Altered C-tactile processing in human dynamic tactile allodynia
Bibliographic record
Abstract
Human unmyelinated (C) tactile afferents signal the pleasantness of gentle skin stroking on hairy (nonglabrous) skin. After neuronal injury, that same type of touch can elicit unpleasant sensations: tactile allodynia. The prevailing pathophysiological explanation is a spinal cord sensitization, triggered by nerve injury, which enables Aβ afferents to access pain pathways. However, a recent mouse knockout study demonstrates that C-tactile afferents are necessary for allodynia to develop, suggesting a role for not only Aβ but also C-tactile afferent signaling. To examine the contribution of C-tactile afferents to the allodynic condition in humans, we applied the heat/capsaicin model of tactile allodynia in 43 healthy subjects and in 2 sensory neuronopathy patients lacking Aβ afferents. Healthy subjects reported tactile-evoked pain, whereas the patients did not. Instead, patients reported their C-touch percept (faint sensation of pleasant touch) to be significantly weaker in the allodynic zone compared to untreated skin. Functional magnetic resonance imaging in 18 healthy subjects and in 1 scanned patient indicated that stroking in the allodynic and control zones evoked different responses in the primary cortical receiving area for thin fiber signaling, the posterior insular cortex. In addition, reduced activation in the medial prefrontal cortices, key areas for C-tactile hedonic processing, was identified. These findings suggest that dynamic tactile allodynia is associated with reduced C-tactile mediated hedonic touch processing. Nevertheless, because the patients did not develop allodynic pain, this seems dependent on Aβ signaling, at least under these experimental conditions.
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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 | 0.000 |
| 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".