Intracellular uptake, transport, and processing of gold nanostructures
Bibliographic record
Abstract
The emerging field of nanomedicine requires better understanding of the interface between nanotechnology and medicine. Better knowledge of the nano-bio interface will lead to better tools for diagnostic imaging and therapy. In this review, recent progress in understanding of how size, shape, and surface properties of nanoparticles (NPs) affect intracellular fate of NPs is discussed. Gold nanostructures are used as a model system in this regard since their physical and chemical properties can be easily manipulated. The NP-uptake is dependent on the physiochemical properties, and once in the cell, most of the NPs are trafficked via an endo-lysosomal path followed by a receptor-mediated endocytosis process at the cell membrane. Within the size range of 2-100 nm, Gold nanoparticles (GNPs) of diameter 50 nm demonstrate the highest uptake. Cellular uptake studies of gold nanorods (GNRs) show that there is a decrease in uptake as the aspect ratio of GNRs increases. Theoretical models support the size- and shape-dependent NP-uptake. The intracellular transport of targeted NPs is faster than untargeted NPs. The surface ligand and charge of NPs play a bigger role in their uptake, transport, and organelle distribution. Exocytosis of NPs is dependent on size and shape as well; however, the trend is different compared to endocytosis. GNPs are now being incorporated into polymer and lipid based NPs to build multifunctional devices. A multifunctional platform based on gold nanostructures, with multimodal imaging, targeting, and therapeutics; hold the possibility of promising directions in medical research.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".