Evidence map›Paper›PMID 37507439›Full record

ArticleScientific reports2023

Full thickness 3D in vitro conjunctiva model enables goblet cell differentiation.

Julian Schwebler, Christina Fey, Daniel Kampik, Christian Lotz

Abstract 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 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
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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.

Julian SchweblerTranslational Center Regenerative Therapies (TLC-RT), Fraunhofer Institute for Silicate Research (ISC), Würzburg, Germany.
Christina FeyTranslational Center Regenerative Therapies (TLC-RT), Fraunhofer Institute for Silicate Research (ISC), Würzburg, Germany.
Daniel KampikDepartment of Ophthalmology, University Hospital Würzburg, Würzburg, Germany.
Christian LotzTranslational Center Regenerative Therapies (TLC-RT), Fraunhofer Institute for Silicate Research (ISC), Würzburg, Germany. christian.lotz@isc.fraunhofer.de.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In vitro culture and generation of highly specialized goblet cells is still a major challenge in conjunctival 3D in vitro equivalents. A model comprising all physiological factors, including mucus-secreting goblet cells has the potential to act as a new platform for studies on conjunctival diseases. We isolated primary conjunctival epithelial cells and fibroblasts from human biopsies. 3D models were generated from either epithelial layers or a combination of those with a connective tissue equivalent. Epithelial models were investigated for marker expression and barrier function. Full-thickness models were analyzed for goblet cell morphology and marker expression via immunofluorescence and quantitative real-time PCR. Simple epithelial models cultured at the air-liquid interface showed stratified multi-layer epithelia with pathologic keratinization and without goblet cell formation. The combination with a connective tissue equivalent to generate a full-thickness model led to the formation of a non-keratinized stratified multi-layer epithelium and induced goblet cell differentiation. In our model, a high resemblance to natural conjunctiva was achieved by the combination of conjunctival epithelial cells with fibroblasts embedded in a collagen-hydrogel as connective tissue equivalent. In the future, our conjunctival in vitro equivalent enables the investigation of goblet cell differentiation, conjunctival pathologies as well as drug testing.

Indexed as

ConjunctivaGoblet CellsCell DifferentiationEpithelial CellsEpitheliumHumans

Identifiers

PMID37507439
PMCPMC10382544

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Registered trials

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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.