Hormonal Regulation of Oligodendrogenesis II: Implications for Myelin Repair
Bibliographic record
Abstract
Alterations in myelin, the protective and insulating sheath surrounding axons, affect brain function, as is evident in demyelinating diseases where the loss of myelin leads to cognitive and motor dysfunction. Recent evidence suggests that changes in myelination, including both hyper- and hypo-myelination, may also play a role in numerous neurological and psychiatric diseases. Protecting myelin and promoting remyelination is thus crucial for a wide range of disorders. Oligodendrocytes (OLs) are the cells that generate myelin, and oligodendrogenesis, the creation of new OLs, continues throughout life and is necessary for myelin plasticity and remyelination. Understanding the regulation of oligodendrogenesis and myelin plasticity within disease contexts is, therefore, critical for the development of novel therapeutic targets. In our companion manuscript, we review literature demonstrating that multiple hormone classes are involved in the regulation of oligodendrogenesis under physiological conditions. The majority of hormones enhance oligodendrogenesis, increasing oligodendrocyte precursor cell differentiation and inducing maturation and myelin production in OLs. Thus, hormonal treatments present a promising route to promote remyelination. Here, we review the literature on hormonal regulation of oligodendrogenesis within the context of disorders. We focus on steroid hormones, including glucocorticoids and sex hormones, peptide hormones such as insulin-like growth factor 1, and thyroid hormones. For each hormone, we describe whether they aid in OL survival, differentiation, or remyelination, and we discuss their mechanisms of action, if known. Several of these hormones have yielded promising results in both animal models and in human conditions; however, a better understanding of hormonal effects, interactions, and their mechanisms will ultimately lead to more targeted therapeutics for myelin repair.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".