A recent history of immune cell sex differences in the peripheral nervous system in persistent pain states
Bibliographic record
Abstract
Pain is entwined with inflammation, and biological sex often influences mechanisms of the immune system. Due to possible differences in inflammatory mechanisms, women are predisposed to autoimmune diseases and chronic pain. Despite sex as a critical variable in clinical cases of autoimmune conditions and its pain comorbidities, fundamental investigations have long underrepresented female subjects in their studies. Fundamental research in the 2010s, however, identified a binary sex specific mechanism for pain in rodents: male pain is microglia-driven while female pain is T cell-driven. Since then, studies have expanded in neuro-immunology to indicate that the sex differences and immune cells involved in these processes take on more elaborate roles when expanded to other causal modalities and anatomical levels of neuropathic and inflammatory pain. In this mini-review, we highlight updated roles for macrophages, T cells, and B cells in the peripheral nervous system during persistent pain conditions: neuropathic pain and inflammatory pain. We discuss sex similarities and sex differences in these cell types. By parsing out the sex specific roles of immune cells in persistent pain states, we may be better positioned to find immune-based therapies that can effectively target chronic pain in sex-biased autoimmune conditions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".