Evidence map›Paper›PMID 38939901›Full record

ArticleJournal of extracellular biology2024

Differentiation of large extracellular vesicles in oral fluid: Combined protocol of small force centrifugation and sedimentation pattern analysis.

Takamasa Kawano, Kohji Okamura, Hiroki Shinchi, Koji Ueda, Takeshi Nomura, Kiyotaka Shiba

Erratum issuedAbstract read
In one paragraph

Article in Journal of extracellular biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Takamasa KawanoDivision of Protein Engineering, Cancer Institute Japanese Foundation for Cancer Research Koto-ku Tokyo Japan.
Kohji OkamuraDepartment of Systems BioMedicine National Center for Child Health and Development Setagaya-ku Tokyo Japan.
Hiroki ShinchiCancer Precision Medicine Center Japanese Foundation for Cancer Research Koto-ku Tokyo Japan.
Koji UedaCancer Precision Medicine Center Japanese Foundation for Cancer Research Koto-ku Tokyo Japan.
Takeshi NomuraDepartment of Oral Oncology Oral and Maxillofacial Surgery, Tokyo Dental College Ichikawa Chiba Japan.
Kiyotaka ShibaDivision of Protein Engineering, Cancer Institute Japanese Foundation for Cancer Research Koto-ku Tokyo Japan.ORCID https://orcid.org/0000-0001-6459-0204

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Extracellular vesicles (EVs) in biofluids are highly heterogeneous entities in terms of their origins and physicochemical properties. Considering the application of EVs in diagnostic and therapeutic fields, it is of extreme importance to establish differentiating methods by which focused EV subclasses are operationally defined. Several differentiation protocols have been proposed; however, they have mainly focused on smaller types of EVs, and the heterogeneous nature of large EVs has not yet been fully explored. In this report, to classify large EVs into subgroups based on their physicochemical properties, we have developed a protocol, named EV differentiation by sedimentation patterns (ESP), in which entities in the crude large EV fraction are first moved through a density gradient of iodixanol with small centrifugation forces, and then the migration patterns of molecules through the gradients are analysed using a non-hierarchical data clustering algorithm. Based on this method, proteins in the large EV fractions of oral fluids clustered into three groups: proteins shared with small EV cargos and enriched in immuno-related proteins (Group 1), proteins involved in energy metabolism and protein synthesis (Group 2), and proteins required for vesicle trafficking (Group 3). These observations indicate that the physiochemical properties of EVs, which are defined through low-speed gradient centrifugation, are well associated with their functions within cells. This protocol enables the detailed subclassification of EV populations that are difficult to differentiate using conventional separation methods.

Indexed as

EVslarge EVssalivasubclassification

Identifiers

PMID38939901
PMCPMC11080912

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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.