The Molecular Signature of Neuropathic Pain in a Human Model System
Bibliographic record
Abstract
Abstract Peripheral neuropathic pain remains challenging to treat, partly due to our limited understanding of the molecular mechanisms at play in humans. In this multicentre cohort study, we describe the local molecular signature of neuropathic pain at the lesion site, using peripheral nerves of patients with Morton’s neuroma as a human model system of neuropathic pain. Plantar tibial nerves were collected from 22 patients with Morton’s neuroma (18 female, median age 60.0 [IQR 16.0] years) and control nerves from 11 participants (4 females, 58.0 [21.0] years) without a nerve injury. Pre-surgery, we collected data on pain severity, duration and nature (e.g., neuropathic pain inventory, NPSI). RNA bulk sequencing of peripheral nerves identified 3349 genes to be differentially expressed between Morton’s neuroma and controls. Gene ontology enrichment analysis and weighted gene co-expression network analyses (WGCNA) revealed modules specific for host defence and neurogenesis. Deconvolution analysis confirmed that the densities of macrophages as well as B-cells were higher in Morton’s neuroma than control samples. The findings for T-cells were inconclusive. Modules associated with defence response, neurogenesis and muscle system development correlated with paroxysmal and evoked pain in people with Morton’s neuroma. Macrophage cell populations identified by deconvolution analysis as well as single differentially expressed genes ( MARCO, CD163, STAB1; indicating the presence of a specific M(GC) subset of macrophages) correlated with paroxysmal pain. Immunofluorescent analyses confirmed the presence of demyelination, higher densities of intraneural T-cells and CD163 + MARCO + macrophage subsets in Morton’s neuroma compared to control nerves. Histological CD68 + macrophage density correlated with burning pain. Our findings provide detailed insight into the local molecular signature in the context of human focal nerve injury. There is clear evidence for an ongoing role of the immune system in chronic peripheral neuropathic pain in humans, with macrophages and specifically the M(GC) MARCO + subset implicated.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| 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".