Differences in iron and manganese concentration may confound the measurement of myelin from <i>R</i><sub>1</sub> and <i>R</i><sub>2</sub> relaxation rates in studies of dysmyelination
Bibliographic record
Abstract
A model of dysmyelination, the Long Evans Shaker (les) rat, was used to study the contribution of myelin to MR tissue properties in white matter. A large region of white matter was identified in the deep cerebellum and was used for measurements of the MR relaxation rate constants, R1 = 1/T1 and R2 = 1/T2 , at 7 T. In this study, R1 of the les deep cerebellar white matter was found to be 0.55 ± 0.08 s (-1) and R2 was found to be 15 ± 1 s(-1) , revealing significantly lower R1 and R2 in les white matter relative to wild-type (wt: R1 = 0.69 ± 0.05 s(-1) and R2 = 18 ± 1 s(-1) ). These deviated from the expected ΔR1 and ΔR2 values, given a complete lack of myelin in the les white matter, derived from the literature using values of myelin relaxivity, and we suspect that metals could play a significant role. The absolute concentrations of the paramagnetic transition metals iron (Fe) and manganese (Mn) were measured by a micro-synchrotron radiation X-ray fluorescence (μSRXRF) technique, with significantly greater Fe and Mn in les white matter than in wt (in units of μg [metal]/g [wet weight tissue]: les: Fe concentration,19 ± 1; Mn concentration, 0.71 ± 0.04; wt: Fe concentration,10 ± 1; Mn concentration, 0.47 ± 0.04). These changes in Fe and Mn could explain the deviations in R1 and R2 from the expected values in white matter. Although it was found that the influence of myelin still dominates R1 and R2 in wt rats, there were non-negligible changes in the contribution of the metals to relaxation. Although there are already problems with the estimation of myelin from R1 and R2 changes in disease models with pathology that also affects the relaxation rate constants, this study points to a specific pitfall in the estimation of changes in myelin in diseases or models with disrupted concentrations of paramagnetic transition metals. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".