Direct quantitative<sup>13</sup>C-filtered<sup>1</sup>H magnetic resonance imaging of PEGylated biomacromolecules in vivo
Bibliographic record
Abstract
Purpose 1H MRI is an established diagnostic method that generally relies on detection of water. Imaging specific macromolecules is normally accomplished only indirectly through the use of paramagnetic tags, which alter the water signal in their vicinity. We demonstrate a new approach in which macromolecular constituents, such as proteins and drug delivery systems, are observed directly and quantitatively in vivo using 1H MRI of 13C-labeled poly(ethylene glycol) (13C-PEG) tags. Methods Molecular imaging of 13C-PEG-labeled species was accomplished by incorporating a modified heteronuclear multiple quantum coherence filter into a gradient echo imaging sequence. We demonstrate the approach by monitoring the real-time distribution of 13C-PEG and 13C-PEGylated albumin injected into the hind leg of a mouse. Results Filtering the 1H PEG signal through the directly coupled 13C nuclei largely eliminates background water and fat signals, thus enabling the imaging of molecules using 1H MRI. Conclusion PEGylation is widely employed to enhance the performance of a multitude of macromolecular therapeutics and drug delivery systems, and 13C-filtered 1H MRI of 13C-PEG thus offers the possibility of imaging and quantitating their distribution in living systems in real time. Magn Reson Med 77:1553–1561, 2017. © 2016 International Society for Magnetic Resonance in Medicine
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".