Investigation of Sub-100 nm Gold Nanoparticles for Laser-Induced Thermotherapy of Cancer
Bibliographic record
Abstract
Specialized gold nanostructures are of interest for the development of alternative treatment methods in medicine. Photothermal therapy combined with gene therapy that supports hyperthermia is proposed as a novel multimodal treatment method for prostate cancer. In this work, photothermal therapy using small (<100 nm) gold nanoparticles and near-infrared (NIR) laser irradiation combined with gene therapy targeting heat shock protein (HSP) 27 was investigated. A series of nanoparticles: nanoshells, nanorods, core-corona nanoparticles and hollow nanoshells, were synthesized and examined to compare their properties and suitability as photothermal agents. In vitro cellular uptake studies of the nanoparticles into prostate cancer cell lines were performed using light scattering microscopy to provide three-dimensional (3D) imaging. Small gold nanoshells (40 nm) displayed the greatest cellular uptake of the nanoparticles studied and were used in photothermal studies. Photothermal treatment of the cancer cell lines with laser irradiation at 800 nm at 4 W on a spot size of 4 mm (FWHM) for 6 or 10 min resulted in an increase in temperature of ~12 °C and decrease in cell viability of up to 70%. However, in vitro studies combining photothermal therapy with gene therapy targeting HSP27 did not result in additional sensitization of the prostate cancer cells to hyperthermia.
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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) | 0.000 | 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".