Four‐pool modeling of proton exchange processes in biological systems in the presence of MRI–paramagnetic chemical exchange saturation transfer (PARACEST) agents
Bibliographic record
Abstract
Signal loss due to magnetization transfer (MT) from the macromolecular protons of biological tissues is an important consideration for the in vivo detection of paramagnetic chemical exchange saturation transfer (PARACEST) agents. In this study, a four-pool model is presented that is based on the modified Bloch equations and incorporates terms for the proton exchange processes that occur in biological systems in the presence of MRI-PARACEST contrast agents. The effect of the exchangeable proton chemical shift and PARACEST agent concentration are modeled in the presence of macromolecule-derived MT. Experimental validation of the model was performed at 9.4 Tesla using Eu(3+)-DOTAM-glycine (Gly)-phenylalanine (Phe) in both aqueous solution and samples containing 10% bovine serum albumin (BSA). The model was then used to measure the agent-bound-water chemical shift and the transverse relaxation time of macromolecular protons of a sample of Vero (nonhuman primate) cells labeled with Eu(3+)-DOTAM-Gly-Phe and a phantom containing mouse brain tissue and 7 mM Eu(3+)-DOTAM-Gly-Phe. In the brain tissue phantom, a chemical shift map with standard deviation (SD) < 0.7 ppm and a T(2) map with SD < 0.6 mus were obtained. The results demonstrate the feasibility of in vivo temperature measurement based on the bound-water chemical shift of Eu(3+)-DOTAM-Gly-Phe in combination with this four-pool model despite the inherent MT effect.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".