Evidence map›Paper›PMID 34562926›Full record

ReviewBiosensors2021

Recent Advances in Electrochemical Biosensors: Applications, Challenges, and Future Scope.

Anoop Singh, Asha Sharma, Aamir Ahmed, Ashok K Sundramoorthy, Hidemitsu Furukawa, Sandeep Arya, Ajit Khosla

Abstract readReview
In one paragraph

Review in Biosensors, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 141 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
141citing papers in PubMed, 1 pooled it
–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

141 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Review
  4. Review
  5. Review
  6. Article
  7. From conventional to biosensor-based detection ofFood and waterborne parasitology · 2026
    Review
  8. Review
  9. Review
  10. Review
  11. Biomaterials for biomarker imaging and detection.Journal of advanced research · 2026
    Review
  12. Review
  13. Article
  14. Review
  15. Article
  16. Review
  17. Article
  18. Article
  19. Review
  20. Review

81 more citing papers are in PubMed but not listed here.

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.

Anoop SinghDepartment of Physics, University of Jammu, Jammu 180006, India.
Asha SharmaDepartment of Physics, University of Jammu, Jammu 180006, India.
Aamir AhmedDepartment of Physics, University of Jammu, Jammu 180006, India.ORCID 0000-0002-0980-5134
Ashok K SundramoorthyDepartment of Chemistry, SRM Institute of Science and Technology, Kattankulathur 603203, India.ORCID 0000-0002-8512-9393
Hidemitsu FurukawaDepartment of Mechanical System Engineering, Graduate School of Science and Engineering, Yamagata University, Yamagata 992-8510, Japan.ORCID 0000-0002-0778-3330
Sandeep AryaDepartment of Physics, University of Jammu, Jammu 180006, India.
Ajit KhoslaDepartment of Mechanical System Engineering, Graduate School of Science and Engineering, Yamagata University, Yamagata 992-8510, Japan.

Funding

Japan Society for the Promotion of Science JP17H01224, JP18H0547, JP19H01122
6 · The paper itself

Abstract

The electrochemical biosensors are a class of biosensors which convert biological information such as analyte concentration that is a biological recognition element (biochemical receptor) into current or voltage. Electrochemical biosensors depict propitious diagnostic technology which can detect biomarkers in body fluids such as sweat, blood, feces, or urine. Combinations of suitable immobilization techniques with effective transducers give rise to an efficient biosensor. They have been employed in the food industry, medical sciences, defense, studying plant biology, etc. While sensing complex structures and entities, a large data is obtained, and it becomes difficult to manually interpret all the data. Machine learning helps in interpreting large sensing data. In the case of biosensors, the presence of impurity affects the performance of the sensor and machine learning helps in removing signals obtained from the contaminants to obtain a high sensitivity. In this review, we discuss different types of biosensors along with their applications and the benefits of machine learning. This is followed by a discussion on the challenges, missing gaps in the knowledge, and solutions in the field of electrochemical biosensors. This review aims to serve as a valuable resource for scientists and engineers entering the interdisciplinary field of electrochemical biosensors. Furthermore, this review provides insight into the type of electrochemical biosensors, their applications, the importance of machine learning (ML) in biosensing, and challenges and future outlook.

Indexed as

Biosensing TechniquesBiomarkersElectrochemical TechniquesMachine LearningBiomarkersamperometricbiosensorelectrochemicalfood quality monitoringmachine learningsensitivityvoltammetric

Identifiers

PMID34562926
PMCPMC8472208

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.