Evidence map›Paper›PMID 32582663›Full record

ReviewFrontiers in bioengineering and biotechnology2020

Nanoscale Technologies in Highly Sensitive Diagnosis of Cardiovascular Diseases.

Chaohong Shi, Haotian Xie, Yifan Ma, Zhaogang Yang, Jingjing Zhang

Abstract readReview
In one paragraph

Review in Frontiers in bioengineering and biotechnology, 2020. 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
–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

11 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
  4. Article
  5. Review
  6. Article
  7. Nanotechnology for cardiovascular diseases.Innovation (Cambridge (Mass.)) · 2022
    Review
  8. Review
  9. Article
  10. Review
  11. 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

5 authors.

Chaohong ShiDepartment of Rehabilitation Medicine, The First People's Hospital of Wenling, Wenzhou Medical University, Wenling, China.
Haotian XieDepartment of Mathematics, The Ohio State University, Columbus, OH, United States.
Yifan MaDepartment of Chemical and Biomolecular Engineering, The Ohio State University, Columbus, OH, United States.
Zhaogang YangDepartment of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, United States.
Jingjing ZhangDepartment of Chemical and Biomolecular Engineering, The Ohio State University, Columbus, OH, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cardiovascular diseases (CVD) are the leading cause of death and morbidity in the world and are a major contributor to healthcare costs. Although enormous progress has been made in diagnosing CVD, there is an urgent need for more efficient early detection and the development of novel diagnostic tools. Currently, CVD diagnosis relies primarily on clinical symptoms based on molecular imaging (MOI) or biomarkers associated with CVDs. However, sensitivity, specificity, and accuracy of the assay are still challenging for early-stage CVDs. Nanomaterial platform has been identified as a promising candidate for improving the practical usage of diagnostic tools because of their unique physicochemical properties. In this review article, we introduced cardiac biomarkers and imaging techniques that are currently used for CVD diagnosis. We presented the applications of various nanotechnologies on diagnosis within cardiac immunoassays (CIAs) and molecular imaging. We also summarized and compared different cardiac immunoassays based on their sensitivities and working ranges of biomarkers.

Indexed as

biomarkercardiovascular disease (CVD)diagnosticmolecular imagingnanotechnology

Identifiers

PMID32582663
PMCPMC7289988

What OpenQuestion holds

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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.