Pillararene‐Based Supramolecular Vesicles for Stimuli‐Responsive Drug Delivery
Bibliographic record
Abstract
Supramolecular vesicles (SMVs) self-assembled from the supra-amphiphiles, consisting of two scaffolds linked together through noncovalent interactions, can realize stimuli-responsive controlled release of encapsulated drugs for enhanced therapeutic efficacy and minimized side effect of drugs. Pillararenes (PAs), an emerging kind of macrocyclic hosts in 2008, are easy to modify with a variety of functionalities. SMVs from PAs and specific guests mainly based on the host-guest interactions have attracted increasing attention because of their drug delivery and controlled drug release. A great progress in the construction and stimuli-responsive drug delivery of the PA-based SMVs has been made since the first work was reported in 2012. This review summarizes the major achievements of the PA-based SMVs for stimuli-responsive drug delivery over the past 5 years, including the microstructures of SMVs, multiple stimuli-responsive SMVs, prodrug SMVs from prodrug PAs and guests, bola-type SMVs, multifunctional SMVs, glucose-responsive SMVs for insulin delivery, novel SMVs from responsive PAs, thermo-responsive SMVs, and ternary SMVs, for chemotherapy, photothermal therapy, photodynamic therapy, and other biological applications. The future challenges and research directions of PA-based SMVs are also outlined from the points of views of the fundamental research, biological applications, and clinical applications of PA-based SMVs.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".