Ultrasound Imaging of Apoptosis in Tumor Response: Novel Preclinical Monitoring of Photodynamic Therapy Effects
Bibliographic record
Abstract
High-frequency ultrasound is a novel method to detect apoptotic cell death based on changes in cell morphology that cause alterations in the viscoelastic and, consequently, the acoustic properties of cell ensembles and tissues. In this study, we evaluated the first preclinical tumor-based use of high-frequency ultrasound spectroscopy to noninvasively monitor tumor treatment by following xenograft malignant melanoma tumor responses to photodynamic therapy (PDT) in vivo. We observed a time-dependant increase in ultrasound backscatter variables after treatment. The observed increases in spectroscopic variables correlated with morphologic findings, indicating increases in apoptotic cell death, which peaked at 24 hours after PDT. We analyzed the changes in spectral slope and backscatter in relation to apoptosis and histologic variations in cell nuclear size. Changes in spectral slope strongly correlated with the changes in mean nuclear size over time, associated with apoptosis, after PDT (P < 0.05). At 48 hours, a decrease in ultrasound backscatter was observed, which could be explained by an increase in cell nuclear degradation. In summary, we show that high-frequency ultrasound spectroscopic variables can be used noninvasively to monitor response after treatment in a preclinical tumor cancer model. These findings provide a foundation for future investigations regarding the use of ultrasound to monitor and aid the customization of treatments noninvasively based on responses to specific interventions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".