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Record W2090772513 · doi:10.1021/ar3000585

Quantum Dot Cytotoxicity and Ways To Reduce It

2012· article· en· W2090772513 on OpenAlexafffund
Françoise M. Winnik, Dušica Maysinger

Bibliographic record

VenueAccounts of Chemical Research · 2012
Typearticle
Languageen
FieldMaterials Science
TopicCarbon and Quantum Dots Applications
Canadian institutionsMcGill UniversityUniversité de Montréal
FundersNatural Sciences and Engineering Research Council of CanadaMinistry of Education, Culture, Sports, Science and Technology
KeywordsQuantum dotNanotechnologyNanotoxicologyNanomedicinePhotobleachingNanoparticleMaterials scienceMedicineChemistryPhysicsFluorescence

Abstract

fetched live from OpenAlex

The dramatic increase in the use of nanoparticles (NP) in industry and research has raised questions about the potential toxicity of such materials. Unfortunately, not enough is known about how the novel, technologically-attractive properties of NPs correlate with the interactions that may take place at the nano/bio interface. The academic, industrial, and regulatory communities are actively seeking answers to the growing concerns on the impact of nanotechnology on humans. In this Account we adopt quantum dots (QDs) as an illustrative example of the difficulties associated with the development of a rational science-based approach to nanotoxicology. The optical properties of QDs are far superior to those of organic dyes in terms of emission and absorption bandwidths, quantum yield, and resistance to photobleaching. Moreover, QDs may be decorated with targeting moieties or drugs and, therefore, are candidates for site-specific medical imaging and for drug delivery, for example in cancer treatment. Earlier this year researchers demonstrated that QD-based imaging using monkeys caused no adverse effects although QDs accumulated in lymph nodes, bone marrow, liver, and spleen for up to 3 months after injection. Such persistence of QDs in live animals does, however, raise concerns about the safety of using QDs both in the laboratory and in the clinic. Researchers anticipate that QDs will be increasingly used not only in clinical applications but also in various manufactured products. For example, QD-solar cells have emerged as viable contenders to complement or replace dye-sensitized solar cells; CdTe/CdS thin film cells have already captured approximately 10 percent of the global market, and in addition, QDs can serve as components of sensors and as emitting materials in LEDs. Given the clear indications that QDs will inevitably become components of a wide range of manufactured and consumer products, researchers and policy makers need to understand the possible health risks associated with exposure to QDs. In this Account, we initially review the known mechanisms by which QDs can damage cells, including oxidative stress elicited by reactive oxygen species (ROS). We discuss lesser-known impairments induced in cells by nanomolar to picomolar concentrations of QDs, which imply that cadmium-containing QDs can exert genotoxic, epigenetic, and metalloestrogenic effects. These observations strongly suggest that minute concentrations of QDs could be sufficient to cause long lasting, even transgenerational, effects. We also consider various modes by which humans could be exposed to QDs in their work or through the environment. Although considerable advances have been made in enhancing the stability and overall quality of QDs, over time they can partially degrade in the environment or in biological systems, and eventually cause small, but cumulative undesirable effects. A combination of toxicological, genetic, epigenetic and imaging approaches is required to create comprehensive guidelines for evaluating the nanotoxicity of nanomaterials, including QDs. Prior to biological investigations with these materials, an indispensible step must be the full characterization of NPs by complementary techniques. Specifically, the concentration, size, charge, and ligand stability of NPs in biological media must be known if we are to understand fully how the properties of nanoparticles and of their biological environment contribute to cytotoxicity.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.002

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 head0.125
GPT teacher head0.410
Teacher spread0.285 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations323
Published2012
Admission routes2
Has abstractyes

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