Injury-Associated PACAP Expression in Rat Sensory and Motor Neurons Is Induced by Endogenous BDNF
Bibliographic record
Abstract
Peripheral nerve injury results in dramatic upregulation in pituitary adenylate cyclase activating polypeptide (PACAP) expression in adult rat dorsal root ganglia and spinal motor neurons mirroring that described for the neurotrophin brain derived neurotrophic factor (BDNF). Thus, we posited that injury-associated alterations in BDNF expression regulate the changes in PACAP expression observed in the injured neurons. The role of endogenous BDNF in induction and/or maintenance of PACAP mRNA expression in injured adult rat motor and sensory neurons was examined by intrathecally infusing or intraperitoneally injecting BDNF-specific antibodies or control IgGs immediately at the time of L4-L6 spinal nerve injury, or in a delayed fashion one week later for 3 days followed by analysis of impact on PACAP expression. PACAP mRNA in injured lumbar sensory and motor neurons was detected using in situ hybridization, allowing quantification of relative changes between experimental groups, with ATF-3 immunofluorescence serving to identify the injured subpopulation of motor neurons. Both the incidence and level of PACAP mRNA expression were dramatically reduced in injured sensory and motor neurons in response to immediate intrathecal anti-BDNF treatment. In contrast, neither intraperitoneal injections nor delayed intrathecal infusions of anti-BDNF had any discernible impact on PACAP expression. This impact on PACAP expression in response to BDNF immunoneutralization in DRG was confirmed using qRT-PCR or by using BDNF selective siRNAs to reduce neuronal BDNF expression. Collectively, our findings support that endogenous injury-associated BDNF expression is critically involved in induction, but not maintenance, of injury-associated PACAP expression in sensory and motor 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 | 0.001 |
| 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".