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Record W2587983445 · doi:10.1039/c6cp08104e

Partitioning of caffeine in lipid bilayers reduces membrane fluidity and increases membrane thickness

2017· article· en· W2587983445 on OpenAlexafffund
Adree Khondker, Alexander Dhaliwal, Richard J. Alsop, Jennifer Tang, Matilda Backholm, An‐Chang Shi, Maikel C. Rheinstädter

Bibliographic record

VenuePhysical Chemistry Chemical Physics · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLipid Membrane Structure and Behavior
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaOntario Ministry of Economic Development and InnovationCanada Foundation for InnovationMcMaster University
KeywordsMembraneCaffeineMembrane fluidityThickeningLipid bilayerBiophysicsChemistryLipid accumulationBiochemistryBiologyEndocrinology

Abstract

fetched live from OpenAlex

Caffeine is a small amphiphilic molecule, which is widely consumed as a stimulant to prevent fatigue, but is also used as a common drug adjuvant in modern medicine. Here, we show that caffeine interacts with unsaturated lipid membranes made of 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC). By combining X-ray diffraction and molecular dynamics simulations, we present evidence that caffeine partitions in lipid membranes and locates at the head group-tail group interface of the bilayers. By attracting water molecules from neighboring lipid molecules, it leads to the formation of "water pockets", i.e., a local increase of water density at this interface. Through this mechanism, caffeine leads to an overall decrease of the gauche defect density in the membranes and an increase of membrane thickness, indicating a loss of membrane fluidity. These non-specific membrane interactions may increase the efficacy of analgesic drugs through changes in the bioavailability and rate of metabolism of these drugs.

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 categoriesMeta-epidemiology (narrow)
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 score0.001
Threshold uncertainty score1.000

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.014
GPT teacher head0.269
Teacher spread0.255 · 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

Citations38
Published2017
Admission routes2
Has abstractyes

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