Unbiased proteomic analysis detects painful systemic inflammatory profile in the serum of nerve-injured mice
Bibliographic record
Abstract
ABSTRACT: Neuropathic pain is a complex, debilitating disease that results from injury to the somatosensory nervous system. The presence of systemic chronic inflammation has been observed in patients with chronic pain but whether it plays a causative role remains unclear. This study aims to determine the perturbation of systemic homeostasis by an injury to peripheral nerve and its involvement in neuropathic pain. We assessed the proteomic profile in the serum of mice at 1 day and 1 month after partial sciatic nerve injury (PSNL) or sham surgery. We also assessed mouse mechanical and cold sensitivity in naïve mice after receiving intravenous administration of serum from PSNL or sham mice. Mass spectrometry-based proteomic analysis revealed that PSNL resulted in a long-lasting alteration of serum proteome, where most of the differentially expressed proteins were in inflammation-related pathways, involving cytokines and chemokines, autoantibodies, and complement factors. Although transferring sham serum to naïve mice did not change their pain sensitivity, PSNL serum significantly lowered mechanical thresholds and induced cold hypersensitivity in naïve mice. With broad anti-inflammatory properties, bone marrow cell extracts not only partially restored serum proteomic homeostasis but also significantly ameliorated PSNL-induced mechanical allodynia, and serum from bone marrow cell extracts-treated PSNL mice no longer induced hypersensitivity in naïve mice. These findings clearly demonstrate that nerve injury has a long-lasting impact on systemic homeostasis, and nerve injury-associated systemic inflammation contributes to the development of neuropathic 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 | 0.000 |
| 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) | 0.001 | 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".