Breast cancer therapy by laser-induced Coulomb explosion of gold nanoparticles.
Bibliographic record
Abstract
OBJECTIVE: Laser-induced Coulomb explosion of gold nanoparticles for breast cancer has been studied by nanophotolysis technique. This study aimed to investigate whether laser-induced bubble formation due to Coulomb explosion can provide an effective approach for selective damage of breast cancer with gold nanoparticles. METHOD: Numerical method involves laser-induced Coulomb explosion of gold nanoparticles. Different parameters related to nanophotolysis such as laser fluence, tumor depth, cluster radius, laser pulse duration, and bubble formation is studied numerically. Numerical simulation was performed using Mat lab. RESULTS: The gold nanoparticles of 10, 20, 30, 40, and 50 nm in radius could penetrate into tumor 1.14, 1.155, 1.189, 1.20 and 1.22 cm in depth respectively. The maximum penetration depth in tumor could be obtained with nanoparticles of 50 nm radius. Short laser pulse of 40 ns with nanoparticles of 10 nm radius could penetrate into tumor 1.14 cm in depth. Bubbles with a radius of 9 µm could effectively kill breast cancer cells without damaging healthy ones. The bubble radius increased from 4 to 9 µm with an increase in pulse duration in the range of 10 to 30 ns. CONCLUSIONS: Gold nanoparticles with increasing radius and bubble formation for selective damage of breast cancer cells are successfully probed. The present calculated results are compared with other experimental findings, and good correlation is found between the present work and previous experimental values. It was demonstrated that bubble formation in tumor may further increase the efficacy of breast cancer treatment.
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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 | 0.000 |
| 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 | 0.000 |
| 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".