Data · dataset · 2021
Multispectral and autofluorescence, human and mouse
Listed in Teesside University Research Data Repository
Gastric inflammation is a major risk factor for gastric cancer.
Description
Current endoscopic methods are not capable to efficiently detect and characterize gastric inflammation, leading to a sub-optimal patients’ care. New non-invasive methods are needed.
Reflectance mucosal light analysis is of particular interest in this indication. The aim of our study was to analyze the reflectance light and specific autofluorescence signals, both in humans and in a mouse model of gastritis. We recruited patients undergoing gastroendoscopic procedure during which reflectance was analysed with a multispectral camera.
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In parallel, the gastritis mouse model of Helicobacter pylori infection was used to investigate reflectance from ex vivo gastric samples using a spectrometer. In both cases, autofluorescence signals were measured using a confocal microscope. In gastritis patients, reflectance modifications were significant in near-infrared spectrum, with a decrease between 610 and 725 nm and an increase between 750 and 840 nm.
Autofluorescence was also modified, showing variations around 550 nm of emission. In H. pylori infected mice developing gastric inflammatory lesions, we observed significant reflectance modifications 18 months after infection, with increased intensity between 617 and 672 nm. Autofluorescence was significantly modified after 1, 3 and 6 months around 550 and 630 nm.
Both in human and in mouse, these reflectance data can be considered as biomarkers and accurately predicted inflammatory state.
Links
Where it is published
- DOI doi.org/10.17632/bcbdcn3t6c.1 ↗
DOI / persistent id · from researchdata tees ac uk
Catalogue records · 1
- OAI-PMH record data.mendeley.com/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai%3Adata… ↗
metadata API · from researchdata tees ac uk
Topics
- From keywords
- Earth & Environmental Science · Engineering · Humanities · Life Sciences · Social Science
- Inferred from text
- Cancer 75%
Provenance · 1 source records, 11 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| Teesside University Research Data Repository | oai:data.mendeley.com/bcbdcn3t6c.1 | 9 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[disease].local:disease:cancer | enrichment · researchdata tees ac uk | keyword-concept-rules@1.0.0 | title+description (75%) |
| concepts[field].local:field:earth-environmental | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:engineering | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:humanities | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:life-sciences | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:social-science | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| description | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/description |
| license | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/rights |
| publication_date | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| title | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/title |