Activatable NIR Fluorescence/MRI Bimodal Probes for in Vivo Imaging by Enzyme-Mediated Fluorogenic Reaction and Self-Assembly
Why this work is in the frame
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Bibliographic record
Abstract
Stimuli-responsive in situ self-assembly of small molecules to form nanostructures in living subjects has produced promising tools for molecular imaging and tissue engineering. However, controlling the self-assembly process to simultaneously activate multimodality imaging signals in a small-molecule probe is challenging. In this paper, we rationally integrate a fluorogenic reaction into enzyme-responsive in situ self-assembly to design small-molecule-based activatable near-infrared (NIR) fluorescence and magnetic resonance (MR) bimodal probes for molecular imaging. Using alkaline phosphatase (ALP) as a model target, we demonstrate that probe (P-CyFF-Gd) can be activated by endogenous ALP overexpressed on cell membranes, producing membrane-localized assembled nanoparticles (NPs) that can be directly visualized by cryo-SEM. Simultaneous enhancements in NIR fluorescence (>70-fold at 710 nm) and r1 relaxivity (∼2.3-fold) enable real-time, high-sensitivity, high-spatial-resolution imaging and localization of the ALP activity in live tumor cells and mice. P-CyFF-Gd can also delineate orthotopic liver tumor foci, facilitating efficient real-time, image-guided surgical resection of tumor tissues in intraoperative mice. This strategy combines activatable NIR fluorescence via a fluorogenic reaction and activatable MRI via in situ self-assembly to promote ALP activity imaging, which could be applicable to design other activatable bimodal probes for in vivo imaging of enzyme activity and locations in real time.
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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 it