Cooling reduces the cutaneous afferent firing response to vibratory stimuli in glabrous skin of the human foot sole
Bibliographic record
Abstract
Skin on the foot sole plays an important role in postural control. Cooling the skin of the foot is often used to induce anesthesia to determine the role of skin in motor and balance control. The effect of cooling on the four classes of mechanoreceptor in the skin is largely unknown, and thus the aim of the present study was to characterize the effects of cooling on individual skin receptors in the foot sole. Such insight will better isolate individual receptor contributions to balance control. Using microneurography, we recorded 39 single nerve afferents innervating mechanoreceptors in the skin of the foot sole in humans. Afferents were identified as fast-adapting (FA) or slowly adapting (SA) type I or II (FA I n = 16, FA II n = 7, SA I n = 6, SA II n = 11). Receptor response to vibration was compared before and after cooling of the receptive field (2-20 min). Overall, firing response was abolished in 30% of all receptors, and this was equally distributed across receptor type (P = 0.69). Longer cooling times were more likely to reduce firing response below 50% of baseline; however, some afferent responses were abolished with shorter cooling times (2-5 min). Skin temperature was not a reliable indicator of the level of receptor activation and often became uncoupled from receptor response levels, suggesting caution in the use of this parameter as an indicator of anesthesia. When cooled, receptors preferentially coded lower frequencies in response to vibration. In response to a sustained indentation, SA receptors responded more like FA receptors, primarily coding "on-off" events.
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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".