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 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 | 0.001 | 0.001 |
| 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) | 0.039 | 0.004 |
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".