Utilization of a rodent model to examine the neurological effects of early life adversity on adolescent pain sensitivity
Bibliographic record
Abstract
All children experience pain, and although many recover quickly, some go on to develop chronic pain. Adolescent chronic pain is a growing epidemic. It is unknown why some adolescents recover without incident and others experience persistent pain. Although unexplored, early life adversity may contribute to the development and maintenance of chronic pain. This study investigated the effects and underlying neurobiological mechanisms of an early life stressor on nociceptive (pain) sensitivity and emotional function in male and female Sprague-Dawley rats. Using maternal separation (MS) as an established model of early life stress, we addressed two aims: investigation of the effects of MS on behavior (anxiety and pain sensitivity), and investigation of the effects of MS on mRNA and pathophysiological changes associated with an acutely painful stimulus. Our results indicate that MS increased anxiety-like behavior and altered nociceptive responsivity in adolescent rats, with decreased mechanical withdrawal thresholds indicative of heightened and prolonged pain-related behavior. The MS groups also demonstrated increased expression of genes involved in regulating the stress and fight-or-flight response, mood, and neuroplasticity; as well as increased levels of inflammatory markers. We conclude that nociception, both at the behavioral and molecular level, is altered in response to the MS stressor.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".