Puberty in females enhances the risk of an outcome of multiple sclerosis in children and the development of central nervous system autoimmunity in mice
Bibliographic record
Abstract
BACKGROUND: For reasons that remain unclear, three times more women develop multiple sclerosis (MS) than men. This preponderance among women is evident only after 12 years of age, implicating pubertal factors in the risk of MS. OBJECTIVE: To investigate the influence of female puberty on central nervous system (CNS) autoimmunity. METHODS: We examined the relationship between age of menarche on MS outcomes in 116 female children (< 16 years old) whom presented with incident 'acquired demyelinating syndromes' (ADS) and were followed prospectively in the national Canadian Pediatric Demyelinating Disease Study, from 2004-2013. Furthermore, we directly investigated the effects of puberty on susceptibility to experimental autoimmune encephalomyelitis (EAE) in two groups of female mice that differed only in their pubertal status. RESULTS: In the ADS children, a later age of menarche was associated with a decreased risk of subsequent MS diagnosis. This relationship persisted, after accounting for patient age at ADS presentation and the presence of ≥1 T2 lesions on brain magnetic resonance imaging (MRI), with a hazard ratio (HR) of 0.64; and additional factors that associate with MS outcomes in ADS children, including low vitamin D levels. Furthermore, we found female mice that had transitioned through puberty were more susceptible to EAE than age-matched, pre-pubertal mice. CONCLUSION: Puberty in females enhances CNS autoimmune mechanisms that lead to MS in humans and EAE in mice.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".