[18F]FDG-PET and [18F]MPPF-PET are brain biomarkers for the creatine transporter Slc6a8 loss of function mutation
Bibliographic record
Abstract
Abstract Pathogenic variants in the creatine transporter gene SLC6A8, reported to represent 2% of all intellectual disabilities in males, result in a spectrum of behavioral abnormalities including developmental delay, intellectual disability, and deficit in speech. While at present there are no effective treatments available, preclinical development and testing of gene therapy and other approaches to increase brain creatine are being actively pursued. In studying a mouse model of the disorder, [18F]fluorodeoxyglucose ([18F]FDG)-based positron emission tomography (PET)/computed tomography (CT) was performed to assess brain glucose metabolism in wild type and creatine transporter mutant mice (Slc6a8 -/y ). The findings demonstrate marked differences in glucose metabolism in the brains of wild type and Slc6a8 -/y mice. In conducting behavioral phenotyping studies, notable abnormalities in behavior in the murine model led to additional studies in serotonin-mediated activity. Serotonergic signaling differences were detected between wild type and Slc6a8 -/y mice using 4-(2′-methoxyphenyl)-1-[2′-(N-2″-pyridinyl)-p-[18F]fluorobenzamido]ethylpiperazine ([18F]MPPF). These data demonstrate that [18F]FDG-PET and [18F]-MPPF-PET may serve as appropriate and sensitive biomarkers that could be used to assess the efficacy of not only new approaches in treating mutations of the creatine transporter SLC6A8 and their effectiveness in normalizing brain metabolism but also in enhancing our understanding of the mechanism of brain dysfunction that occurs in this complex brain disorder.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".