Formulating Nanomedicines: Focus on Carbon Nanotubes as Novel Nanoexcipients
Bibliographic record
Abstract
Many recently designed drug delivery systems have been constructed from nano-sized components that serve as the carrier or targeting ligand for a therapeutic agent. Even though these materials have been regarded previously as inert or non-active components of dosage forms, they are now recognized as sometimes being even more important than the drug itself. Hence, it is becoming increasingly imperative that the pharmaceutically relevant properties, including identity, physicochemical characteristics, purity, solubility and toxicity, of these functional nano-excipients be fully characterized. Carbon nanotubes (CNTs) are novel nanomaterials made of carbon atoms that have wide application potential in many areas of nanomedicine. However, because of their significant potential, CNTs, as building blocks for nanomedicines, need to be characterized more fully. Studies to date indicate that both physical and chemical properties of CNTs play an important role in their interactions with cells. Therefore, a full understanding of the physical properties of CNTs, such as identity, chirality, particle size, aspect ratio, morphology and dispersion state, as well as chemical properties such as purity, defect sites and types and functional groups, will be essential to develop a full characterization panel of these versatile nanomaterials.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 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".