Combining <sup>31</sup>P‐<sup>1</sup>H Cross Polarization With Magnetization Transfer: A Novel Approach for Myelin Investigation
Bibliographic record
Abstract
ABSTRACT MRI is a crucial tool for studying white matter, which is primarily composed of myelin, a phospholipid‐rich sheath surrounding nerve fibers. Myelin damage leads to disrupted neurological function, which is a prominent feature in neurodegenerative diseases like multiple sclerosis. Current MRI techniques for detecting myelin use hydrogen nuclei (1H) exclusively to generate contrast. Phosphorus (31P) is highly concentrated in myelin phospholipids relative to other brain structures. Due to sensitivity of the anisotropic chemical shifts and dipolar couplings to structure and dynamics, 31P may provide richer and more specific ways to probe the myelin bilayers. Key experiments aimed at developing MRI compatible probes of myelin 31P are demonstrated. First, a solid‐state NMR technique, cross polarization (CP) is compared with single pulse excitation in white matter. The 1H‐31P CP spectrum retains the morphology sensitive 31P powder pattern of the single‐pulse spectrum, but lacks the aqueous 31P peak. Second, by combining magnetization transfer (MT) with CP, we observe bi‐directional polarization exchange between myelin 31P and surrounding water, apparently proceeding through a unique 1H pool that is distinct from the 1H that typically dominates MT. Pulsed magnetic field gradients are used to isolate magnetization that originates from myelin 31P and transferred to aqueous 1H. This small signal, approximately 1/68,000 that of the 1H water signal, could offer access to structural and dynamic information from the membrane/water interface not previously available. This two‐step transfer process opens new possibilities for understanding myelin and white matter disease and injury. These proof‐of‐principle findings may have broad implications for both basic neuroscience and clinical imaging. By leveraging 31P as a myelin probe, this approach offers a novel tool for studying myelin and could aid in detection and treatment monitoring of white matter disease and injury. Future work will investigate the translation of this technique to MRI.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".