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Enregistrement W2090772513 · doi:10.1021/ar3000585

Quantum Dot Cytotoxicity and Ways To Reduce It

2012· article· en· W2090772513 sur OpenAlex

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Notice bibliographique

RevueAccounts of Chemical Research · 2012
Typearticle
Langueen
DomaineMaterials Science
ThématiqueCarbon and Quantum Dots Applications
Établissements canadiensMcGill UniversityUniversité de Montréal
Organismes subventionnairesNatural Sciences and Engineering Research Council of CanadaMinistry of Education, Culture, Sports, Science and Technology
Mots-clésQuantum dotNanotechnologyNanotoxicologyNanomedicinePhotobleachingNanoparticleMaterials scienceMedicineChemistryPhysicsFluorescence

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,023
Score d'incertitude au seuil0,523

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,125
Tête enseignante GPT0,410
Écart entre enseignants0,285 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle