Comparison of Four Common Gold Nanoparticles for Photothermal Cancer Therapy: A Review
Bibliographic record
Abstract
Current cancer treatment options, including surgery, chemotherapy and radiation therapy, often cause damage to healthy tissue and reduce a patient's quality of life with well-known side effects, such as pain, infection and nerve damage. Recent research has shown that gold nanoparticles used as photothermal agents in photothermal therapy may pose as an alternative to traditional treatments. This great potential is due to their ability to selectively accumulate in cancerous tissue, efficiently absorb near-infrared light, and kill cancerous tissue without harming surrounding cells. Gold nanoparticles show promise in increasing treatment efficacy and reducing the side effects associated with cancer therapy. While recent studies show the potential of gold nanoparticles, the existing literature is limited in drawing comparisons between studies and practical use for photothermal therapy. This paper reviews notable studies on four common gold nanoparticles used in the therapeutic treatment of cancer: gold nanocages, gold nanospheres, gold nanorods, and gold nanoshells. By comparing key characteristics of the particles’, including their synthesis, toxicity, absorption spectrum, and selective photothermal lethality, gold nanospheres can be recommended for use in photothermal therapy. Although forms of each gold nanoparticle were found to have a low toxicity, gold nanospheres can be rapidly synthesized and appear to exceed in selective photothermal lethality and immature tumour accumulation. Due to these advantages in using gold nanospheres for photothermal therapy, cancer could be treated more effectively and improve patient prognosis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".