Evidence map›Paper›PMID 30686489›Full record

ArticleBiophysical journal2019

Core-Shell Modeling of Light Scattering by Vesicles: Effect of Size, Contents, and Lamellarity.

Anna Wang, Christopher Chan Miller, Jack W Szostak

Abstract read
In one paragraph

Article in Biophysical journal, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 citing papers in PubMed.

  1. Reversible Redox-Responsive Peptide Condensates With RNA.Chembiochem : a European journal of chemical biology · 2026
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  14. Prebiotic amino acids bind to and stabilize prebiotic fatty acid membranes.Proceedings of the National Academy of Sciences of the United States of America · 2019
    Article
  15. Review
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

3 authors.

Anna WangDepartment of Molecular Biology and Center for Computational and Integrative Biology, Massachusetts General Hospital, Boston, Massachusetts. Electronic address: anna.wang@unsw.edu.au.
Christopher Chan MillerAtomic and Molecular Physics Division, Harvard Smithsonian Center for Astrophysics, Cambridge, Massachusetts.
Jack W SzostakDepartment of Molecular Biology and Center for Computational and Integrative Biology, Massachusetts General Hospital, Boston, Massachusetts. Electronic address: szostak@molbio.mgh.harvard.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Having a fast, reliable method for characterizing vesicles is vital for their use as model cell membranes in biophysics, synthetic biology, and origins of life studies. Instead of the traditionally used Rayleigh-Gans-Debye approximation, we use an exact extended Lorenz-Mie solution for how core-shell particles scatter light to model vesicle turbidity. This approach enables accurate interpretations of simple turbidimetric measurements and is able to accurately model highly scattering vesicles, such as larger vesicles, those with multiple layers, and those with encapsulated material. We uncover several surprising features, including that vesicle lamellarity has a larger effect on sample turbidity than vesicle size and that the technique can be used to measure the membrane thickness of vesicles. We also examine potential misinterpretations of turbidimetry and discuss when measurements are limited by forward and multiple scattering and by the geometry of the instrument.

Indexed as

LightModels, TheoreticalScattering, RadiationLipid BilayersOleic AcidPhosphatidylcholines1-palmitoyl-2-oleoylphosphatidylcholineLipid BilayersOleic AcidPhosphatidylcholines

Identifiers

PMID30686489
PMCPMC6382849

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.