Magnetic resonance imaging reveals specific anatomical changes in the brain of Agat‐ and Gamt‐mice attributed to creatine depletion and guanidinoacetate alteration
Bibliographic record
Abstract
Abstract Arginine:glycine amidinotransferase‐ and guanidinoacetate methyltransferase deficiency are severe neurodevelopmental disorders. It is not known whether mouse models of disease express a neuroanatomical phenotype. High‐resolution magnetic resonance imaging (MRI) with advanced image analysis was performed in perfused, fixed mouse brains encapsulated with the skull from male, 10‐12 week old Agat ‐exc and B6J.Cg‐Gamt tm1Isb mice (n = 48; n = 8 per genotype, strain). T2‐weighted MRI scans were nonlinearly aligned to a 3D atlas of the mouse brain with 62 structures identified. Local differences in brain shape related to genotype were assessed by analysis of deformation fields. Creatine (Cr) and guanidinoacetate (GAA) were measured with high‐performance liquid chromatography (HPLC) in brain homogenates (n = 24; n = 4 per genotype, strain) after whole‐body perfusion. Cr was decreased in the brain of Agat‐ and Gamt mutant mice. GAA was decreased in Agat−/− and increased in Gamt−/−. Body weight and brain volume were lower in Agat−/− than in Gamt−/−. The analysis of entire brain structures revealed corpus callosum, internal capsule, fimbria and hypothalamus being different between the genotypes in both strains. Eighteen and fourteen significant peaks (local areas of difference in relative size) were found in Agat‐ and Gamt mutants, respectively. Comparing Agat−/− with Gamt−/−, we found changes in three brain regions, lateral septum, amygdala, and medulla. Intra‐strain differences in four brain structures can be associated with Cr deficiency, while the inter‐strain differences in three brain structures of the mutant mice may relate to GAA. Correlating these neuroanatomical findings with gene expression data implies the role of Cr metabolism in the developing brain and the importance of early intervention in patients with Cr deficiency syndromes.
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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 | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".