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Record W2758084306 · doi:10.1039/c7bm00730b

Self-assembly of peptide amphiphiles for drug delivery: the role of peptide primary and secondary structures

2017· review· en· W2758084306 on OpenAlexaff
Zhenhua Song, Xing Chen, Xinru You, Keqing Huang, Arvind Dhinakar, Zhipeng Gu, Jun Wu

Bibliographic record

VenueBiomaterials Science · 2017
Typereview
Languageen
FieldMaterials Science
TopicSupramolecular Self-Assembly in Materials
Canadian institutionsRegional Municipality of WaterlooUniversity of Waterloo
FundersNatural Science Foundation of Guangdong ProvinceNational Natural Science Foundation of China
KeywordsAmphiphilePeptideChemistryAlkylPrimary (astronomy)Self-assemblyDrug deliveryDrugCombinatorial chemistryStereochemistryBiochemistryOrganic chemistryPharmacologyCopolymerBiology

Abstract

fetched live from OpenAlex

Peptide amphiphiles (PAs), functionalized with alkyl chains, are capable of self-assembling into various nanostructures. Recently, PAs have been considered as ideal drug carriers due to their good biocompatibility, specific biological functions, and hypotoxicity to normal cells and tissues. Meanwhile, the nanocarriers formed by PAs are able to achieve controlled drug release and enhanced cell uptake in response to the stimulus of the physiological environment or specific biological factors in the location of the lesion. However, the underlying detailed drug delivery mechanism, especially from the aspect of primary and secondary structures of PAs, has not been systematically summarized or discussed. Focusing on the relationship between the primary and secondary structures of PAs and stimuli-responsive drug delivery applications, this review highlights the recent advances, challenges, and opportunities of PA-based functional drug nanocarriers, and their potential pharmaceutical applications are discussed.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.027
GPT teacher head0.307
Teacher spread0.279 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations107
Published2017
Admission routes1
Has abstractyes

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