Evidence map›Paper›PMID 42044142›Full record

ArticlePloS one2026

Gene expression dynamics in wound healing: Comparative analysis between the wound edge and center.

Ksenia Zlobina, Elham Aslankoohi, Marco Rolandi, Rivkah Isseroff, Marcella Gomez

Abstract readComparative Study
In one paragraph

Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Ksenia ZlobinaApplied Mathematics Department, University of California Santa Cruz, California, United States of America.ORCID https://orcid.org/0000-0001-6592-0965
Elham AslankoohiElectrical and Computer Engineering Department, University of California Santa Cruz, Santa Cruz, California, United States of America.
Marco RolandiElectrical and Computer Engineering Department, University of California Santa Cruz, Santa Cruz, California, United States of America.
Rivkah IsseroffDepartment of Dermatology, School of Medicine, University of California Davis, Davis, California, United States of America.
Marcella GomezApplied Mathematics Department, University of California Santa Cruz, California, United States of America.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Wound healing is a dynamic and spatially heterogeneous process involving coordinated activity across multiple cell types. We analyze a high-resolution porcine wound-healing transcriptomic dataset of 150 samples from wound edges and centers collected across 15 time points (days 0-21). Using correlation-based clustering and gene ontology analysis, we identify major groups of synchronously expressed genes representing immune activity, extracellular matrix (ECM) remodeling, epithelial repair and several tissue-specific clusters. Immune clusters peak on days 1-6 and are consistently higher at the wound center. ECM clusters show early suppression followed by gradual activation in both regions. Epithelial clusters remain high at the wound edge but show a day-1 drop and gradual recovery at the center. Additional hair, muscle and lipid clusters display abrupt, non-smooth patterns driven by sample heterogeneity. A low-dimensional projection of cluster means reveals a "round-trip" healing trajectory in immune-epithelial space. This analysis provides a transcriptomic reference for acute wound healing and highlights the importance of sampling precision in wound transcriptomics.

Indexed as

Gene Expression RegulationTranscriptomeWound HealingAnimalsExtracellular MatrixGene Expression ProfilingSwine

Identifiers

PMID42044142
PMCPMC13119960

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.