Optimization of Bio-Nano Interface Using Gold Nanostructures as a Model Nanoparticle System
Bibliographic record
Abstract
Better knowledge of interface between nanotechnology and biology will lead to advanced biomedical tools for imaging and therapeutics.In this review, recent progress in the understanding of how size, shape, and surface properties of nanoparticles (NPs) affect intracellular uptake, transport, and processing of NPs will be discussed.Gold NPs are used as a model system in this regard since their size, shape, and surface properties can be easily manipulated.Recent experimental and theoretical studies have shown that NP-uptake is dependent upon size and shape of the NPs.Within the size range of 2-100 nm, Gold nanoparticles (GNPs) of diameter 50 nm demonstrate the highest uptake.Cellular uptake studies of rod-shaped gold nanoparticles (GNRs) show that there is a decrease in uptake as the aspect ratio of GNRs increases.The surface ligand and charge of NPs play an important role in their uptake process as well.Different proteins on the surface of the NPs can be coated for effective targeting of NPs into specific organelles.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.Exocytosis of NPs is also dependent on the size and shape of the NPs, however, the trend was different to endocytosis process.These findings provide useful information to tailor nano-scale devices at single cell level for effective applications in diagnosis, therapeutics, and imaging.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".