Detection of localized inclusions of gold nanoparticles in Intralipid-1% by point-radiance spectroscopy
Bibliographic record
Abstract
Interstitial fiber-optic-based approaches used in both diagnostic and therapeutic applications rely on localized light-tissue interactions. We present an optical technique to identify spectrally and spatially specific exogenous chromophores in highly scattering turbid media. Point radiance spectroscopy is based on directional light collection at a single point with a side-firing fiber that can be rotated up to 360 deg. A side firing fiber accepts light within a well-defined, solid angle, thus potentially providing an improved spatial resolution. Measurements were performed using an 800-μm diameter isotropic spherical diffuser coupled to a halogen light source and a 600 μm, ∼43 deg cleaved fiber (i.e., radiance detector). The background liquid-based scattering phantom was fabricated using 1% Intralipid. Light was collected with 1 deg increments through 360 deg-segment. Gold nanoparticles , placed into a 3.5-mm diameter capillary tube were used as localized scatterers and absorbers introduced into the liquid phantom both on- and off-axis between source and detector. The localized optical inhomogeneity was detectable as an angular-resolved variation in the radiance polar plots. This technique is being investigated as a potential noninvasive optical modality for prostate cancer monitoring.
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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 | 0.001 |
| 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 | 0.001 |
| 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".