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Record W2792803144 · doi:10.3389/fphar.2018.00202

Poly(Ethylene Glycol)–Polylactide Micelles for Cancer Therapy

2018· review· en· W2792803144 on OpenAlex

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

VenueFrontiers in Pharmacology · 2018
Typereview
Languageen
FieldMaterials Science
TopicNanoparticle-Based Drug Delivery
Canadian institutionsUniversity of Waterloo
FundersNational Natural Science Foundation of China
KeywordsNanocarriersMicelleEthylene glycolDrug deliveryPEG ratioNanotechnologyChemistryCancerMaterials scienceMedicineOrganic chemistryInternal medicine

Abstract

fetched live from OpenAlex

For the treatment of malignancy, many therapeutic agents, including small molecules, photosensitizers, immunomodulators, proteins and genes, and so forth, have been loaded into nanocarriers for controllable cancer therapy. Among these nanocarriers, polymeric micelles have been considered as one of the most promising nanocarriers, some of which have already been applied in different stages of clinical trials. The successful advantages of polymeric micelles from bench to bedside are due to their special core/shell structures, which can carry specific drugs in certain disease conditions. Particularly, poly(ethylene glycol)-polylactide (PEG-PLA) micelles have been considered as one of the most promising platforms for drug delivery. The PEG shell effectively prevents the adsorption of proteins and phagocytes, thereby evidently extending the blood circulation period. Meanwhile, the hydrophobic PLA core can effectively encapsulate many therapeutic agents. This review summarizes recent advances in PEG-PLA micelles for the treatment of malignancy. In addition, future perspectives for the development of PEG-PLA micelles as drug delivery systems are also presented.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.636
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.043
GPT teacher head0.364
Teacher spread0.321 · 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