The impact of nerve injury on the immune system across the lifespan is sexually dimorphic
Bibliographic record
Abstract
Although nerve injury-associated neuroinflammation contributes to neuropathic pain, the long-term impact of such injury on systemic homeostasis and its potential role in pain remains elusive. In this study, we aim to understand the systemic changes that are present alongside chronic pain in nerve-injured male and female mice across their lifespan. We monitored mechanical and cold sensitivity in male and female mice starting at the age of 3-4 months old when they received spared nerve injury (SNI), up to 20-month post-injury. Alongside, we collected blood samples to track changes in immune cells with flow cytometry, and to assess inflammation-related serum proteome using a 111-target Proteome Profiler. We also transferred serum from sham/SNI mice to naïve mice to determine the potential of systemic contribution to pain. While nerve injury did not affect immune cell composition in the blood, it triggered a long-lasting disturbance of molecular profile in the serum of sham/SNI mice, in a sex-dependent manner. Compared to sham surgery, nerve injury amplified regulation of inflammatory proteins in males, but slightly reduced it in females. These changes in the serum occurred in parallel with long-lasting mechanical and cold hypersensitivity in the nerve-injured mice. Both male and female SNI serum induced hypersensitivity when transferred to naïve mice, regardless of a sex-matched or sex-mismatched transfer. Our results highlight that a local nerve injury can have persistent systemic impact. Injury-associated systemic inflammation could contribute to neuropathic pain, but the underlying mechanisms may be sexually dimorphic.
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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 | 0.001 |
| 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".