sensory neurons does not diminish their capacity for NGF-dependent plasticity, namely trkA mRNA expression and collateral growth of central afferent axons.
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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 | 0.000 |
| 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".