Evidence map›Paper›PMID 36034216›Full record

ArticleiScience2022

New bladder cancer non-invasive surveillance method based on voltammetric electronic tongue measurement of urine.

Javier Monreal-Trigo, Miguel Alcañiz, M Carmen Martínez-Bisbal, Alba Loras, Lluís Pascual, José Luis Ruiz-Cerdá, Alberto Ferrer, Ramón Martínez-Máñez

Abstract read
In one paragraph

Article in iScience, 2022. 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. Relevance ofCancers · 2024
    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

8 authors.

Javier Monreal-TrigoInstituto Interuniversitario de Investigación de Reconocimiento Molecular y Desarrollo Tecnológico (IDM), Universitat Politècnica de València - Universitat de València, Camino de Vera, s/n, 46022 Valencia, Spain.
Miguel AlcañizInstituto Interuniversitario de Investigación de Reconocimiento Molecular y Desarrollo Tecnológico (IDM), Universitat Politècnica de València - Universitat de València, Camino de Vera, s/n, 46022 Valencia, Spain.
M Carmen Martínez-BisbalInstituto Interuniversitario de Investigación de Reconocimiento Molecular y Desarrollo Tecnológico (IDM), Universitat Politècnica de València - Universitat de València, Camino de Vera, s/n, 46022 Valencia, Spain.
Alba LorasUnidad Mixta de Investigación en Nanomedicina y Sensores, Universitat Politècnica de València - Instituto de Investigación Sanitaria La Fe, 46026 Valencia, Spain.
Lluís PascualInstituto Interuniversitario de Investigación de Reconocimiento Molecular y Desarrollo Tecnológico (IDM), Universitat Politècnica de València - Universitat de València, Camino de Vera, s/n, 46022 Valencia, Spain.
José Luis Ruiz-CerdáUnidad Mixta de Investigación en Nanomedicina y Sensores, Universitat Politècnica de València - Instituto de Investigación Sanitaria La Fe, 46026 Valencia, Spain.
Alberto FerrerDepartamento de Estadística e Investigación Operativa Aplicadas y Calidad, Universitat Politècnica de València, Camino de Vera, s/n, 46022 Valencia, Spain.
Ramón Martínez-MáñezInstituto Interuniversitario de Investigación de Reconocimiento Molecular y Desarrollo Tecnológico (IDM), Universitat Politècnica de València - Universitat de València, Camino de Vera, s/n, 46022 Valencia, Spain.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Bladder cancer (BC) is the sixth leading cause of death by cancer. Depending on the invasiveness of tumors, patients with BC will undergo surgery and surveillance lifelong, owing the high rate of recurrence and progression. In this context, the development of strategies to support non-invasive BC diagnosis is focusing attention. Voltammetric electronic tongue (VET) has been demonstrated to be of use in the analysis of biofluids. Here, we present the implementation of a VET to study 207 urines to discriminate BC and non-BC for diagnosis and surveillance to detect recurrences. Special attention has been paid to the experimental setup to improve reproducibility in the measurements. PLSDA analysis together with variable selection provided a model with high sensitivity, specificity, and area under the ROC curve AUC (0.844, 0.882, and 0.917, respectively). These results pave the way for the development of non-invasive low-cost and easy-to-use strategies to support BC diagnosis and follow-up.

Indexed as

CancerComputer-aided diagnosis methodDevices

Identifiers

PMID36034216
PMCPMC9399275

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.