Evidence map›Paper›PMID 41522236›Full record

Reviewnpj biomedical innovations2026

Decoding wound healing: cellular insights and technological advances.

Kayleigh A Berthiaume Fox, Emily R Galvin, Erika Kness-Knezinskis, Andrew C Hostler, Kellen Chen, Geoffrey C Gurtner

Abstract readReview
In one paragraph

Review in npj biomedical innovations, 2026. 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. Article
  2. Review
  3. Article
  4. Review
  5. Review
  6. Review
  7. Review
  8. Article
  9. Review
  10. Article
  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

6 authors.

Kayleigh A Berthiaume FoxMedical Scientist MD/PhD Training Program (MSTP), University of Arizona College of Medicine, Tucson, AZ USA.
Emily R GalvinDepartment of Biomedical Engineering, The University of Arizona, Tucson, AZ USA.
Erika Kness-KnezinskisDepartment of Surgery, The University of Arizona College of Medicine, Tucson, AZ USA.
Andrew C HostlerDepartment of Surgery, The University of Arizona College of Medicine, Tucson, AZ USA.
Kellen ChenDepartment of Biomedical Engineering, The University of Arizona, Tucson, AZ USA.
Geoffrey C GurtnerDepartment of Biomedical Engineering, The University of Arizona, Tucson, AZ USA.

Funding

PHYSIOLOGYT32HL007249 · NHLBI · UNIVERSITY OF ARIZONA · PI Brett A Colson, JOHN P KONHILAS · 1985 to 2026
$12.5M
Center for Dental, Oral, and Craniofacial Tissue and Organ Regeneration (C-DOCTOR)U24DE026914 · NIDCR · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI CHAI, YANG, CHEN, YONG · 2017 to 2019
$12.0M
SHORT-TERM TRAINING STUDENTS IN HEALTH PROFESSIONAL SCHOT35HL007479 · NHLBI · UNIVERSITY OF ARIZONA · PI GEOFFREY C GURTNER, JAMES Kuang-Jan LIAO · 1985 to 2026
$4.4M
NHLBI NIH HHS T32 HL007249NHLBI NIH HHS T35 HL007479NIDCR NIH HHS U24 DE026914
6 · The paper itself

Abstract

Wound healing is a complex process involving spatiotemporal patterning of cellular activity across four overlapping phases: hemostasis, inflammation, proliferation, and remodeling, which restore anatomic and functional tissue integrity. Recent advances in cell-based technologies have increased focus on cell populations, heterogeneity, and phenotypes during healing. This manuscript reviews traditional and emerging technologies that advance our understanding of the cellular biology of wounds, from histological methods to high-resolution single-cell, spatial-, and multi-omics.

Indexed as

Computational biology and bioinformaticsDiabetes complicationsGene expression profiling

Identifiers

PMID41522236
PMCPMC12779541

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.