Evidence map›Paper›PMID 37803028›Full record

ArticleScientific reports2023

Quantifying innervation facilitated by deep learning in wound healing.

Abijeet Singh Mehta, Sam Teymoori, Cynthia Recendez, Daniel Fregoso, Anthony Gallegos, Hsin-Ya Yang, Elham Aslankoohi, Marco Rolandi, Roslyn Rivkah Isseroff, Min Zhao and 1 more

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
1.9field-weighted citation impact, top 14% 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, 14 citations in OpenAlex.

  1. Review
  2. Characterization of cutaneous wound healing in swine.JID innovations : skin science from molecules to population health · 2026
    Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Learning-based inference of longitudinal image changes: Applications in embryo development, wound healing, and aging brain.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  9. Review
  10. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors at 2 institutions in 1 country.

Abijeet Singh MehtaDepartment of Dermatology, University of California, Davis, CA, 95616, USA. abijeet.mehta@northwestern.edu.
Sam TeymooriDepartment of Applied Mathematics, University of California, Santa Cruz, CA, 95064, USA.
Cynthia RecendezDepartment of Dermatology, University of California, Davis, CA, 95616, USA.
Daniel FregosoDepartment of Dermatology, University of California, Davis, CA, 95616, USA.
Anthony GallegosDepartment of Dermatology, University of California, Davis, CA, 95616, USA.
Hsin-Ya YangDepartment of Dermatology, University of California, Davis, CA, 95616, USA.
Elham AslankoohiDepartment of Electrical and Computer Engineering, University of California, Santa Cruz, CA, 95064, USA.
Marco RolandiDepartment of Electrical and Computer Engineering, University of California, Santa Cruz, CA, 95064, USA.
Roslyn Rivkah IsseroffDepartment of Dermatology, University of California, Davis, CA, 95616, USA.
Min ZhaoDepartment of Dermatology, University of California, Davis, CA, 95616, USA. minzhao@ucdavis.edu.
Marcella GomezDepartment of Applied Mathematics, University of California, Santa Cruz, CA, 95064, USA. mgomez26@ucsc.edu.
University of California, Davis · USUniversity of California, Santa Cruz · US

Funding

Molecular Generators at Corneal Wounds Produce and regulate the Wound Electrical SignalsR01EY019101 · NEI · UNIVERSITY OF CALIFORNIA AT DAVIS · PI ZHAO, MIN · 2010 to 2023
$5.2M
NEI NIH HHS R01 EY019101
6 · The paper itself

Abstract

The peripheral nerves (PNs) innervate the dermis and epidermis, and are suggested to play an important role in wound healing. Several methods to quantify skin innervation during wound healing have been reported. Those usually require multiple observers, are complex and labor-intensive, and the noise/background associated with the immunohistochemistry (IHC) images could cause quantification errors/user bias. In this study, we employed the state-of-the-art deep neural network, Denoising Convolutional Neural Network (DnCNN), to perform pre-processing and effectively reduce the noise in the IHC images. Additionally, we utilized an automated image analysis tool, assisted by Matlab, to accurately determine the extent of skin innervation during various stages of wound healing. The 8 mm wound is generated using a circular biopsy punch in the wild-type mouse. Skin samples were collected on days 3, 7, 10 and 15, and sections from paraffin-embedded tissues were stained against pan-neuronal marker- protein-gene-product 9.5 (PGP 9.5) antibody. On day 3 and day 7, negligible nerve fibers were present throughout the wound with few only on the lateral boundaries of the wound. On day 10, a slight increase in nerve fiber density appeared, which significantly increased on day 15. Importantly, we found a positive correlation (R

Indexed as

Deep LearningAnimalsMiceNerve FibersPeripheral NervesSkinWound Healing

Identifiers

PMID37803028
PMCPMC10558471
OpenAlexW4387397918

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.