Phantom studies with gold nanorods as contrast agents for photoacoustic imaging: novel and old approaches
Bibliographic record
Abstract
Photoacoustic imaging is emerging as a bioimaging technique. The development of contrast agents extend the potential towards novel application. The design of stable phantoms is needed to achieve a semi-quantitative evaluation of the performance of contrast agents. The aim of this study was to investigate the PA signal generated from gold nanorods (GNRs) loaded in custom made phantoms. VevoLAZR (VisualSonics Inc., Toronto) was used with custom made agar phantom, with 5 parallel polyethylene tubes (with 0.58mm internal and 0.99mm external diameter), and a PDMS phantom, with six parallel channels with sizes from 50 μm to 500 μm, loaded with two different types of GNRs: PEGGNRs (53nm length and 11nm axial diameter, plasmon resonance at 840nm, 87nM (15mM Au equivalent)); and gold nanorods (NPZ) coated in a dense layer of hydrophilic polymers by Nanopartz Inc., Loveland, CO (41nm length and 10nm axial diameter, plasmon resonance at 808nm, 83 nM (14mM Au equivalent)). The absorption spectra acquired with the PA system and the spectrophotometer were compared. The reproducibility and stability of the PA signal were evaluated at different dilutions. The dynamic variation of the PA signal was evaluated as function of the number of the GNRs. The SNR and the contrast were measured across the range of concentrations studied. The custom made agar phantom demonstrated suitable for the characterization of PA contrast agents such as PEG-GNRs and NPZ. The PDMS phantom is promising in the field of photoacoustics, therefore future works will conducted exploiting its precise and controlled geometry.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".