Evidence map›Paper›PMID 42367254›Full record

ArticleNature electronics2022

Smart textiles for personalized healthcare.

Alberto Libanori, Guorui Chen, Xun Zhao, Yihao Zhou, Jun Chen

Abstract read
In one paragraph

Article in Nature electronics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 154 papers.

0numbers the graph read from it
0cells of the map it votes in
154citing 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

154 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Review
  6. Review
  7. Review
  8. Article
  9. Article
  10. Advances in Wearable Bioimaging.Advanced materials (Deerfield Beach, Fla.) · 2026
    Review
  11. Article
  12. Review
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Review

94 more citing papers are in PubMed but not listed here.

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.

Alberto LibanoriDepartment of Bioengineering, University of California, Los Angeles, Los Angeles, CA, USA.
Guorui ChenDepartment of Bioengineering, University of California, Los Angeles, Los Angeles, CA, USA.
Xun ZhaoDepartment of Bioengineering, University of California, Los Angeles, Los Angeles, CA, USA.
Yihao ZhouDepartment of Bioengineering, University of California, Los Angeles, Los Angeles, CA, USA.
Jun ChenDepartment of Bioengineering, University of California, Los Angeles, Los Angeles, CA, USA.ORCID 0000-0002-3439-0495

Funding

Magnetoelastic Vascular GraftsR01HL175135 · NHLBI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Jun Chen · 2025 to 2026
$1.1M
A soft magnetoelastic microneedle patch for rapid skin cancer screeningR01CA287326 · NCI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Jun Chen · 2024 to 2026
$1.1M
NCI NIH HHS R01 CA287326NHLBI NIH HHS R01 HL175135
6 · The paper itself

Abstract

Incorporating sensing and therapeutic capabilities into everyday textiles could be a powerful approach in the development of personalized healthcare. The creation of such smart textiles has been driven by the fabrication of various miniaturized platform technologies, and has led to the construction of compact, autonomous and interconnected functional textiles. Here we review the development of smart textiles for application in personalized healthcare. We examine the different platform technologies, the various fabrication strategies and the range of clinical scenarios in which they are used. We also explore the current commercial and regulatory landscapes, and consider issues of data management. Finally, we highlight the key steps required to transition these technological platforms to commercial applications.

Identifiers

PMID42367254
PMCPMC13309183

What OpenQuestion holds

Textmetadata
LicenceTDM
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.