Optical detection of gold nanoparticles in a prostate-shaped porcine phantom
Bibliographic record
Abstract
Gold nanoparticles can be used as molecular contrast agents binding specifically to cancer sites and thus delineating tumor regions. Imaging gold nanoparticles deeply embedded in tissues with optical techniques possesses significant challenges due to multiple scattering of optical photons that blur the obtained images. Both diagnostic and therapeutic applications can benefit from a minimally invasive technique that can identify, localize, and quantify the payloads of gold nanoparticles deeply embedded in biological tissues. An optical radiance technique is applied to map localized inclusions of gold nanorods in 650- to 900-nm spectral range in a porcine phantom that mimics prostate geometry. Optical radiance defines a variation in the angular density of photons impinging on a selected point in the tissue from various directions. The inclusions are formed by immersing a capillary filled with gold nanorods in the phantom at increasing distances from the detecting fiber. The technique allows the isolation of the spectroscopic signatures of the inclusions from the background and identification of inclusion locations in the angular domain. Detection of ∼4×1010 gold nanoparticles or 0.04 mg Au/mL (detector-inclusion separation 10 mm, source-detector separation 15 mm) in the porcine tissue is demonstrated. The encouraging results indicate a promising potential of radiance spectroscopy in early prostate cancer diagnostics with gold nanoparticles.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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".