Dynamic patterns of BDNF expression in injured sensory neurons: differential modulation by NGF and NT‐3
Bibliographic record
Abstract
It has been suggested that altered retrograde neurotrophin support contributes to the phenotypic switch observed in BDNF expression in injured sensory neurons. Thus, modulatory influences of NGF and NT-3 on BDNF expression in injured adult rat DRG neurons were examined using in situ hybridization and immunohistochemical approaches. Quantitative analysis reveals a biphasic response to sciatic nerve injury, whereby in the first day following injury, BDNF expression is up-regulated in approximately 83% of injured neurons including all small neurons, and a larger pool of trkB expressing neurons than in intact. By 1 week and up to 3 weeks later expression is still seen in approximately 66% of injured neurons, but the characteristic phenotypic switch in the subpopulations expressing BDNF occurs, whereby expression in the trkA population is reduced and expression in trkB- and in trkC-positive neurons is elevated. NGF infusion results in elevated levels of BDNF expression in both intact and injured trkA-positive neurons, accompanied by reduced trkB expression. NT-3 acts in an opposite fashion effecting a down-regulation in BDNF expression in intact neurons and preventing/reducing the injury-associated increases in BDNF expression in both trkC- and nontrkC-expressing subpopulations of injured neurons. These effects suggest NGF can regulate BDNF expression in trkA-expressing neurons regardless of the axonal state and that elevated levels of BDNF may contribute to the down-regulation in trkB expression associated with these states. Furthermore, the findings demonstrate that NT-3 can act in an antagonistic fashion to NGF in the regulation of BDNF expression in intact neurons, and mitigate BDNF's expression in injured neurons.
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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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".