Microglia and multiple sclerosis
Bibliographic record
Abstract
Microglia participate in all phases of the multiple sclerosis (MS) disease process. As members of the innate immune system, these cells have evolved to respond to stranger/danger signals; such a response within the central nervous system (CNS) environment has the potential to induce an acute inflammatory response. Engagement of Toll-like receptors (TLRs), a major family of pattern-recognition receptors (PRRs), provides an important mechanism whereby microglia can interact with both exogenous and endogenous ligands within the CNS. Such interactions modulate the capacity of microglia to present antigens to cells of the adaptive immune system and thus contribute to the initiation and propagation of the more sophisticated antigen-directed responses. This inflammatory response introduces the potential for bidirectional feedback between CNS resident and infiltrating systemic cells. Such interactions acquire particular relevance in the era of therapeutics for MS because the infiltrating cells can be subjected to systemic immunomodulatory therapies known to change their functional properties. Phagocytosis by microglia/macrophages is a hallmark of the MS lesion; however, the extent of tissue damage and the type of cell death will dictate subsequent innate responses. Microglia/macrophages are armed with a battery of effector molecules, such as reactive nitrogen species, that may contribute to CNS tissue injury, specifically to the injury of oligodendrocytes that is associated with MS. A therapeutic challenge is to modulate the dynamic properties of microglia/macrophages so as to limit potentially damaging innate responses, to protect the CNS from injury, and to promote local recovery.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".