Evidence map›Paper›PMID 39030436›Full record

ArticleScientific reports2024

The use of a protein network analysis to explore the complexity of early skin inflammation after oronasal mask application- A pilot study.

Amanda Feldt, Bijar Ghafouri, Peter R Worsley, Sara Bergstrand

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

4 authors.

Amanda FeldtDepartment of Acute Internal Medicine and Geriatrics in Linköping, and Department of Health, Medicine and Caring Sciences, Linköping University, 581 83, Linköping, SE, Sweden. Amanda.feldt@liu.se.
Bijar GhafouriPain and Rehabilitation Centre, and Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden.
Peter R WorsleySkin Sensing Research Group, School of Health Sciences, University of Southampton, Southampton, UK.
Sara BergstrandDepartment of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden.

Funding

Region Östergötland RÖ-987649Vetenskapsrådet 2020-01704
6 · The paper itself

Abstract

Medical devices, such as non-invasive ventilation masks, save lives in health care settings but can be a cause of tissue injuries due to the pressure and shear loads on skin and soft tissue. These pressure injuries could be painful for the individual and cause a significant economic impact on healthcare providers. In the etiology of device related pressure ulcers, inflammation plays an important role. However, the exact nature and timing of inflammatory biomarker upregulation is still unknown in the early stages of skin damage. This study aimed to explore the inflammatory profile of vulnerable skin sites following non-invasive mask application on a convenience sample of eleven hospital patients. Seventy-one inflammatory proteins were explored from sebum sampled at the skin surface after oronasal mask application. A multivariate analysis to investigate differences between loaded and control site was conducted, with a protein network analysis used to explore interactions in the early inflammation. The study revealed that 21 cytokines and chemokines were important for the separation between loaded and control site. These proteins were associated with remodeling of tissue, vascular wound healing and/or cell death.

Indexed as

MasksSkinAdultAgedBiomarkersCytokinesFemaleHumansInflammationMaleMiddle AgedPilot ProjectsPressure UlcerProtein Interaction MapsWound HealingBiomarkersCytokines

Identifiers

PMID39030436
PMCPMC11271608

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.