Physicochemical, pharmaceutical and biological approaches toward designing optimized and efficient hydrophobically modified chitosan-based polymeric micelles as a nanocarrier system for targeted delivery of anticancer drugs
Bibliographic record
Abstract
Hydrophobically modified chitosan-based polymeric micelles (CBPMs) are formed through self-aggregation of chitosan amphiphilic derivatives. Their core-shell structure, diversity and the fact that all of their properties are adjustable through reconciling the interactions among their three main constituents: chitosan, hydrophilic segment and hydrophobic segment as well as with the outside medium through changing the ratio and chemical structure of each component's, chemical structure distinguish them from other chitosan-based drug delivery systems (DDSs) and give rise to these promising candidates for targeted delivery of lipophilic anticancer drugs. The majority of review articles conducted previously on chitosan-based DDSs have only made simple differential comparisons between such systems and the anticancer drugs that have been delivered through them. In this review article, all the basic properties of CBPMs including physicochemical, pharmaceutical and biological properties are technically detailed and discussed. The intention of this article is to outline and discuss salient features of CBPMs to contribute to the understanding of optimized strategies for the design of stable and efficient CBPMs.
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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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".