Miktoarm Star Polymers: Branched Architectures in Drug Delivery
Bibliographic record
Abstract
Delivering active pharmaceutical agents to disease sites using soft polymeric nanoparticles continues to be a topical area of research. It is becoming increasingly evident that the composition of amphiphilic macromolecules plays a significant role in developing efficient nanoformulations. Branched architectures with asymmetric polymeric arms emanating from a central core junction have provided a pivotal venue to tailor their key parameters. The build-up of miktoarm stars offers vast polymer arm tunability, aiding in the development of macromolecules with adjustable properties, and allows facile inclusion of endogenous stimulus-responsive entities. Miktoarm star-based micelles have been demonstrated to exhibit denser coronae, very low critical micelle concentrations, high drug loading contents, and sustained drug release profiles. With significant advances in chemical methodologies, synthetic articulation of miktoarm polymer architecture, and determination of their structure-property relationships, are now becoming streamlined. This is helping advance their implementation into formulating efficient therapeutic interventions. This review brings into focus the important discoveries in the syntheses of miktoarm stars of varied compositions, their aqueous self-assembly, and contributions their formulations are making in advancing the field of drug delivery.
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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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".