Sex differences in the inflammatory response of the mouse DRG and its connection to pain in experimental autoimmune encephalomyelitis
Bibliographic record
Abstract
Multiple Sclerosis (MS) is an autoimmune disease with notable sex differences. Women are not only more likely to develop MS but are also more likely than men to experience neuropathic pain in the disease. It has been postulated that neuropathic pain in MS can originate in the peripheral nervous system at the level of the dorsal root ganglia (DRG), which houses primary pain sensing neurons (nociceptors). These nociceptors become hyperexcitable in response to inflammation, leading to peripheral sensitization and eventually central sensitization, which maintains pain long-term. The mouse model experimental autoimmune encephalomyelitis (EAE) is a good model for human MS as it replicates classic MS symptoms including pain. Using EAE mice as well as naïve primary mouse DRG neurons cultured in vitro, we sought to characterize sex differences, specifically in peripheral sensory neurons. We found sex differences in the inflammatory profile of the EAE DRG, and in the TNFα downstream signaling pathways activated intracellularly in cultured nociceptors. We also found increased cell death with TNFα treatment. Given that TNFα signaling has been shown to initiate intrinsic apoptosis through mitochondrial disruption, this led us to investigate sex differences in the mitochondria's response to TNFα. Our results demonstrate that male sensory neurons are more sensitive to mitochondrial stress, making them prone to neuronal injury. In contrast, female sensory neurons appear to be more resistant to mitochondrial stress and exhibit an inflammatory and regenerative phenotype that may underlie greater nociceptor hyperexcitability and pain. Understanding these sex differences at the level of the primary sensory neuron is an important first step in our eventual goal of developing sex-specific treatments to halt pain development in the periphery before central sensitization is established.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".