Evidence map›Paper›PMID 35024500›Full record

ArticleJACC. Basic to translational science2021

Comprehensive Assessment of High-Risk Plaques by Dual-Modal Imaging Catheter in Coronary Artery.

Sunwon Kim, Hyeong Soo Nam, Min Woo Lee, Hyun Jung Kim, Woo Jae Kang, Joon Woo Song, Jeongmoo Han, Dong Oh Kang, Wang-Yuhl Oh, Hongki Yoo and 1 more

Open access · goldAbstract read
In one paragraph

Article in JACC. Basic to translational science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 pooled it
2.5field-weighted citation impact, top 10% 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

10 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.

  1. Pooled it
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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

11 authors at 4 institutions in 1 country.

Sunwon KimMultimodal Imaging and Theranostic Lab, Cardiovascular Center, Korea University Guro Hospital, Seoul, South Korea.
Hyeong Soo NamDepartment of Mechanical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea.
Min Woo LeeDepartment of Biomedical Engineering, Hanyang University, Seoul, South Korea.
Hyun Jung KimMultimodal Imaging and Theranostic Lab, Cardiovascular Center, Korea University Guro Hospital, Seoul, South Korea.
Woo Jae KangDepartment of Mechanical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea.
Joon Woo SongMultimodal Imaging and Theranostic Lab, Cardiovascular Center, Korea University Guro Hospital, Seoul, South Korea.
Jeongmoo HanDepartment of Mechanical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea.
Dong Oh KangMultimodal Imaging and Theranostic Lab, Cardiovascular Center, Korea University Guro Hospital, Seoul, South Korea.
Wang-Yuhl OhDepartment of Mechanical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea.
Hongki YooDepartment of Mechanical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea.
Jin Won KimMultimodal Imaging and Theranostic Lab, Cardiovascular Center, Korea University Guro Hospital, Seoul, South Korea.
Korea Advanced Institute of Science and Technology · KRKorea University Medical Center · KRHanyang University · KRKorea University · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Coronary plaque destabilization involves alterations in microstructure and biochemical composition; however, no imaging approach allows such comprehensive characterization. Herein, the authors demonstrated a simultaneous microstructural and biochemical assessment of high-risk plaques in the coronary arteries in a beating heart using a fully integrated optical coherence tomography and fluorescence lifetime imaging (FLIm). It was found that plaque components such as lipids, macrophages, lipids+macrophages, and fibrotic tissues had unique fluorescence lifetime signatures that were distinguishable using multispectral FLIm. Because FLIm yielded massive biochemical readouts, the authors incorporated machine learning framework into FLIm, and ultimately, their approach enabled an automated, quantitative imaging of multiple key components relevant for plaque destabilization.

Indexed as

ch, channelFL, fluorescence lifetimeFLIm, fluorescence lifetime imagingfluorescence lifetime imaginghigh-risk plaqueICC, intraclass correlation coefficientinflammationIR, intensity ratioIVUS, intravascular ultrasoundlipidmachine learningMФ, macrophageOCT, optical coherence tomographyoptical coherence tomographyRFC, random forest classifierROI, region of interestSMC, smooth muscle cellTCFA, thin-cap fibroatheromaUV, ultraviolet

Identifiers

PMID35024500
PMCPMC8733747
OpenAlexW4200348146

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.