Experimental autoimmune encephalomyelitis pathogenesis alters along animal age: impact of S100B expression
Bibliographic record
Abstract
Multiple Sclerosis (MS) is the leading inflammatory and non-traumatic cause of disability in young adults, with late-onset MS emerging in middle-aged patients often resulting in poorer treatment responses and worse prognoses. The calcium-binding protein S100B is elevated in MS patients, and its targeting has shown promise in reducing disease severity in experimental autoimmune encephalomyelitis (EAE) models. However, most studies on MS pathology have focused on young animal models, leaving a gap in understanding the effects of age and S100B ablation on disease progression throughout the lifespan. This study aimed to characterize EAE in mice of different ages, examining demyelination, inflammation, and immune responses to determine whether S100B ablation could mitigate MS pathogenesis across the lifespan. EAE was induced in six cohorts of C57BL/6 mice: young adults (3 months), older adults (6 months), and middle-aged (12 months), including corresponding S100B knockout (KO) groups, followed for 23 days. Upon sacrifice, spinal cords were assessed via immunohistochemistry and Real-Time qPCR, while splenocytes were analyzed for immune cell characterization. Results indicated a more severe disease course in 12-month-old mice, marked by increased gliosis, inflammation, and impaired microglial phagocytic activity. Notably, S100B absence reduced gliosis and inflammatory markers across all ages, with 12-month-old S100B KO mice showing increased regulatory T cells. These findings highlight the exacerbating role of age and elevated S100B in MS progression, underscoring the importance of identifying age-specific MS markers and therapeutic targets.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".