Activatable NIR Fluorescence/MRI Bimodal Probes for in Vivo Imaging by Enzyme-Mediated Fluorogenic Reaction and Self-Assembly
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 r 1 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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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) | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 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".