Rett syndrome: a neurological disorder with metabolic components
Bibliographic record
Abstract
Rett syndrome (RTT) is a neurological disorder caused by mutations in the X-linked gene methyl-CpG-binding protein 2 ( MECP2 ), a ubiquitously expressed transcriptional regulator. Despite remarkable scientific progress since its discovery, the mechanism by which MECP2 mutations cause RTT symptoms is largely unknown. Consequently, treatment options for patients are currently limited and centred on symptom relief. Thought to be an entirely neurological disorder, RTT research has focused on the role of MECP2 in the central nervous system. However, the variety of phenotypes identified in Mecp2 mutant mouse models and RTT patients implicate important roles for MeCP2 in peripheral systems. Here, we review the history of RTT, highlighting breakthroughs in the field that have led us to present day. We explore the current evidence supporting metabolic dysfunction as a component of RTT, presenting recent studies that have revealed perturbed lipid metabolism in the brain and peripheral tissues of mouse models and patients. Such findings may have an impact on the quality of life of RTT patients as both dietary and drug intervention can alter lipid metabolism. Ultimately, we conclude that a thorough knowledge of MeCP2's varied functional targets in the brain and body will be required to treat this complex syndrome.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".