Hormones in experimental autoimmune encephalomyelitis (EAE) animal models
Bibliographic record
Abstract
Abstract Multiple sclerosis (MS) is an inflammatory demyelinating disease of the central nervous system (CNS) in which activated immune cells attack the CNS and cause inflammation and demyelination. While the etiology of MS is still largely unknown, the interaction between hormones and the immune system plays a role in disease progression, but the mechanisms by which this occurs are incompletely understood. Severalin vitroandin vivoexperimental, but also clinical studies, have addressed the possible role of the endocrine system in susceptibility and severity of autoimmune diseases. Although there are several demyelinating models, experimental autoimmune encephalomyelitis (EAE) is the oldest and most commonly used model for MS in laboratory animals which enables researchers to translate their findings from EAE into human. Evidences imply that there is great heterogeneity in the susceptibility to the induction, the method of induction, and the response to various immunological or pharmacological interventions, which led to conflicting results on the role of specific hormones in the EAE model. In this review, we address the role of endocrine system in EAE model to provide a comprehensive view and a better understanding of the interactions between the endocrine and the immune systems in various models of EAE, to open up a ground for further detailed studies in this field by considering and comparing the results and models used in previous studies.
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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 | 0.002 | 0.002 |
| 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) | 0.003 | 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".