TNFα in MS and Its Animal Models: Implications for Chronic Pain in the Disease
Bibliographic record
Abstract
Multiple Sclerosis (MS) is a debilitating autoimmune disease often accompanied by severe chronic pain. The most common type of pain in MS, called neuropathic pain, arises from disease processes affecting the peripheral and central nervous systems. It is incredibly difficult to study these processes in patients, so animal models such as experimental autoimmune encephalomyelitis (EAE) mice are used to dissect the complex mechanisms of neuropathic pain in MS. The pleiotropic cytokine tumor necrosis factor α (TNFα) is a critical factor mediating neuropathic pain identified by these animal studies. The TNF signaling pathway is complex, and can lead to cell death, inflammation, or survival. In complex diseases such as MS, signaling through the TNFR1 receptor tends to be pro-inflammation and death, whereas signaling through the TNFR2 receptor is pro-homeostatic. However, most TNFα-targeted therapies indiscriminately block both arms of the pathway, and thus are not therapeutic in MS. This review explores pain in MS, inflammatory TNF signaling, the link between the two, and how it could be exploited to develop more effective TNFα-targeting pain therapies.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".