Photoacoustic imaging biomarkers for monitoring biophysical changes during nanobubble-mediated radiation treatment
Bibliographic record
Abstract
The development of novel anticancer therapies warrants the parallel development of biomarkers that can quantify their effectiveness. Photoacoustic imaging has the potential to measure changes in tumor vasculature during treatment. Establishing the accuracy of imaging biomarkers requires direct comparisons with gold histological standards. In this work, we explore whether a new class of submicron, vascular disrupting, ultrasonically stimulated nanobubbles enhance radiation therapy. In vivo experiments were conducted on mice bearing prostate cancer tumors. Combined nanobubble plus radiation treatments were compared against conventional microbubbles and radiation alone (single 8 Gy fraction). Acoustic resolution photoacoustic imaging was used to monitor the effects of the treatments 2- and 24-hs post-administration. Histological examination provided metrics of tumor vascularity and tumoral cell death, both of which were compared to photoacoustic-derived biomarkers. Photoacoustic metrics of oxygen saturation reveal a 20 % decrease in oxygenation within 24 h post-treatment. The spectral slope metric could separate the response of the nanobubble treatments from the microbubble counterparts. This study shows that histopathological assessment correlated well with photoacoustic biomarkers of treatment response.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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".