Prediction of Gold Nanoparticle and Microwave-Induced Hyperthermia Effects on Tumor Control via a Simulation Approach
Bibliographic record
Abstract
Hyperthermia acts as a powerful adjuvant to radiation therapy and chemotherapy. Recent advances show that gold nanoparticles (Au-NPs) can mediate highly localized thermal effects upon interaction with laser radiation. The purpose of the present study was to investigate via in silico simulations the mechanisms of Au-NPs and microwave-induced hyperthermia, in correlation to predictions of tumor control (biological endpoints: tumor shrinkage and cell death) after hyperthermia treatment. We also study in detail the dependence of the size, shape and structure of the gold nanoparticles on their absorption efficiency, and provide general guidelines on how one could modify the absorption spectrum of the nanoparticles in order to meet the needs of specific applications. We calculated the hyperthermia effect using two types of Au-NPs and two types of spherical tumors (prostate and melanoma) with a radius of 3 mm. The plasmon peak for the 30 nm Si-core Au-coated NPs and the 20 nm Au-NPs was found at 590 nm and 540 nm, respectively. Considering the plasmon peaks and the distribution of NPs in the tumor tissue, the induced thermal profile was estimated for different intervals of time. Predictions of hyperthermic cell death were performed by adopting a three-state mathematical model, where "three-state" includes (i) alive, (ii) vulnerable, and (iii) dead states of the cell, and it was coupled with a tumor growth model. Our proposed methodology and preliminary results could be considered as a proof-of-principle for the significance of simulating accurately the hyperthermia-based tumor control involving the immune system. We also propose a method for the optimization of treatment by overcoming thermoresistance by biological means and specifically through the targeting of the heat shock protein 90 (HSP90), which plays a critical role in the thermotolerance of cells and tissues.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.000 |
| 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".