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Record W2986556275 · doi:10.1021/acs.cgd.9b01457

Sterol Uptake by an Alkali-β-Cyclodextrin Metal–Organic Framework

2019· article· en· W2986556275 on OpenAlexafffund
Barry A. Blight, Towseef I. Ahmad, Helena J. Shepherd, Christopher S. Jennings, Livia I. Ferland, Simon J. Teat, Jeremy S. Rossman

Bibliographic record

VenueCrystal Growth & Design · 2019
Typearticle
Languageen
FieldChemistry
TopicMetal-Organic Frameworks: Synthesis and Applications
Canadian institutionsUniversity of New Brunswick
FundersMedical Research CouncilUniversity of KentNew Brunswick Innovation FoundationUniversity of New Brunswick
KeywordsCyclodextrinChemistryBilayerAlkali metalMembraneSterolPotassium hydroxideCrystallizationSubstrate (aquarium)Lipid bilayerConfocal microscopySelf-assemblyCholesterolBeta-CyclodextrinsPotassiumChemical engineeringCrystallographyOrganic chemistryBiochemistry

Abstract

fetched live from OpenAlex

β - Cyclodextrin is well-known in cellular biology for its ability to moderate cholesterol levels in lipid bilayer membranes. Its use in extended network solids remains elusive due to the low symmetry of this macrocyclic system. Self-assembly of two different β - cyclodextrin metal–organic frameworks (MOFs) with extended nanotube structures is achieved by crystallization with excess potassium hydroxide, one in the presence of cholesterol. We then further demonstrate the proclivity of one of these MOFs to absorb cholesterol and two other sterols from solution using NMR and confocal microscopy techniques. This work demonstrates that these network solids show great potential in both substrate delivery and/or extraction.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.018
GPT teacher head0.236
Teacher spread0.218 · 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 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

Citations23
Published2019
Admission routes2
Has abstractyes

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