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Record W2412976347 · doi:10.1139/cjc-2016-0068

Polymers made of bile acids: from soft to hard biomaterials

2016· article· en· W2412976347 on OpenAlexafffundvenue
Alexander J. Cunningham, X. X. Zhu

Bibliographic record

VenueCanadian Journal of Chemistry · 2016
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicAdvanced Drug Delivery Systems
Canadian institutionsUniversité de MontréalUniversité du Québec à Montréal
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsSoft materialsElastomerPolymerChemistryNanotechnologyBiocompatible materialMoleculeIntermolecular forceDrug deliveryCovalent bondPolymer scienceCombinatorial chemistryOrganic chemistryMaterials scienceBiomedical engineering

Abstract

fetched live from OpenAlex

Bile acids are gaining increasing importance as building blocks in the development of novel polymeric materials. This is evidenced by the growing number of publications advocating the advantages of their incorporation in the design and construction of materials. Composed of a rigid steroid backbone, functional groups with potential towards diverse reactions, and a biocompatible framework, there are various ways in which these molecules can be utilized to afford biomaterials via distinct architectures. Soft materials utilize the intrinsic capacity of bile acids to self-assemble and have seen a range of applications, most notably in the field of drug delivery. On the other hand, there is also the possibility of including bile acids in the polymer backbone, which has been used in the preparation of elastomers. This review discusses a selection of materials that can be prepared using bile acids and the advantages afforded by these molecules. Focus will be on the development of soft and hard materials, where soft materials are described as being held by weak intermolecular interactions, whereas hard materials are mechanically stronger with bile acids covalently incorporated in the polymer network.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.158
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.000

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 head0.058
GPT teacher head0.350
Teacher spread0.292 · 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 teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations14
Published2016
Admission routes3
Has abstractyes

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