A Reactive and Specific Sensor for Activity-Based <sup>19</sup>F-MRI Sensing of Zn<sup>2+</sup>
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide The rapid fluctuations of metal ion levels in biological systems are faster than the time needed to map fluorinated sensors designed for the 19 F-MRI of cations. An attractive modular solution might come from the activity-based sensing approach. Here, we propose a highly reactive but still ultimately specific synthetic fluorinated sensor for 19 F-MRI mapping of labile Zn 2+ . The sensor comprises a dipicolylamine scaffold for Zn 2+ recognition conjugated to a fluorophenyl acetate entity. Upon binding to Zn 2+, the synthetic sensor is readily hydrolyzed, and the frequency of its 19 F-functional group in 19 F-NMR is shifted by 12 ppm, allowing the display of the Zn 2+ distribution as an artificial MRI-colored map highlighting its specificity compared to other metal ions. The irreversible Zn 2+ -induced hydrolysis results in a “turn-on” 19 F-MRI, potentially detecting the cation even upon a transient elevation of its levels. We envision that additional metal-ion sensors can be developed based on the principles demonstrated in this work, expanding the molecular toolbox currently used for 19 F-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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".