Fluorescence nanoparticles “quantum dots” as drug delivery system and their toxicity: a review
Bibliographic record
Abstract
Fluorescence nanocrystals or quantum dots (QDs) are engineered nanoparticles (NP) that have shown great promise with potential for many biological and biomedical applications, especially in drug delivery/activation and cellular imaging. The use of nanotechnology in medicine directed to drug delivery is set to expand in the coming years. However, it is unclear whether QDs, which are defined as NPs rather than small molecules, can specifically and effectively deliver drugs to molecular targets at subcellular levels. When QDs are linked to suitable ligands that are site specific, it has been shown to be brighter and photostable when compared with organic dyes. Interestingly, pharmaceutical sciences are exploiting NPs to minimize toxicity and undesirable side effects of drugs. The unforeseen hazardous properties of the carrier NPs themselves have given rise to some concern in a clinical setting. The kind of hazards encountered with this new nanotechnology materials are complex compared with conventional limitations created by traditional delivery systems. The development of cadmium-derived QDs shows great potential for treatment and diagnosis of cancer and site-directed delivery by virtue of their size-tunable fluorescence and with highly customizable surface for directing their bioactivity and targeting. However, data regarding the pharmacokinetic and toxicology studies require further investigation and development, and it poses great difficulties to ascertain the risks associated with this new technology. Additionally, nanotechnology also displays yet another inherent risk for toxic cadmium, which will enter as a new form of hazard in the biomedical field. This review will look at cadmium-derived QDs and discuss their future and their possible toxicities in a disease situation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".