A feed-forward spinal cord glycinergic neural circuit gates mechanical allodynia
Bibliographic record
Abstract
Neuropathic pain is characterized by mechanical allodynia induced by low-threshold myelinated Aβ-fiber activation. The original gate theory of pain proposes that inhibitory interneurons in the lamina II of the spinal dorsal horn (DH) act as "gate control" units for preventing the interaction between innocuous and nociceptive signals. However, our understanding of the neuronal circuits underlying pain signaling and modulation in the spinal DH is incomplete. Using a rat model, we have shown that the convergence of glycinergic inhibitory and excitatory Aβ-fiber inputs onto PKCγ+ neurons in the superficial DH forms a feed-forward inhibitory circuit that prevents Aβ input from activating the nociceptive pathway. This feed-forward inhibition was suppressed following peripheral nerve injury or glycine blockage, leading to inappropriate induction of action potential outputs in the nociceptive pathway by Aβ-fiber stimulation. Furthermore, spinal blockage of glycinergic synaptic transmission in vivo induced marked mechanical allodynia. Our findings identify a glycinergic feed-forward inhibitory circuit that functions as a gate control to separate the innocuous mechanoreceptive pathway and the nociceptive pathway in the spinal DH. Disruption of this glycinergic inhibitory circuit after peripheral nerve injury has the potential to elicit mechanical allodynia, a cardinal symptom of neuropathic pain.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".