Stress, the immune system and vulnerability to degenerative disorders of the central nervous system in transgenic mice expressing glucocorticoid receptor antisense RNA
Bibliographic record
Abstract
Current research evidence suggests that interactions between genetic and environmental factors contribute to modulate the susceptibility to degenerative disorders, including inflammatory and autoimmune diseases of the central nervous system (CNS). In this context, bidirectional communication between the neuroendocrine and immune systems during ontogeny plays a pivotal role in programming the development of neuroendocrine and immune responses in adult life, thereby influencing the predisposition to several disease entities. Glucocorticoids (GCs), the end products of the hypothalamic-pituitary-adrenocortical (HPA) axis, gender and signals generated by hypothalamic-pituitary-gonadal (HPG) axis are major players coordinating the development of immune system function and exerting powerful effects in the susceptibility to autoimmune disorders, including experimental autoimmune encephalomyelitis (EAE), the experimental model for multiple sclerosis (MS). In particular, GCs exert their beneficial immunosuppressive and anti-inflammatory effects in inflammatory disorders of the CNS, after binding to their cytoplasmic receptors (GRs). Here we review our work using transgenic (Tg) mice with a dysfunctional GR from early embryonic life on programming vulnerability to EAE. The GR-deficiency of these Tg mice confers resistance to active EAE induction. The interplay between GCs, proinflammatory mediators, gender and EAE is summarized. On the basis of our data, it does appear that exposure to a defective GR through development programs major changes in endogenous neuroendocrine and immune mechanisms controlling the vulnerability to EAE. These studies highlight the plasticity of the HPA-immune axis and its pharmacological manipulation in autoimmune diseases of the CNS.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".