Evidence map›Paper›PMID 38175358›Full record

ArticleNano-micro letters2024

Highly Aligned Ternary Nanofiber Matrices Loaded with MXene Expedite Regeneration of Volumetric Muscle Loss.

Moon Sung Kang, Yeuni Yu, Rowoon Park, Hye Jin Heo, Seok Hyun Lee, Suck Won Hong, Yun Hak Kim, Dong-Wook Han

Open access · diamondAbstract read
In one paragraph

Article in Nano-micro letters, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
3.3field-weighted citation impact, top 5% of its field
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

11 citing papers in PubMed, 28 citations in OpenAlex.

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  11. 3D bioprintable MgTheranostics · 2025
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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

8 authors at 1 institution in 1 country.

Moon Sung Kang *Department of Cogno-Mechatronics Engineering, College of Nanoscience and Nanotechnology, Pusan National University, Busan, 46241, Republic of Korea.
Yeuni Yu *Medical Research Institute, School of Medicine, Pusan National University, Yangsan, 50612, Republic of Korea.
Rowoon Park *Department of Cogno-Mechatronics Engineering, College of Nanoscience and Nanotechnology, Pusan National University, Busan, 46241, Republic of Korea.
Hye Jin HeoDepartment of Anatomy, School of Medicine, Pusan National University, Yangsan, 50612, Republic of Korea.
Seok Hyun LeeDepartment of Cogno-Mechatronics Engineering, College of Nanoscience and Nanotechnology, Pusan National University, Busan, 46241, Republic of Korea.
Suck Won HongDepartment of Cogno-Mechatronics Engineering, College of Nanoscience and Nanotechnology, Pusan National University, Busan, 46241, Republic of Korea. swhong@pusan.ac.kr.
Yun Hak KimMedical Research Institute, School of Medicine, Pusan National University, Yangsan, 50612, Republic of Korea. yunhak10510@pusan.ac.kr.
Dong-Wook HanDepartment of Cogno-Mechatronics Engineering, College of Nanoscience and Nanotechnology, Pusan National University, Busan, 46241, Republic of Korea. nanohan@pusan.ac.kr.
Pusan National University · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Current therapeutic approaches for volumetric muscle loss (VML) face challenges due to limited graft availability and insufficient bioactivities. To overcome these limitations, tissue-engineered scaffolds have emerged as a promising alternative. In this study, we developed aligned ternary nanofibrous matrices comprised of poly(lactide-co-ε-caprolactone) integrated with collagen and Ti

Indexed as

MyogenesisNext generation sequencingRegeneration of volumetric muscle lossTernary nanofibrous matricesTi3C2Tx MXene nanoparticle

Identifiers

PMID38175358
PMCPMC10767178
OpenAlexW4390589918

What OpenQuestion holds

Textmetadata
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.