Nanoparticle heterogeneity: an emerging structural parameter influencing particle fate in biological media?
Bibliographic record
Abstract
Drug nanocarriers' surface chemistry is often presumed to be uniform. For instance, the polymer surface coverage and distribution of ligands on nanoparticles are described with averaged values obtained from quantification techniques based on particle populations. However, these averaged values may conceal heterogeneities at different levels, either because of the presence of particle sub-populations or because of surface inhomogeneities, such as patchy surfaces on individual particles. The characterization and quantification of chemical surface heterogeneities are tedious tasks, which are rather limited by the currently available instruments and research protocols. However, heterogeneities may contribute to some non-linear effects observed during the nanoformulation optimization process, cause problems related to nanocarrier production scale-up and correlate with unexpected biological outcomes. On the other hand, heterogeneities, while usually unintended and detrimental to nanocarrier performance, may, in some cases, be sought as adjustable properties that provide NPs with unique functionality. In this review, results and processes related to this issue are compiled, and perspectives and possible analytical developments are discussed.
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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 | 0.001 |
| 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 | 0.001 |
| 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".