MétaCan
Menu
Back to cohort
Record W3179294233 · doi:10.3390/nano11071771

Current Developments in Native Nanometric Discoidal Membrane Bilayer Formed by Amphipathic Polymers

2021· review· en· W3179294233 on OpenAlexaff
Mansoore Esmaili, Mohamed A. Eldeeb, Ali Akbar Moosavi‐Movahedi

Bibliographic record

VenueNanomaterials · 2021
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLipid Membrane Structure and Behavior
Canadian institutionsMcGill UniversityMontreal Neurological Institute and HospitalUniversity of Alberta
Fundersnot available
KeywordsModel lipid bilayerLipid bilayerMembraneAmphiphileBiophysicsBilayerPolymerNanotechnologyMembrane proteinVesicleChemistryMaterials scienceBiochemistryCopolymerBiologyLipid bilayer phase behaviorOrganic chemistry

Abstract

fetched live from OpenAlex

Unlike cytosolic proteins, membrane proteins (MPs) are embedded within the plasma membrane and the lipid bilayer of intracellular organelles. MPs serve in various cellular processes and account for over 65% of the current drug targets. The development of membrane mimetic systems such as bicelles, short synthetic polymers or amphipols, and membrane scaffold proteins (MSP)-based nanodiscs has facilitated the accommodation of synthetic lipids to stabilize MPs, yet the preparation of these membrane mimetics remains detergent-dependent. Bio-inspired synthetic polymers present an invaluable tool for excision and liberation of superstructures of MPs and their surrounding annular lipid bilayer in the nanometric discoidal assemblies. In this article, we discuss the significance of self-assembling process in design of biomimetic systems, review development of multiple series of amphipathic polymers and the significance of these polymeric "belts" in biomedical research in particular in unraveling the structures, dynamics and functions of several high-value membrane protein targets.

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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.031
GPT teacher head0.335
Teacher spread0.304 · 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

Citations7
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueNanomaterialsSame topicLipid Membrane Structure and BehaviorFrench-language works237,207