Evidence map›Paper›PMID 36403088›Full record

ArticleBiophysical journal2023

Elucidating lipid conformations in the ripple phase: Machine learning reveals four lipid populations.

Matthew Davies, A D Reyes-Figueroa, Andrey A Gurtovenko, Daniel Frankel, Mikko Karttunen

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Biophysical journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. On an RNA-Membrane Protogenome.Life (Basel, Switzerland) · 2025
    Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Matthew DaviesSchool of Engineering, Newcastle University, Newcastle, United Kingdom.
A D Reyes-FigueroaDepartment of Chemistry, The University of Western Ontario, London, Ontario, Canada; The Centre of Advanced Materials and Biomaterials Research, The University of Western Ontario, London, Ontario, Canada; Centro de Investigación en Matemáticas Unidad Monterrey, Apodaca, Nuevo León, México; Consejo Nacional de Ciencia y Tecnología, Benito Juárez, Ciudad de México, Mexico.
Andrey A GurtovenkoInstitute of Macromolecular Compounds, Russian Academy of Sciences, St. Petersburg, Russia; Faculty of Physics, St. Petersburg State University, St. Petersburg, Russia.
Daniel FrankelSchool of Engineering, Newcastle University, Newcastle, United Kingdom.
Mikko KarttunenDepartment of Chemistry, The University of Western Ontario, London, Ontario, Canada; The Centre of Advanced Materials and Biomaterials Research, The University of Western Ontario, London, Ontario, Canada; Department of Physics and Astronomy, The University of Western Ontario, London, Ontario, Canada. Electronic address: mkarttu@uwo.ca.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A new mixed radial-angular, three-particle correlation function method in combination with unsupervised machine learning was applied to examine the emergence of the ripple phase in dipalmitoylphosphatidylcholine (DPPC) lipid bilayers using data from atomistic molecular dynamics simulations of system sizes ranging from 128 to 4096 lipids. Based on the acyl tail conformations, the analysis revealed the presence of four distinct conformational populations of lipids in the ripple phases of the DPPC lipid bilayers. The expected gel-like (ordered; L

Indexed as

Lipid BilayersMolecular Dynamics SimulationCell MembraneMachine LearningMolecular ConformationLipid Bilayers

Identifiers

PMID36403088
PMCPMC9892614

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.