Highly thermal stable RNase A@PbS/ZnS quantum dots as NIR-IIb image contrast for visualizing temporal changes of microvasculature remodeling in flap
Bibliographic record
Abstract
Abstract Surgeons face great challenges in acquiring high-performance imaging because fluorescence probes with desired thermal stability remains rare. Here, hybrid lead sulfide/zinc sulfide quantum dots (PbS/ZnS QDs) nanostructures emitting in the long-wavelength end of the second near-infrared (NIR-IIb) window were synthesized and conjugated with Ribonuclease-A (RNase A). Such formed RNase A@PbS/ZnS QDs exhibited strong NIR IIb fluorescence and thermal stability, as supported by the photoluminescent emission assessment at different temperatures. This will allow the RNase A@PbS/ZnS QDs to provide stable fluorescence signals for long-time intraoperative imaging navigation, despite often happened, undesirable thermal accumulation in vivo. Compared to NIR-IIa fluorescence imaging, NIR-IIb vascular fluorescence imaging achieved larger penetration depth, higher signal/background ratios and nearly zero endogenous tissue autofluorescence. Moreover, these QDs illustrate the reliability during the real-time and long-time precise assessment of flap perfusion by clearly visualizing microvasculature map. These findings contribute to intraoperative imaging navigation with higher precision and lower risk. Graphical Abstract
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How this classification was reachedexpand
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 | 0.001 |
| 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) | 0.220 | 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 teacher head, 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".