Nanodiamonds as novel nanomaterials for biomedical applications: drug delivery and imaging systems
Bibliographic record
Abstract
Detonation nanodiamonds (NDs) are emerging as delivery vehicles for small chemical drugs and macromolecular biotechnology products due to their primary particle size of 4 to 5 nm, stable inert core, reactive surface, and ability to form hydrogels. Nanoprobe technology capitalizes on the intrinsic fluorescence, high refractive index, and unique Raman signal of the NDs, rendering them attractive for in vitro and in vivo imaging applications. This review provides a brief introduction of the various types of NDs and describes the development of procedures that have led to stable single-digit-sized ND dispersions, a crucial feature for drug delivery systems and nanoprobes. Various approaches used for functionalizing the surface of NDs are highlighted, along with a discussion of their biocompatibility status. The utilization of NDs to provide sustained release and improve the dispersion of hydrophobic molecules, of which chemotherapeutic drugs are the most investigated, is described. The prospects of improving the intracellular delivery of nucleic acids by using NDs as a platform are exemplified. The photoluminescent and optical scattering properties of NDs, together with their applications in cellular labeling, are also reviewed. Considering the progress that has been made in understanding the properties of NDs, they can be envisioned as highly efficient drug delivery and imaging biomaterials for use in animals and humans.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".