Identification of multisegmental nociceptive afferents that modulate locomotor circuits in the neonatal mouse spinal cord
Bibliographic record
Abstract
Compared to proprioceptive afferent collateral projections, less is known about the anatomical, neurochemical, and functional basis of nociceptive collateral projections modulating lumbar central pattern generators (CPG). Quick response times are critical to ensure rapid escape from aversive stimuli. Furthermore, sensitization of nociceptive afferent pathways can contribute to a pathological activation of motor circuits. We investigated the extent and role of collaterals of capsaicin-sensitive nociceptive sacrocaudal afferent (nSCA) nerves that directly ascend several spinal segments in Lissauer's tract and the dorsal column and regulate motor activity. Anterograde tracing demonstrated direct multisegmental projections of the sacral dorsal root 4 (S4) afferent collaterals in Lissauer's tract and in the dorsal column. Subsets of the traced S4 afferent collaterals expressed transient receptor potential vanilloid 1 (TRPV1), which transduces a nociceptive response to capsaicin. Electrophysiological data revealed that S4 dorsal root stimulation could evoke regular rhythmic bursting activity, and our data suggested that capsaicin-sensitive collaterals contribute to CPG activation across multiple segments. Capsaicin's effect on S4-evoked locomotor activity was potent until the lumbar 5 (L5) segments, and diminished in rostral segments. Using calcium imaging we found elevated calcium transients within Lissauer's tract and dorsal column at L5 segments when compared to the calcium transients only within the dorsal column at the lumbar 2 (L2) segments, which were desensitized by capsaicin. We conclude that lumbar locomotor networks in the neonatal mouse spinal cord are targets for modulation by direct multisegmental nSCA, subsets of which express TRPV1 in Lissauer's tract and the dorsal column. J. Comp. Neurol. 521:2870-2887, 2013. © 2013 Wiley Periodicals, Inc.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".