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Gold Nanorods are Selective Cytotoxic Agents

2021· article· en· W3186339165 on OpenAlexaff
Mohamed A.M. El Gendy, Michael Weinfeld, Ahmed Sabry S. Abdoon

Bibliographic record

VenueAnti-Cancer Agents in Medicinal Chemistry · 2021
Typearticle
Languageen
FieldMaterials Science
TopicGold and Silver Nanoparticles Synthesis and Applications
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCytotoxic T cellHeLaCytotoxicityCell cultureCancer cellChemistryCancer researchMolecular biologyCancerCellBiologyIn vitroMedicineBiochemistryInternal medicine

Abstract

fetched live from OpenAlex

Background: Gold nanorods (GNRs) are very promising agents with multiple applications in medicine and biology. However, the cytotoxic effects of GNRs have not been fully explored. Objective: Therefore, the main objective of this study was to determine the selective cytotoxic effect of GNRs towards several human tumor cell lines. Methods: To address this issue, three sizes of GNRs (10-nm, 25-nm, and 50-nm) were tested against two human tumor cell lines, namely, human hepatoma HepG2 and human prostate PC3 cancer cells. As GNRs are usually stored in soft tissues inside living bodies, we also tested the effect of GNRs on murine splenocyte viability. To determine if the GNRs displayed selective cytotoxicity towards cancer cells, active GNRs with the size showing the least cytotoxicity to splenocytes were then tested against a panel of 11 human tumor cell lines and two human non-tumor cell lines. Results: Our results showed that the most cytotoxic size of GNRs is 10-nm, followed by the 25-nm GNRs, while the 50-nm GNRs did not show a significant effect. In addition, the 25-nm GNRs were the least cytotoxic to splenocytes when tested for 24 and 48 h. These GNRs showed a selective cytotoxic effect to prostate cancer PC3 cells with median inhibitory concentration (IC50) = 8.3 + 0.37 μM, myeloblastic leukemia HL60 cells (IC50 = 19.7 + 0.89 μM), cervical cancer HeLa cells (IC50 = 24.6 + 0.37 μM), renal adenocarcinoma 786.0 cells (IC50 = 27.34 + 0.6 μM), and hepatoma HepG2 cells (IC50 = 27.79 + 0.03 μM) when compared to the effect on the non-tumor human cells; skin fibroblast BJ cell line (IC50 = 40.13 + 0.7 μM) or epithelial breast MCF10A cells (IC50 = 33.2 + 0.89 μM). High selectivity indices (SIs) were observed in GNRs-treated PC3 and HL60 cells with values ranging from 1.69 to 4.83, whereas moderate SIs were observed in GNRs-treated HeLa, 786.0, and HepG2 cells with values ranging from 1.19 to 1.63. Other cells did not show a similar selective effect, including human laryngeal HEp2 cells, colon HCT116, metastatic renal adenocarcinoma ACHN cells, and human breast cancer cells (MCF7, MDA-MB-231, and MDA-MB-468 cells). The effect of GNRs was confirmed using the colony formation assay and the effect was found to be cell cycle-specific. Finally, it was shown that laser treatment could potentiate the cytotoxic effect of the 25-nm GNRs. Conclusion: GNRs are selective cytotoxic agents and they have the potential to act as candidate anticancer agents.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.033
GPT teacher head0.309
Teacher spread0.276 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2021
Admission routes1
Has abstractyes

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