Differing Affinities of Gold Nanostars and Nanospheres toward HeLa and HepG2 Cells: Implications for Cancer Therapy
Bibliographic record
Abstract
Gold nanoparticles have potential applications for the diagnosis and treatment of diseases due to their optical, sensing, and biological properties. Gold nanoparticles can be used as nanocarriers for the delivery of therapeutic agents or as nanoprobes to detect and monitor intracellular events. Studying their localization and properties in cells is an essential step toward developing nanoparticle products for in vivo use. Raman spectroscopy is a powerful and noninvasive method that we used to investigate how two different morphologies of gold particles, namely, nanostars and nanospheres, interact with cervical cancer cells (HeLa) and liver cancer cells (HepG2). Gold nanoparticles with branched structures are more effective in enhancing the Raman spectra for cell-relevant bands compared to nanospheres. Moreover, we observed a higher level of Raman enhancement of nanostars and nanospheres in HeLa cells compared to HepG2 cells, suggesting HeLa could uptake a higher level of both types under the same condition. We also used scanning electron microscopy and light microscopy to study the distribution of both types of nanoparticles in cells. Our results highlight the importance of nanomorphology in mediating changes in affinity of gold nanoparticles to different chemical structures in cells, which is important for developing nanomedicines for cancer therapy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".