Encoding of inflammatory hyperalgesia in mice spinal cord
Bibliographic record
Abstract
Abstract Inflammation modifies the input-output properties of peripheral nociceptive neurons, thus leading to hyperalgesia, a condition in which the perception of noxious heat stimuli is altered such that the same stimulus produces enhanced pain. The increased nociceptive output enters the superficial dorsal spinal cord (SDH), which comprises the first CNS network integrating the noxious information. Here we used in vivo calcium imaging and a computational approach to investigate how the SDH network in mice encodes the injury-mediated abnormal input from peripheral nociceptive neurons. We show that the application of noxious heat stimuli to the hind paw in naïve mice before induction of injury affects the activity of 70% of recorded neurons by either increasing or suppressing it. Application of the same noxious heat stimuli to hyperalgesic skin following injury leads to activation of previously non-responded cells and de-suppression of the “suppressed” neurons. We further demonstrate that reduction in synaptic inhibition mimics the response to the noxious stimuli in hyperalgesic conditions. Using a computational model of the SDH network, we predict that the “disinhibitory” effect of hyperalgesic stimuli results from the inflammation-mediated increased afferent input to the SDH network and a decrease in SDH inhibition. Both of these processes synergistically contribute to the injury-mediated increase in SDH output towards higher brain centers.
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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) | 0.000 | 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 | 0.001 |
| 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".