Article in Biosensors, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.4field-weighted citation impact, top 40% 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
2 citing papers in PubMed, 4 citations in OpenAlex.
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
17 authors at 3 institutions in 1 country.
Ana María M MurilloCenter for Biomedical Technology (CTB), Universidad Politécnica de Madrid, Parque Científico y Tecnológico de la UPM, Campus de Montegancedo, Pozuelo de Alarcón, 28223 Madrid, Spain.
Luis G ValleCenter for Biomedical Technology (CTB), Universidad Politécnica de Madrid, Parque Científico y Tecnológico de la UPM, Campus de Montegancedo, Pozuelo de Alarcón, 28223 Madrid, Spain.ORCID 0000-0002-2355-9291
Yolanda RamírezCenter for Biomedical Technology (CTB), Universidad Politécnica de Madrid, Parque Científico y Tecnológico de la UPM, Campus de Montegancedo, Pozuelo de Alarcón, 28223 Madrid, Spain.
María Jesús SánchezEscuela Técnica Superior de Ingenieros Industriales, Universidad Politécnica de Madrid, C/José Gutiérrez Abascal 2, 28006 Madrid, Spain.
Beatriz SantamaríaCenter for Biomedical Technology (CTB), Universidad Politécnica de Madrid, Parque Científico y Tecnológico de la UPM, Campus de Montegancedo, Pozuelo de Alarcón, 28223 Madrid, Spain.ORCID 0000-0003-3714-7922
E Molina-RoldanHealth Research Institute of the Hospital Clínico San Carlos, IdISSC. C/Profesor Martín Lagos s/n, 4ª Planta Sur, 28040 Madrid, Spain.ORCID 0000-0002-8882-8405
Isabel Ortega-MadueñoHealth Research Institute of the Hospital Clínico San Carlos, IdISSC. C/Profesor Martín Lagos s/n, 4ª Planta Sur, 28040 Madrid, Spain.
Elena UrcelayHealth Research Institute of the Hospital Clínico San Carlos, IdISSC. C/Profesor Martín Lagos s/n, 4ª Planta Sur, 28040 Madrid, Spain.ORCID 0000-0001-8470-8396
Luca TramarinCenter for Biomedical Technology (CTB), Universidad Politécnica de Madrid, Parque Científico y Tecnológico de la UPM, Campus de Montegancedo, Pozuelo de Alarcón, 28223 Madrid, Spain.
Pedro HerrerosCenter for Biomedical Technology (CTB), Universidad Politécnica de Madrid, Parque Científico y Tecnológico de la UPM, Campus de Montegancedo, Pozuelo de Alarcón, 28223 Madrid, Spain.ORCID 0000-0002-7543-1142
Araceli Díaz-PeralesCenter for Plant Biotechnology and Genomics (CBGP), Universidad Politécnica de Madrid, Parque Científico y Tecnológico de la UPM, Campus de Montegancedo, Pozuelo de Alarcón, 28223 Madrid, Spain.ORCID 0000-0002-1093-3627
María Garrido-ArandiaCenter for Plant Biotechnology and Genomics (CBGP), Universidad Politécnica de Madrid, Parque Científico y Tecnológico de la UPM, Campus de Montegancedo, Pozuelo de Alarcón, 28223 Madrid, Spain.ORCID 0000-0001-6114-5754
Jaime Tome-AmatCenter for Plant Biotechnology and Genomics (CBGP), Universidad Politécnica de Madrid, Parque Científico y Tecnológico de la UPM, Campus de Montegancedo, Pozuelo de Alarcón, 28223 Madrid, Spain.ORCID 0000-0003-4442-3649
Guadalupe Hernández-RamírezCenter for Plant Biotechnology and Genomics (CBGP), Universidad Politécnica de Madrid, Parque Científico y Tecnológico de la UPM, Campus de Montegancedo, Pozuelo de Alarcón, 28223 Madrid, Spain.
Rocío L EspinosaCenter for Biomedical Technology (CTB), Universidad Politécnica de Madrid, Parque Científico y Tecnológico de la UPM, Campus de Montegancedo, Pozuelo de Alarcón, 28223 Madrid, Spain.ORCID 0000-0001-8524-7229
María F LagunaCenter for Biomedical Technology (CTB), Universidad Politécnica de Madrid, Parque Científico y Tecnológico de la UPM, Campus de Montegancedo, Pozuelo de Alarcón, 28223 Madrid, Spain.ORCID 0000-0002-1580-4509
Miguel HolgadoCenter for Biomedical Technology (CTB), Universidad Politécnica de Madrid, Parque Científico y Tecnológico de la UPM, Campus de Montegancedo, Pozuelo de Alarcón, 28223 Madrid, Spain.ORCID 0000-0001-9299-1371
Hospital Clínico San Carlos · ESCentre for Plant Biotechnology and Genomics · ESUniversidad Politécnica de Madrid · ES
Funding
Ministry of Economy, Industry and Competitiveness TEC2017-84846-RUniversidad Politécnica de Madrid VIMPACTO20MHB
6 · The paper itself
Abstract
In the present work, highly multiplexed diagnostic KITs based on an Interferometric Optical Detection Method (IODM) were developed to evaluate six Coronavirus Disease 2019 (COVID-19)-related biomarkers. These biomarkers of COVID-19 were evaluated in 74 serum samples from severe, moderate, and mild patients with positive polymerase chain reaction (PCR), collected at the end of March 2020 in the Hospital Clínico San Carlos, in Madrid (Spain). The developed multiplexed diagnostic KITs were biofunctionalized to simultaneously measure different types of specific biomarkers involved in COVID-19. Thus, the serum samples were investigated by measuring the total specific Immunoglobulins (sIgT), specific Immunoglobulins G (sIgG), specific Immunoglobulins M (sIgM), specific Immunoglobulins A (sIgA), all of them against SARS-CoV-2, together with two biomarkers involved in inflammatory disorders, Ferritin (FER) and C Reactive Protein (CRP). To assess the results, a Multiple Linear Regression Model (MLRM) was carried out to study the influence of IgGs, IgMs, IgAs, FER, and CRP against the total sIgTs in these serum samples with a goodness of fit of 73.01% (Adjusted R-Squared).
Indexed as
COVID-19COVID-19 TestingBiomarkersC-Reactive ProteinFerritinsHumansImmunoglobulin A, SecretoryReagent Kits, DiagnosticSARS-CoV-2BiomarkersC-Reactive ProteinFerritinsImmunoglobulin A, SecretoryReagent Kits, DiagnosticCOVID-19 biomarkersferritinimmunoglobulininterferometric optical detection methodSARS-CoV-2serum
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.
Integration of Multiple Interferometers in Highly Multiplexed Diagnostic KITs to Evaluate Several Biomarkers of COVID-19 in Serum. · full record | OpenQuestion