Allopregnanolone and neuroinflammation: a focus on multiple sclerosis
Bibliographic record
Abstract
The progesterone derivative allopregnanolone (ALLO) is one of the most widely studied compounds among neurosteroids. Through interactions with GABA-A receptors expressed by neurons and glial cells, ALLO has been shown to affect diverse aspects of neural cell physiology, including cell proliferation and survival, migration, and gene expression. Recent data point to important roles for ALLO in different neurodegenerative disorders, including Alzheimer's disease, Parkinson's disease, and multiple sclerosis (MS). Dysregulation in ALLO biosynthesis pathways has been reported in brain tissue from MS patients as well as in the central nervous system (CNS) tissue derived from MS animal models. Administration of ALLO has been shown to ameliorate neurobehavioral deficits together with neuropathology and inflammation in the CNS of animals with autoimmune demyelination. These findings are in line with previous reports indicating growth- and differentiation-promoting actions of ALLO on neurons and glial cells as well as its neuroprotective effects in the context of other CNS diseases. Nonetheless, these findings have also raised the possibility that ALLO might influence leukocyte biology and associated neuroinflammatory mechanisms independent of its neuroregenerative properties. Herein, we review the current knowledge regarding the role of ALLO in the pathogenesis of MS, and discuss the potential cellular and molecular pathways that might be influenced by ALLO in the context of disease.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".