Evidence map›Paper›PMID 37131342›Full record

ArticleEuropean physical journal plus2023

Taguchi method: artificial neural network approach for the optimization of high-efficiency microfluidic biosensor for COVID-19.

Imed Ben Romdhane, Asma Jemmali, Sameh Kaziz, Fraj Echouchene, Thamraa Alshahrani, Hafedh Belmabrouk

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in European physical journal plus, 2023. 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. 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

6 authors.

Imed Ben RomdhaneLaboratory of Electronics and Microelectronics, Faculty of Science of Monastir, University of Monastir, 5019 Monastir, Tunisia.
Asma JemmaliLaboratory of Electronics and Microelectronics, Faculty of Science of Monastir, University of Monastir, 5019 Monastir, Tunisia.
Sameh KazizQuantum and Statistical Physics Laboratory, Faculty of Sciences of Monastir, University of Monastir, 5019 Monastir, Tunisia.
Fraj EchoucheneLaboratory of Electronics and Microelectronics, Faculty of Science of Monastir, University of Monastir, 5019 Monastir, Tunisia.
Thamraa AlshahraniDepartment of Physics, College of Science, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia.
Hafedh BelmabroukLaboratory of Electronics and Microelectronics, Faculty of Science of Monastir, University of Monastir, 5019 Monastir, Tunisia.ORCID 0000-0003-0840-0271

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

COVID-19 is a pandemic disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This virus is mainly spread by droplets, respiratory secretions, and direct contact. Caused by the huge spread of the COVID-19 epidemic, research is focused on the study of biosensors as it presents a rapid solution for reducing incidents and fatality rates. In this paper, a microchip flow confinement method for the rapid transport of small sample volumes to sensor surfaces is optimized in terms of the confinement coefficient Graphic abstract:

Identifiers

PMID37131342
PMCPMC10132959

What OpenQuestion holds

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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.