Control of Neonatal Spinal Networks by Nociceptors: A Potential Role for TRP Channel Based Therapies
Bibliographic record
Abstract
Pediatric spinal cord injury (SCI) often leads to increased nociceptive input resulting in aberrant motor output like tremor and spasticity. Acute plasticity within spinal pain and motor networks following pediatric SCI may result in long-term sensorimotor disabilities. Despite this, pediatric SCI remains poorly understood. Part of the problem lies in the paucity of detailed studies aimed at defining sensorimotor control by nociceptors during development. This review provides an overview of work that highlights afferent control of sensorimotor networks by defined nociceptors in the developing spinal cord. Here, we focus on the well established and widely used neonatal sensorimotor model called sacrocaudal afferent (SCA) pathway. Until recently, the identity of specific subclasses of nociceptive afferents in the SCA pathway controlling developing sensorimotor networks was unknown. We highlight here the use of members of the Transient Receptor Potential (TRP) ion channels and mouse genetics to identify specific subsets of nociceptive afferents in the SCA pathway. In addition, we highlight the use of mouse genetics to map sensorimotor networks during development and potential future applications. A neonatal spinal cord model of central neuropathic pain via a defined set of nociceptors is presented as a probe into potential therapeutic avenues in neonatal SCI. Finally, knowledge translation from neonatal basic research to the pediatric population in the clinic is described. In conclusion, studies in neonatal models may lead to therapeutic strategies and pharmaceuticals for chronic pain and motor dysfunction after SCI during development.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".