Evidence map›Paper›PMID 37852942›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2023

Metallization of Targeted Protein Assemblies in Cell-Derived Extracellular Matrix by Antibody-Guided Biotemplating.

Chang Woo Song, Jaewan Ahn, Insung Yong, Nakhyun Kim, Chan E Park, Sein Kim, Sung-Yoon Chung, Pilnam Kim, Il-Doo Kim, Jae-Byum Chang

Abstract read
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

10 authors.

Chang Woo SongDepartment of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Daejeon, 34141, Republic of Korea.ORCID 0000-0002-3596-3291
Jaewan AhnDepartment of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Daejeon, 34141, Republic of Korea.ORCID 0000-0002-0136-8427
Insung YongDepartment of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Daejeon, 34141, Republic of Korea.
Nakhyun KimDepartment of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Daejeon, 34141, Republic of Korea.
Chan E ParkDepartment of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Daejeon, 34141, Republic of Korea.
Sein KimDepartment of Biomedical Engineering, Sungkyunkwan University (SKKU), Suwon, 16419, Republic of Korea.
Sung-Yoon ChungDepartment of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Daejeon, 34141, Republic of Korea.ORCID 0000-0002-2260-6201
Pilnam KimDepartment of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Daejeon, 34141, Republic of Korea.ORCID 0000-0002-5592-4599
Il-Doo KimDepartment of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Daejeon, 34141, Republic of Korea.ORCID 0000-0002-9970-2218
Jae-Byum ChangDepartment of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Daejeon, 34141, Republic of Korea.ORCID 0000-0002-3554-5647

Funding

Ministry of Education 2021R1C1C1006642Ministry of Education 2022R1A5A6000846Ministry of Science and ICT, South Korea 2020M3F7A1094300Ministry of Science and ICT, South Korea 2020R1A2C3013127Ministry of Science and ICT, South Korea 2021M3A9I4026318Ministry of Science and ICT, South Korea 2022M3H4A1A01008918Ministry of Science and ICT, South Korea 2022M3H9A2096195Ministry of Science and ICT, South Korea 2022M3H9A2096201Ministry of SMEs and Startups S3178295National Research Foundation of Korea
6 · The paper itself

Abstract

Biological systems are composed of hierarchical structures made of a large number of proteins. These structures are highly sophisticated and challenging to replicate using artificial synthesis methods. To exploit these structures in materials science, biotemplating is used to achieve biocomposites that accurately mimic biological structures and impart functionality of inorganic materials, including electrical conductivity. However, the biological scaffolds used in previous studies are limited to stereotypical and simple morphologies with little synthetic diversity because of a lack of control over their morphologies. This study proposes that the specific protein assemblies within the cell-derived extracellular matrix (ECM), whose morphological features are widely tailorable, can be employed as versatile biotemplates. In a typical procedure, a fibrillar assembly of fibronectin-a constituent protein of the ECM-is metalized through an antibody-guided biotemplating approach. Specifically, the antibody-bearing nanogold is attached to the fibronectin through antibody-antigen interactions, and then metals are grown on the nanogold acting as a seed. The biomimetic structure can be adapted for hydrogen production and sensing after improving its electrical conductivity through thermal sintering or additional metal growth. This study demonstrates that cell-derived ECM can be an attractive option for addressing the diversity limitation of a conventional biotemplate.

Indexed as

Extracellular MatrixFibronectinsAntibodiesBiomimeticsAntibodiesFibronectinsantibodybiotemplatingextracellular matrixhydrogenmetallization

Identifiers

PMID37852942
PMCPMC10724409

What OpenQuestion holds

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LicenceCC BY
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.