Effect of Physico-Chemical Properties of Nanoparticles on Their Intracellular Uptake
Bibliographic record
Abstract
Cellular internalization of inorganic, lipidic and polymeric nanoparticles is of great significance in the quest to develop effective formulations for the treatment of high morbidity rate diseases. Understanding nanoparticle-cell interactions plays a key role in therapeutic interventions, and it continues to be a topic of great interest to both chemists and biologists. The mechanistic evaluation of cellular uptake is quite complex and is continuously being aided by the design of nanocarriers with desired physico-chemical properties. The progress in biomedicine, including enhancing the rate of uptake by the cells, is being made through the development of structure-property relationships in nanoparticles. We summarize here investigations related to transport pathways through active and passive mechanisms, and the role played by physico-chemical properties of nanoparticles, including size, geometry or shape, core-corona structure, surface chemistry, ligand binding and mechanical effects, in influencing intracellular delivery. It is becoming clear that designing nanoparticles with specific surface composition, and engineered physical and mechanical characteristics, can facilitate their internalization more efficiently into the targeted cells, as well as enhance the rate of cellular uptake.
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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 | 0.000 |
| 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".