Quantifying intestinal lipolysis with MRI and TD-NMR: Proof of concept using dairy cream digested in vitro
Bibliographic record
Abstract
Understanding lipid digestion is crucial for promoting human health. Traditional methods for studying lipolysis face challenges in sample representativeness and pre-treatment, and cannot measure real-time lipolysis in vivo . Thus, non-invasive techniques like magnetic resonance imaging (MRI) need to be developed. This study assessed the MRI water-fat separation method for monitoring in vitro intestinal digestion of dairy cream , supported by high-performance thin-layer chromatography (HP-TLC) and time-domain nuclear magnetic resonance (TD-NMR). A clear distinction was found between the T 2 of undigested lipids (∼120 ms) and lipolytic products (0.1–15 ms). The short T 2 of lipolytic products likely results from semi-crystalline structures formed with bile salts. While MRI methods cannot detect such fast-relaxing protons, it effectively quantified lipolysis by tracking the residual undigested lipids, showing high correlation with HP-TLC results (R 2 = 0.93 and 0.95 for 13-s and 6-min MRI methods, respectively). The rapid 13-s MRI method offers strong potential for future in vivo applications.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".