Early life stress aggravates disease pathogenesis in mice with experimental autoimmune encephalomyelitis: Support for a two-hit hypothesis of multiple sclerosis etiology
Bibliographic record
Abstract
Vision problems are one of the earliest diagnosed symptoms of multiple sclerosis (MS). The onset and progression of vision loss and the underlying pathogenesis in MS may be influenced by cumulative psychophysiological stress. Here, we used a two-hit model of stress in female mice to determine if early life stress (ELS, the first hit) influences the response to an immunization that induces experimental autoimmune encephalomyelitis (EAE, the second hit) later in life. We hypothesized that ELS caused by animal transportation from a vendor during early postnatal development represents a co-factor which can exacerbate the clinical severity of EAE. Indeed, adult EAE mice with a history of ELS displayed more severe clinical signs and delayed recovery compared to non-stressed EAE mice. ELS also diminished visual acuity measured by optokinetic responses, as well as locomotion and exploratory behaviours in EAE mice. Notably, ELS accelerated vision loss and caused earlier onset of visual impairments in EAE. Exacerbated functional impairments in stressed EAE mice were highly correlated with circulating corticosterone levels. The findings show that the progression of induced EAE in adulthood can be significantly impacted by adverse early life experiences. These observations emphasize the importance of comprehensive behavioural testing, including non-motor functions, to enhance the translational value of preclinical animal models of MS. Moreover, shipment stress of laboratory animals should be considered a necessary variable in preclinical MS research. The consideration of cumulative lifetime stresses provides a new perspective of MS pathogenesis within a personalized medicine framework.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".