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Record W6983539897

MRI as a Complement to Solid-State NMR for Lateral Lipid Diffusion

2020· article· en· W6983539897 on OpenAlexaff

Bibliographic record

VenueScholarship at UWindsor (University of Windsor) · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLipid Membrane Structure and Behavior
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsDiffusionLipid bilayerLiposomeMoleculeDiffusion MRIMembranePhosphatidylcholineLateral diffusion
DOInot available

Abstract

fetched live from OpenAlex

Lipid membranes are an integral part of the human body; allowing for cell structure and stability, they are extremely difficult to study due to their complex organization and size. Researchers commonly study these structures by utilizing liposomes in their place. As such, a variety of techniques exist to study these complex structures in-depth. Solid-state nuclear magnetic resonance (NMR) is the current tool most commonly used to measure lateral lipid diffusion. As more information is gathered from orthogonal techniques, more accurate and extensive simulations are produced to understand different interactions and properties of membranes, which can be applied to more complex cellular systems.\nDiffusion Magnetic Resonance Imaging (MRI) is a new technique being adapted to study the random motion of hydrogen nuclei in molecules. Fast and slow-moving particles can be differentiated according to their diffusion coefficients which correlate to the of these different sized molecules in space. In this experiment, high diffusion numbers correspond to faster molecular movement. In a complex structure consisting of various molecular species, identification of known values, such as water, allows for easier identification of interest lipids in samples. The diffusion coefficient provides information that allows scientists to calculate the rate of movement of a particle, particularly with the help of the modified Einstein relation. This technique can be used to simulate the lateral movement of lipids along the plane of the membrane, and define how they behave in more complex environments.\nUsing a homogenized mixture of phosphatidylcholine lipids from sunflowers and water, lipid movement was studied using a series of diffusion MRI experiments. The sensitivity to specific lipid diffusion was increased with each experiment to refine instrument parameters. The data was fitted to a biexponential curve which allowed for the separation of the slow and fast diffusion coefficients in 3 axes: x, y and z. Using information from the initial trials, a T2-Diffusion Correlation map was generated and allowed for more accurate results of other samples tested later on. As well, a sample was tested on a supported lipid bilayer, to produce a greater signal by limiting the planes the lipid could move in.\nThe information obtained was similar to previous measurement attempts using alternative methods and reinforces the current value of applying MRI as a novel method to study lipid dynamics in both free-floating liposomes and supported bilayers. With more testing, MRI may show promise to complement solid-state NMR and aid in better understanding these complex systems.

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)0.002
metaresearch head score (Gemma)0.002
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: none
Teacher disagreement score0.009
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.003
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0090.005

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 head0.019
GPT teacher head0.256
Teacher spread0.237 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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