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Record W4291606177 · doi:10.1002/chem.202202050

Pillararene‐Based Supramolecular Vesicles for Stimuli‐Responsive Drug Delivery

2022· review· en· W4291606177 on OpenAlex
Chen Wang, Hang Li, Jiangtao Dong, Yuxia Chen, Xingkun Luan, Xiaona Li, Xuezhong Du

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueChemistry - A European Journal · 2022
Typereview
Languageen
FieldChemistry
TopicSupramolecular Chemistry and Complexes
Canadian institutionsMinistry of Education and Child Care
FundersNational Natural Science Foundation of China
KeywordsDrug deliveryVesicleSupramolecular chemistryNanotechnologyDrugChemistryMedicineMaterials sciencePharmacologyBiochemistryCrystallography

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.951
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0030.001
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0120.000

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.

Opus teacher head0.067
GPT teacher head0.295
Teacher spread0.228 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it