A novel near-infrared nanomaterial with high quantum efficiency and its applications in real time <i>in vivo</i> imaging
Bibliographic record
Abstract
Abstract Fluorescence imaging signal is severely limited by the quantum efficiency and emission wavelength. To overcome these challenges, novel NIR-emitting K 5 NdLi 2 F 10 nanoparticles under NIR excitation was introduced as fluorescence imaging probe for the first time. The photostability of K 5 NdLi 2 F 10 nanoparticles in the water, phosphate buffer saline, fetal bovine serum and living mice was investigated. The fluorescence signal was detected with depths of 3.5 and 2.0 cm in phantom and pork tissue, respectively. Fluorescence spectrum with a significant signal-to-background ratio of 10:1 was captured in living mice. Moreover, clear NIR images were virtualized for the living mice after intravenous injection. The imaging ability of nanoparticles in tumor-beard mice were evaluated, the enrichment of K 5 NdLi 2 F 10 nanoparticles in tumor site due to the enhanced permeability and retention effect was confirmed. The systematic studies of toxicity, bio-distribution and in-vivo dynamic imaging suggest that these materials give high biocompatibility and low toxicity. These NIR-emitting nanoparticles with high quantum efficiency, high penetration and low toxicity might facilitate tumor identification in deep tissues more sensitively.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".