Protective Effects of PACAP in a Rat Model of Diabetic Neuropathy
Bibliographic record
Abstract
Pituitary adenylate cyclase-activating peptide (PACAP) is a neuropeptide with a widespread occurrence and diverse effects. PACAP has well-documented neuro- and cytoprotective effects, proven in numerous studies. Among others, PACAP is protective in models of diabetes-associated diseases, such as diabetic nephropathy and retinopathy. As the neuropeptide has strong neurotrophic and neuroprotective actions, we aimed at investigating the effects of PACAP in a rat model of streptozotocin-induced diabetic neuropathy, another common complication of diabetes. Rats were treated with PACAP1-38 every second day for 8 weeks starting simultaneously with the streptozotocin injection. Nerve fiber morphology was examined with electron microscopy, chronic neuronal activation in pain processing centers was studied with FosB immunohistochemistry, and functionality was assessed by determining the mechanical nociceptive threshold. PACAP treatment did not alter body weight or blood glucose levels during the 8-week observation period. However, PACAP attenuated the mechanical hyperalgesia, compared to vehicle-treated diabetic animals, and it markedly reduced the morphological signs characteristic for neuropathy: axon-myelin separation, mitochondrial fission, unmyelinated fiber atrophy, and basement membrane thickening of endoneurial vessels. Furthermore, PACAP attenuated the increase in FosB immunoreactivity in the dorsal spinal horn and periaqueductal grey matter. Our results show that PACAP is a promising therapeutic agent in diabetes-associated complications, including diabetic neuropathy.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".