Assessing Near-Infrared Quantum Dots for Deep Tissue, Organ, and Animal Imaging Applications
Bibliographic record
Abstract
Semiconductor quantum dots (Qdots) have emerged as novel ultrasensitive optical probes to target, detect, and image fundamental events occurring within the biological system. In particular, near-infrared (near-IR) Qdots holds great promise as in vivo contrast agents for real-time bioimaging capabilities. In this study, biocompatible near-IR Qdots are used to image organs, tissues, and cells. Compared to visible Qdots, we obtained a significant enhancement in signal detection sensitivity for imaging deep tissues and organs. In addition, biomolecules were used to target these optical contrast agents for multiplexed imaging of cells and organs in vivo. The ability to simultaneously distinguish emission profiles of multiple near-IR Qdots will likely emerge as important tools for addressing fundamental questions in molecular biology and in medical sciences.
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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 | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".