Role of Peripheral Immune Cells in Multiple Sclerosis and Experimental Autoimmune Encephalomyelitis
Bibliographic record
Abstract
Multiple sclerosis (MS) is a chronic autoimmune disease that affects the myelination of the neurons present in the central nervous system (CNS). The exact etiology of MS development is unclear, but various environmental and genetic factors might play a role in initiating the disease. Experimental autoimmune encephalomyelitis (EAE) is a mouse model that is used to study the pathophysiology of MS disease as well as the effects of possible therapeutic agents. In addition, autoreactive immune cells trigger an inflammatory process upon the recognition of CNS antigens, which leads to destruction of the neurons. These include innate immune cells such as macrophages, dendritic cells, and natural killer cells. Additionally, the activation and extravasation of adaptive immune cells such as CD4+ T cells into the CNS may lead to further exacerbation of the disease. However, many studies revealed that immune cells could have either a protective or pathological role in MS. In this review, we highlight the roles of innate and adaptive immune cellular and soluble players that contribute to the pathogenesis of MS and EAE, which may be used as potential targets for therapy.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".