Evidence map›Paper›PMID 41981209›Full record

ArticleCommunications biology2026

Real-time characterisation of microbe-induced inflammation using a novel zebrafish larval corneal injury and infection model.

Kelvin K W Cheng, Carl S Tucker, Justyna Cholewa-Waclaw, Stephen Mitchell, Fraser Laidlaw, Bethany Mills, Adriano G Rossi

Abstract read
In one paragraph

Article in Communications biology, 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

7 authors.

Kelvin K W ChengCentre for Inflammation Research, Institute for Regeneration and Repair, University of Edinburgh, Edinburgh, UK. kcheng@ed.ac.uk.ORCID http://orcid.org/0000-0001-5618-082X
Carl S TuckerBioresearch & Veterinary Services (BVS) Aquatics Facility, College of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh, UK.
Justyna Cholewa-WaclawHigh Content Screening Facility, University of Edinburgh, Edinburgh, UK.
Stephen MitchellKings Buildings, University of Edinburgh, Edinburgh, UK.
Fraser LaidlawKings Buildings, University of Edinburgh, Edinburgh, UK.ORCID http://orcid.org/0000-0002-5907-0447
Bethany MillsCentre for Inflammation Research, Institute for Regeneration and Repair, University of Edinburgh, Edinburgh, UK.ORCID http://orcid.org/0000-0003-3209-9490
Adriano G RossiCentre for Inflammation Research, Institute for Regeneration and Repair, University of Edinburgh, Edinburgh, UK.

Funding

RCUK | Medical Research Council (MRC) MR/K013386/1Wellcome Trust (Wellcome) 320097/Z/24/Z
6 · The paper itself

Abstract

Microbial keratitis (MK) is a major global cause of blindness. Yet, treatment is heavily dependent on antimicrobials with limited options for immunomodulators - despite the critical role of dysregulated immune responses in disease pathogenesis. This gap reflects a critical unmet clinical need and is compounded by the lack of model systems capable of real-time high-resolution immune dynamics analysis. To address this, we developed a zebrafish larvae MK model utilising transgenic zebrafish lines with fluorescently labelled neutrophils, macrophages and basal epithelial cells. Corneal injury triggered rapid immune cell recruitment which was amplified by exposure to pro-inflammatory mediators such as N-formylmethionine-leucyl-phenylalanine (fMLF) and leukotriene B

Indexed as

Corneal InjuriesInflammationKeratitisZebrafishAnimalsAnimals, Genetically ModifiedDisease Models, AnimalLarvaMacrophagesNeutrophils

Identifiers

PMID41981209
PMCPMC13261116

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.