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Record W4410198414 · doi:10.1016/j.bbi.2025.05.004

A recent history of immune cell sex differences in the peripheral nervous system in persistent pain states

2025· review· en· W4410198414 on OpenAlexafffund
Madelene F. S. Ho, Andréia V. Faria, Jason R. Plemel, Bradley J. Kerr

Bibliographic record

VenueBrain Behavior and Immunity · 2025
Typereview
Languageen
FieldMedicine
TopicPain Mechanisms and Treatments
Canadian institutionsUniversity of AlbertaWomen and Children’s Health Research Institute
FundersCanadian Institutes of Health Research
KeywordsPeripheralImmune systemPeripheral nervous systemMedicineNervous systemNeuroscienceCentral nervous systemImmunologyPsychologyInternal medicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.969
Threshold uncertainty score0.938

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.051
GPT teacher head0.293
Teacher spread0.242 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations9
Published2025
Admission routes2
Has abstractyes

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