Evidence map›Paper›PMID 31016206›Full record

ArticleJournal of immunology research2019

Automated and Reproducible Detection of Vascular Endothelial Growth Factor (VEGF) in Renal Tissue Sections.

Nayana Damiani Macedo, Aline Rodrigues Buzin, Isabela Bastos de Araujo, Breno Valentim Nogueira, Tadeu Uggere Andrade, Denise Coutinho Endringer, Dominik Lenz

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of immunology research, 2019. 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
0.3field-weighted citation impact, top 25% of its field
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, 2 citations in OpenAlex.

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 at 2 institutions in 2 countries.

Nayana Damiani MacedoUniversity Vila Velha, Pharmaceutical Sciences, Vila Velha, Brazil.
Aline Rodrigues BuzinUniversity Vila Velha, Pharmaceutical Sciences, Vila Velha, Brazil.
Isabela Bastos de AraujoDepartment of Morphology, Federal University of Espírito Santo, Vitória, Brazil.
Breno Valentim NogueiraDepartment of Morphology, Federal University of Espírito Santo, Vitória, Brazil.
Tadeu Uggere AndradeUniversity Vila Velha, Pharmaceutical Sciences, Vila Velha, Brazil.
Denise Coutinho EndringerUniversity Vila Velha, Pharmaceutical Sciences, Vila Velha, Brazil.
Dominik LenzUniversity Vila Velha, Pharmaceutical Sciences, Vila Velha, Brazil.ORCID 0000-0001-6932-401X
Universidade Vila Velha · BRUniversidade Federal do Espírito Santo · BR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundManual analysis of tissue sections, such as for pathological diagnosis, requires an analyst with substantial knowledge and experience. Reproducible image analysis of biological samples is steadily gaining scientific importance. The aim of the present study was to employ image analysis followed by machine learning to identify vascular endothelial growth factor (VEGF) in kidney tissue that had been subjected to hypoxia.

methodsLight microscopy images of renal tissue sections stained for VEGF were analyzed. Subsequently, machine learning classified the cells as VEGF

resultsVEGF was detected and cells were counted with high sensitivity and specificity.

conclusionWith great clinical, diagnostic, and research potential, automatic image analysis offers a new quantitative capability, thereby adding numerical information to a mostly qualitative diagnostic approach.

Indexed as

Automation, LaboratoryHistological TechniquesHumansHypoxiaImage Processing, Computer-AssistedKidneyMachine LearningMicroscopyParaffin EmbeddingSensitivity and SpecificityVascular Endothelial Growth Factor AVascular Endothelial Growth Factor AVEGFA protein, human

Identifiers

PMID31016206
PMCPMC6444260
OpenAlexW2922955490

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.