Dye-doped silica nanoparticles: synthesis, surface chemistry and bioapplications
Bibliographic record
Abstract
Abstract Background Fluorescent silica nanoparticles have been extensively utilised in a broad range of biological applications and are facilitated by their predictable, well-understood, flexible chemistry and apparent biocompatibility. The ability to couple various siloxane precursors with fluorescent dyes and to be subsequently incorporated into silica nanoparticles has made it possible to engineer these fluorophores-doped nanomaterials to specific optical requirements in biological experimentation. Consequently, this class of nanomaterial has been used in applications across immunodiagnostics, drug delivery and human-trial bioimaging in cancer research. Main body This review summarises the state-of-the-art of the use of dye-doped silica nanoparticles in bioapplications and firstly accounts for the common nanoparticle synthesis methods, surface modification approaches and different bioconjugation strategies employed to generate biomolecule-coated nanoparticles. The use of dye-doped silica nanoparticles in immunoassays/biosensing, bioimaging and drug delivery is then provided and possible future directions in the field are highlighted. Other non-cancer-related applications involving silica nanoparticles are also briefly discussed. Importantly, the impact of how the protein corona has changed our understanding of NP interactions with biological systems is described, as well as demonstrations of its capacity to be favourably manipulated. Conclusions Dye-doped silica nanoparticles have found success in the immunodiagnostics domain and have also shown promise as bioimaging agents in human clinical trials. Their use in cancer delivery has been restricted to murine models, as has been the case for the vast majority of nanomaterials intended for cancer therapy. This is hampered by the need for more human-like disease models and the lack of standardisation towards assessing nanoparticle toxicity. However, developments in the manipulation of the protein corona have improved the understanding of fundamental bio–nano interactions, and will undoubtedly assist in the translation of silica nanoparticles for disease treatment to the clinic.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".