Quantum Dot Cytotoxicity and Ways To Reduce It
Notice bibliographique
Résumé
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.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,002 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».