Bibliographic record
Abstract
The tumour vasculature supplies the tumour with oxygen and nutrition that are vital for the survival and growth of cancer cells. Vascular disrupting agents (VDAs) are a category of cancer therapeutic drugs that target the endothelial cells lining the vessels and damage tumour vasculature. Photoacoustic (PA) imaging has high sensitivity in detecting vascular changes due to the high optical absorption of hemoglobin. PA quantitative analysis can be used to further extract structural and biochemical changes of hemoglobin through ultrasound spectral analysis and multi-spectral PA imaging. I proposed the use of simulations, in-vitro, and in-vivo PA imaging to assess the efficacy of VDAs in cancer therapy. PA signals acquired from single and a collection of red blood cells (RBCs) were examined before and after acid sphingomyelinase exposure (SMase is a signaling molecule excreted due to endothelial cells damage) to investigate changes at the cellular level. Bleeding of tumour vasculature was simulated using a fractal-based model of bifurcating cylinders and the diffusion of blood to the tumour interstitium. Wavelength selection and fluence matching approaches were proposed to improve chromophore quantification in real time for PA imaging. In-vivo experiments were conducted to examine how PA imaging can be used to monitor the effect of the VDA 5,6-dimethylxanthenone-4-acetic acid (DMXAA). PA ultrasound frequency analysis and multi-spectral PA imaging were used to detect structural and biochemical changes to the tumour vasculature. PA quantitative analysis of the in-vivo data demonstrates a decrease in the total hemoglobin 72 hrs post DMXAA injection and an increase in Hb 24 hrs post DMXAA injection. The changes in the chromophores’ composition are due to changes in the environment as the RBCs extravasate to the surrounding tissues as seen from the in-vitro experiments. A decrease in the average spectral slope at 24 hrs post injection was measured. The decrease in the spectral slope was correlated to an increase in the effective vessel size due to bleeding, which was also supported by the vascular tree simulations. The combination of simulations, in-vitro and in-vivo studies demonstrate the capability of PA signal analysis in monitoring VDA at early time points.
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 | 0.000 | 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".