Evidence map›Paper›PMID 33251124›Full record

ArticleMethodsX2020

Numerical framework for simulating bio-species transport in microfluidic channels with application to antibody biosensors.

Fatemeh Shahbazi, Masoud Jabbari, Mohammad Nasr Esfahani, Amir Keshmiri

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

Article in MethodsX, 2020. 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
0.4field-weighted citation impact, top 41% 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

1 citing paper in PubMed, 8 citations in OpenAlex.

  1. 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

4 authors at 3 institutions in 1 country.

Fatemeh ShahbaziDepartment of Mechanical, Aerospace and Civil Engineering, University of Manchester, Manchester M13 9PL, UK.
Masoud JabbariDepartment of Mechanical, Aerospace and Civil Engineering, University of Manchester, Manchester M13 9PL, UK.
Mohammad Nasr EsfahaniDepartment of Electronic Engineering, University of York, York YO10 5DD, UK.
Amir KeshmiriDepartment of Mechanical, Aerospace and Civil Engineering, University of Manchester, Manchester M13 9PL, UK.
University of Manchester · GBManchester Academic Health Science Centre · GBUniversity of York · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Diagnosis is a fundamental stage in health care and medical treatment. Microfluidic biosensors and lab-on-a-chip devices are amongst the few practical tools for achieving this goal. A new computational code, specifically for designing microfluidic-integrated biosensors is developed, the details of which is presented in this work. This new approach is developed using control-volume based finite-element (CVFEM) method and solves bio-recognition chemical reactions and full Navier-Stokes equations. The results of the proposed platform are validated against the experimental data for a microfluidic based biosensor, where excellent agreement is achieved. The properties of the biosensor, sample, buffer fluid and even the microfluidic channel can easily be modified in this platform. This feature provides the scientific community with the ability to design a specific biosensor for requested point-of-care applications.•A new approach is developed using control-volume based finite-element (CVFEM) method for investigating flow inside a microfluidic-integrated biosensor. It is also used to study the influence of surface functionalization on binding cycle.•The proposed model solves bio-recognition chemical reactions as well as full Navier-Stokes and energy equations. Experimental-based or personalized equations of the chemical reactions and flow behaviour are adoptable to this code.•The developed model is Fortran-based and has the potential to be used in both industry and academia for biosensing technology.

Indexed as

BiosensorsCFDComputational codeComputational fluid dynamicsMicrofluidicsNumerical modelling

Identifiers

PMID33251124
PMCPMC7679250
OpenAlexW3095055250

What OpenQuestion holds

Textmetadata
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.