Association of Peripheral Serum MicroRNAs With Persistent Phantom Limb Pain in Individuals With Amputation
Bibliographic record
Abstract
OBJECTIVE: Individuals with major limb amputation(s) frequently experience phantom limb sensations, which are described as vivid impressions of either parts or entire missing limb(s). Despite the high incidence and prevalence of phantom limb pain, the underlying pathophysiology of phantom limb pain remains poorly understood. The objective of this study was to evaluate a possible role of microRNAs in the pathophysiology of phantom limb pain. DESIGN: Adults with acquired limb amputation and varying degrees of phantom limb pain consented to provide clinical data and blood samples. One hundred forty participants with single or multiple limb amputation(s) were enrolled. The Visual analog scale and neuropathic pain symptom inventory were administered to evaluate the pain. Serum samples were analyzed for microRNA expression and bioinformatic analysis was performed. RESULTS: Sixty-seven participants did not experience phantom limb pain, whereas 73 participants experienced varying severities of phantom limb pain measured on a pain scale. Linear regression analysis suggested that the time since amputation is inversely related to severity of the pain. A significantly increased expression of 16 microRNAs was observed in participants experiencing phantom limb pain. Bioinformatic analysis shows a possible role of these microRNAs in regulating genes expressed in peripheral neuropathy. CONCLUSIONS: This study provides the first evidence of association of microRNA in phantom limb pain.
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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 | 0.002 |
| 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".