Normal-appearing White Matter in Patients with Phenylketonuria: Water Content, Myelin Water Fraction, and Metabolite Concentrations
Bibliographic record
Abstract
PURPOSE: To prospectively assess relative water content (RWC), myelin water fraction (MWF), and hydrogen 1 magnetic resonance (MR) spectroscopy findings in the white matter (WM) of patients with phenylketonuria (PKU). MATERIALS AND METHODS: This study was approved by the institution's investigational review board, and informed consent was obtained. T2 water relaxation data were acquired by using a 48-echo measurement in a transverse plane through the genu and splenium of the corpus callosum in 16 patients (six men, 10 women; age range, 18-40 years) with PKU and 16 age- and sex-matched control subjects. MR spectroscopy was performed in a voxel (94x70x15 mm) above the ventricles. WM in control subjects (defined as normal WM) was compared with normal-appearing WM (NAWM) and diffuse WM lesions in patients with PKU by using a Student t test. RESULTS: Patients with PKU had two forms of NAWM: (a) areas that looked normal on intermediate-weighted (IW) and T2-weighted MR images and long T2 maps and (b) areas that looked normal on IW and T2-weighted MR images but were hyperintense on long T2 maps. Both forms of NAWM showed increased RWC (up to 2.5%, P<.001) and reduced MWF (up to 56%, P<.001) relative to normal WM; these changes paralleled those seen in diffuse WM lesions. Approximately 9% of the water in diffuse WM lesions was in a reservoir with a long T2 time of 200-800 msec. Myoinositol concentrations were reduced by 14% (P=.003) in patients with PKU. CONCLUSION: In patients with PKU, NAWM and diffuse WM lesions have altered RWC and MWF relative to normal WM, and diffuse WM lesions show a redistribution of water into an extracellular reservoir with a long T2 time.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".