Spinal cord injury transiently alters Meissner's corpuscle density in the digit pads of macaque monkeys
Bibliographic record
Abstract
Meissner's corpuscles (MCs) are cutaneous mechanoreceptors found in glabrous skin and are exquisitely sensitive to light touch. Along with other receptors, they provide continuous sensory feedback that informs the execution of fine manual behaviors. Following cervical spinal deafferentation injuries, hand use can be initially severely impaired, but substantial recovery occurs over many weeks, even when ~95% of the original input is permanently lost. While most SCI research focuses on central neural pathway responses, little is known about the role of peripheral receptors in facilitating recovery. We begin to address this by asking the following: (1) What is the normal pattern of MCs in the distal pads of all five digits in the macaque monkey (with hands similar to humans)? (2) What happens to these receptors 4-5 months following either a dorsal column lesion (DCL) or a combined dorsal root/dorsal column lesion (DRL/DCL), when functional recovery is largely complete? (3) What happens chronically, 12-14 months later? Our findings show that in normal monkeys, MCs are densest in the distal pads of the opposing thumb and index finger, with the greatest concentration on the thumb. This reflects a close functional relationship between receptor density and precision grip. At 4-5 months post-injury, there was a (~30%) loss of MCs on the deafferented digits of the injured hand compared with the contralateral side. However, 12-14 months after a DRL/DCL, receptor densities had returned to normal levels. Our findings indicate a complex peripheral response and highlight the importance of the periphery in shaping central changes.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".