Adenosine‐Linked CEST MRI Signatures of NAD⁺ Biosynthesis Precursors for Neurodegenerative Disease Detection
Bibliographic record
Abstract
BACKGROUND: Disruptions in NAD⁺ and purine metabolic pathways are implicated in neurodegenerative diseases such as Alzheimer's. Chemical Exchange Saturation Transfer (CEST) MRI offers a non-invasive modality to track these metabolic signatures in vivo. Identifying CEST properties of NAD⁺ and its precursors may enhance diagnostic sensitivity. METHOD: In vitro CEST MRI experiments were conducted using 3 mM agarose phantoms containing 30 mM of NAD⁺, NMN, NR, Na, Nam, or Trp in phosphate buffer across pH 5.5-8.0. Scans at 7T and 9.4T (Bruker BioSpin) were performed at 37 ± 0.2°C. Z-spectra were acquired using saturation transfer-prepared FLASH (7T) and RARE (9.4T) sequences with B₁ = 0.3-1.5 µT and Δω = ±7ppm. B₀ inhomogeneity was corrected using WASSR. T₁/T₂ were measured and Z-spectra were fitted with a three-pool Bloch-McConnell model. RESULT: NAD⁺ exhibited a prominent CEST peak at 2.03 ppm attributable to the adenine amine group, with up to 15% contrast and positive pH dependence at 2.98 ppm (amide). This 2.03 ppm signal-arising from the adenine ring common to NAD⁺, ATP, and adenosine-suggests potential for broader application in imaging purine metabolism. NMN also showed distinct peaks at 0.7, 2.92, and 4.42 ppm. Tryptophan and NR demonstrated pH-sensitive exchange, while most metabolites followed base-catalyzed exchange mechanisms. Peak separation and spectral clarity were enhanced at 9.4T, supporting further exploration of high-field imaging. CONCLUSION: The 2.03 ppm adenine-associated CEST peak from NAD⁺ represents a promising biomarker for imaging adenosine-linked metabolic pathways, including ATP and related purines. This in vitro characterisation highlights the feasibility of targeting this signal to non-invasively probe purine metabolism in neurodegeneration. These findings lay the groundwork for future in vivo studies investigating ATP, cAMP, and NAD⁺ dynamics via CEST MRI.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".