Multiple Sclerosis and the Endogenous Opioid System
Bibliographic record
Abstract
Multiple sclerosis (MS) is an autoimmune disease characterized by chronic inflammation, neuronal degeneration and demyelinating lesions within the central nervous system. The mechanisms that underlie the pathogenesis and progression of MS are not fully known and current therapies have limited efficacy. Preclinical investigations using the murine experimental autoimmune encephalomyelitis (EAE) model of MS, as well as clinical observations in patients with MS, provide converging lines of evidence implicating the endogenous opioid system in the pathogenesis of this disease. In recent years, it has become increasingly clear that endogenous opioid peptides, binding μ- (MOR), κ- (KOR) and δ-opioid receptors (DOR), function as immunomodulatory molecules within both the immune and nervous systems. The endogenous opioid system is also well known to play a role in the development of chronic pain and negative affect, both of which are common comorbidities in MS. As such, dysregulation of the opioid system may be a mechanism that contributes to the pathogenesis of MS and associated symptoms. Here, we review the evidence for a connection between the endogenous opioid system and MS. We further explore the mechanisms by which opioidergic signaling might contribute to the pathophysiology and symptomatology of MS.
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".