Engineering multifunctional nanoparticles: all‐in‐one versus one‐for‐all
Bibliographic record
Abstract
Abstract Multifunctional nanoparticles have been developed to overcome the conventional hurdles associated with the diagnosis and treatment of disease. However, there are often caveats involved with the development and clinical translation of multifunctional nanoparticles largely regarding the notion that additional functionality increases nanoparticle complexity. Here, we discuss two design concepts, a conventional approach, ‘all‐in‐one’, and introduce the concept of ‘one‐for‐all’ to suggest that multifunctionality does not necessarily result in multicomponent complex nanoparticles. This review focuses on the design concepts of all‐in‐one and one‐for‐all with examples of each approach and a discussion on the implications for clinical translation. WIREs Nanomed Nanobiotechnol 2013, 5:250–265. doi: 10.1002/wnan.1217 This article is categorized under: Therapeutic Approaches and Drug Discovery > Emerging Technologies Diagnostic Tools > In Vivo Nanodiagnostics and Imaging Therapeutic Approaches and Drug Discovery > Nanomedicine for Oncologic Disease
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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 | 0.001 |
| 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".