Cadmium-Free CuInS<sub>2</sub>/ZnS Quantum Dots for Sentinel Lymph Node Imaging with Reduced Toxicity
Bibliographic record
Abstract
Semiconductor quantum dots (QDs) could significantly impact the performance of biomedical near-infrared (NIR) imaging by providing fluorescent probes that are brighter and more photostable than conventional organic dyes. However, the toxicity of the components of NIR emitting II-VI and IV-VI QDs that have been made so far (Cd, Hg, Te, Pb, etc.) has remained a major obstacle to the clinical use of QDs. Here, we present the synthesis of CuInS(2)/ZnS core/shell QDs emitting in the NIR ( approximately 800 nm) with good quantum yield and stability even after transfer into water. We demonstrate the potential of these QDs by imaging two regional lymph nodes (LNs) in vivo in mice. We then compare the inflammatory response of the axillary LN induced by different doses of CuInS(2)/ZnS and CdTeSe/CdZnS QDs and show a clear difference in acute local toxicity, the onset of inflammation only occurring at a 10 times more concentrated dose for CuInS(2)/ZnS QDs than for their Cd-containing counterparts.
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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 | 0.000 | 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".