Evidence map›Paper›PMID 36005062›Full record

ReviewBiosensors2022

Portable Electrochemical Biosensors Based on Microcontrollers for Detection of Viruses: A Review.

Muhammad Afiq Abdul Ghani, Anis Nurashikin Nordin, Munirah Zulhairee, Adibah Che Mohamad Nor, Mohd Shihabuddin Ahmad Noorden, Muhammad Khairul Faisal Muhamad Atan, Rosminazuin Ab Rahim, Zainiharyati Mohd Zain

Open access · goldAbstract readReview
In one paragraph

Review in Biosensors, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
2.7field-weighted citation impact, top 10% 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

11 citing papers in PubMed, 36 citations in OpenAlex.

  1. Review
  2. Review
  3. Article
  4. Article
  5. Article
  6. Review
  7. Review
  8. Electrochemical biosensor detection on respiratory and flaviviruses.Applied microbiology and biotechnology · 2023
    Review
  9. Review
  10. Article
  11. 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 at 2 institutions in 1 country.

Muhammad Afiq Abdul GhaniMEMS-VLSI Research Unit, Department of Electrical and Computer Engineering, Engineering Faculty, International Islamic University Malaysia, Kuala Lumpur 53100, Federal Territory of Kuala Lumpur, Malaysia.
Anis Nurashikin NordinMEMS-VLSI Research Unit, Department of Electrical and Computer Engineering, Engineering Faculty, International Islamic University Malaysia, Kuala Lumpur 53100, Federal Territory of Kuala Lumpur, Malaysia.ORCID 0000-0002-8301-4365
Munirah ZulhaireeElectrochemical Material and Sensor (EMaS) Research Group, Faculty of Applied Sciences, Universiti Teknologi MARA, Shah Alam 40450, Selangor, Malaysia.
Adibah Che Mohamad NorFaculty of Pharmacy, Universiti Teknologi MARA, Puncak Alam Campus, Bandar Puncak Alam 42300, Selangor, Malaysia.
Mohd Shihabuddin Ahmad NoordenFaculty of Pharmacy, Universiti Teknologi MARA, Puncak Alam Campus, Bandar Puncak Alam 42300, Selangor, Malaysia.
Muhammad Khairul Faisal Muhamad AtanMEMS-VLSI Research Unit, Department of Electrical and Computer Engineering, Engineering Faculty, International Islamic University Malaysia, Kuala Lumpur 53100, Federal Territory of Kuala Lumpur, Malaysia.
Rosminazuin Ab RahimMEMS-VLSI Research Unit, Department of Electrical and Computer Engineering, Engineering Faculty, International Islamic University Malaysia, Kuala Lumpur 53100, Federal Territory of Kuala Lumpur, Malaysia.ORCID 0000-0002-7220-9183
Zainiharyati Mohd ZainElectrochemical Material and Sensor (EMaS) Research Group, Faculty of Applied Sciences, Universiti Teknologi MARA, Shah Alam 40450, Selangor, Malaysia.
International Islamic University Malaysia · MYUniversiti Teknologi MARA · MY

Funding

Ministry of Science and Technology Malaysia CV02211032
6 · The paper itself

Abstract

With the rise of zoonotic diseases in recent years, there is an urgent need for improved and more accessible screening and diagnostic methods to mitigate future outbreaks. The recent COVID-19 pandemic revealed an over-reliance on RT-PCR, a slow, costly and lab-based method for diagnostics. To better manage the pandemic, a high-throughput, rapid point-of-care device is needed for early detection and isolation of patients. Electrochemical biosensors offer a promising solution, as they can be used to perform on-site tests without the need for centralized labs, producing high-throughput and accurate measurements compared to rapid test kits. In this work, we detail important considerations for the use of electrochemical biosensors for the detection of respiratory viruses. Methods of enhancing signal outputs via amplification of the analyte, biorecognition of elements and modification of the transducer are also explained. The use of portable potentiostats and microfluidics chambers that create a miniature lab are also discussed in detail as an alternative to centralized laboratory settings. The state-of-the-art usage of portable potentiostats for detection of viruses is also elaborated and categorized according to detection technique: amperometry, voltammetry and electrochemical impedance spectroscopy. In terms of integration with microfluidics, RT-LAMP is identified as the preferred method for DNA amplification virus detection. RT-LAMP methods have shorter turnaround times compared to RT-PCR and do not require thermal cycling. Current applications of RT-LAMP for virus detection are also elaborated upon.

Indexed as

Biosensing TechniquesCOVID-19VirusesHumansNucleic Acid Amplification TechniquesPandemicsPoint-of-Care Systemsamperometryelectrochemical biosensorselectrochemical impedance spectroscopypotentiostatRT-LAMPvirus detectionvoltammetry

Identifiers

PMID36005062
PMCPMC9406062
OpenAlexW4292582675

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.