Evidence map›Paper›PMID 35624294›Full record

ArticleScientific reports2022

A free customizable tool for easy integration of microfluidics and smartphones.

Federico Schaumburg, Juan P Vidocevich, Gabriel S Gerlero, Nazarena Pujato, Joana Macagno, Pablo A Kler, Claudio L A Berli

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.8field-weighted citation impact, top 16% 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

4 citing papers in PubMed, 22 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. Next-Generation Microfluidics for Biomedical Research and Healthcare Applications.Biomedical engineering and computational biology · 2023
    Review
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 3 institutions in 1 country.

Federico SchaumburgInstituto de Desarrollo Tecnológico para la Industria Química (INTEC, UNL-CONICET), Colectora RN 168, S3000GLN, Santa Fe, Argentina. fschaumburg@intec.unl.edu.ar.
Juan P VidocevichInstituto de Desarrollo Tecnológico para la Industria Química (INTEC, UNL-CONICET), Colectora RN 168, S3000GLN, Santa Fe, Argentina.
Gabriel S GerleroCentro de Investigación de Métodos Computacionales (CIMEC, UNL-CONICET), Colectora RN 168, S3000GLN, Santa Fe, Argentina.
Nazarena Pujato *Laboratorio de Tecnología Inmunológica (FBCB, UNL), Colectora RN 168, S3000GLN, Santa Fe, Argentina.
Joana Macagno *Instituto de Desarrollo Tecnológico para la Industria Química (INTEC, UNL-CONICET), Colectora RN 168, S3000GLN, Santa Fe, Argentina.
Pablo A KlerCentro de Investigación de Métodos Computacionales (CIMEC, UNL-CONICET), Colectora RN 168, S3000GLN, Santa Fe, Argentina.
Claudio L A BerliInstituto de Desarrollo Tecnológico para la Industria Química (INTEC, UNL-CONICET), Colectora RN 168, S3000GLN, Santa Fe, Argentina. cberli@santafe-conicet.gov.ar.
Instituto de Desarrollo Tecnológico para la Industria Química · ARConsejo Nacional de Investigaciones Científicas y Técnicas · ARCentro de Investigación de Métodos Computacionales · AR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The integration of smartphones and microfluidics is nowadays the best possible route to achieve effective point-of-need testing (PONT), a concept increasingly demanded in the fields of human health, agriculture, food safety, and environmental monitoring. Nevertheless, efforts are still required to integrally seize all the advantages of smartphones, as well as to share the developments in easily adoptable formats. For this purpose, here we present the free platform appuente that was designed for the easy integration of microfluidic chips, smartphones, and the cloud. It includes a mobile app for end users, which provides chip identification and tracking, guidance and control, processing, smart-imaging, result reporting and cloud and Internet of Things (IoT) integration. The platform also includes a web app for PONT developers, to easily customize their mobile apps and manage the data of administered tests. Three application examples were used to validate appuente: a dummy grayscale detector that mimics quantitative colorimetric tests, a root elongation assay for pesticide toxicity assessment, and a lateral flow immunoassay for leptospirosis detection. The platform openly offers fast prototyping of smartphone apps to the wide community of lab-on-a-chip developers, and also serves as a friendly framework for new techniques, IoT integration and further capabilities. Exploiting these advantages will certainly help to enlarge the use of PONT with real-time connectivity in the near future.

Indexed as

Mobile ApplicationsSmartphoneFood SafetyHumansLab-On-A-Chip DevicesMicrofluidics

Identifiers

PMID35624294
PMCPMC9142529
OpenAlexW4281709332

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.