Estimation of Minimum Doses for Optimized Quantum Dot Contrast‐Enhanced Vascular Imaging In Vivo
Bibliographic record
Abstract
Quantum dot (QD) contrast-enhanced molecular imaging has potential for early cancer detection and image guided treatment, but there is a lack of quantitative image contrast data to determine optimum QD administered doses, affecting the feasibility, risk and cost of such procedures, especially in vivo. Vascular fluorescence contrast-enhanced imaging is performed on nude mice bearing dorsal skinfold window chambers, injected with 4 different QD solutions emitting in the visible and near infrared. Linear relationships are observed among the vascular contrast, injected contrast agent volume, and QD concentration in blood. Due primarily to differential light absorption by blood, the vasculature is optimally visualized when exciting in the 435-480 nm region in 81% of the cases (89 out of 110 regions of interest in 22 window chambers). The threshold dose, defined here as the quantity of injected nanoparticles required to yield a vascular target-to-autofluorescence ratio of 2, varies from 10.6 to 0.15 pmol g(-1) depending on the QD emission wavelength. The wavelength optimization maximum and broadband gain, defined as the ratio of threshold doses estimated for optimal and suboptimal (worst wavelength or broadband) spectral illumination, has average values of 4.5 and 1.9, respectively. This study demonstrates, for the first time, optimized QD imaging in vivo. It also proposes and validates a theoretical framework for QD dose estimation and quantifies the effects of blood absorption, QD emission wavelength, and vessel diameter relative to the threshold dose.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 | 0.000 |
| 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 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".