Computational Modeling With Phantom-Tissue Validation of Gold-Nanorod-Enhanced Laser Ablation of Prostate Cancer
Bibliographic record
Abstract
Abstract The purpose of this study was to develop a computational model for the laser ablation (LA) of prostate cancer, enhanced by gold-nanorods (GNRs) in a phantom-tissue system, and to explore the effect of GNRs on the ablation zone. A prostate biomimetic tissue (PBT) was prepared with different volume fractions of GNRs (i.e., 0, 1.68 × 10−7 or 8.40 × 10−7). Specifically, the computational model was built by considering the change of light properties of PBTs with and without GNRs and introducing the dynamic heat source determined by porcine liver carbonization, reported elsewhere. The computational model was then validated by comparing the simulation and the ex vivo LA experiment in terms of three performance indexes, namely, (i) the spatiotemporal temperature distribution, (ii) ablation zone, and (iii) carbonization zone, with the three volume fractions of GNRs in the PBT model, as mentioned above. Except for minor discrepancies found in the carbonization zone, the proposed model agrees with the experimental data. The effect of GNRs on LA was explored with the help of the model, and nine combinations of the laser powers and the volume fractions of GNRs were tested. The result shows that the ablation zone increases with the increase in the volume fraction of GNRs for all three laser powers used. Two conclusions can be drawn: (1) loading GNRs into the tissues may increase the ablation zone of LA, and (2) the proposed computational model is a reliable tool for predicting the spatiotemporal temperature distribution and the ablation zone of the GNR-enhanced LA.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".