Evidence map›Paper›PMID 36850837›Full record

ReviewSensors (Basel, Switzerland)2023

Smart Graphene-Based Electrochemical Nanobiosensor for Clinical Diagnosis: Review.

Irkham Irkham, Abdullahi Umar Ibrahim, Pwadubashiyi Coston Pwavodi, Fadi Al-Turjman, Yeni Wahyuni Hartati

Open access · goldAbstract readReview
In one paragraph

Review in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
10.5field-weighted citation impact, top 1% 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

17 citing papers in PubMed, 68 citations in OpenAlex.

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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

5 authors at 3 institutions in 2 countries.

Irkham IrkhamDepartment of Chemistry, Faculty of Mathematics and Natural Sciences, Padjadjaran University, Bandung 40173, Indonesia.ORCID 0000-0001-9938-2931
Abdullahi Umar IbrahimDepartment of Biomedical Engineering, Near East University, Mersin 10, Nicosia 99010, Turkey.ORCID 0000-0003-3850-9921
Pwadubashiyi Coston PwavodiDepartment of Bioengineering/Biomedical Engineering, Faculty of Engineering, Cyprus International University, Haspolat, North Cyprus via Mersin 10, Nicosia 99010, Turkey.
Fadi Al-TurjmanResearch Center for AI and IoT, Faculty of Engineering, University of Kyrenia, Mersin 10, Kyrenia 99320, Turkey.ORCID 0000-0001-6375-4123
Yeni Wahyuni HartatiDepartment of Chemistry, Faculty of Mathematics and Natural Sciences, Padjadjaran University, Bandung 40173, Indonesia.ORCID 0000-0003-1463-6352
Near East University · CYPadjadjaran University · IDCyprus International University · CY

Funding

Padjadjaran University 2203/UN6.3.1/PT.00/2022
6 · The paper itself

Abstract

The technological improvement in the field of physics, chemistry, electronics, nanotechnology, biology, and molecular biology has contributed to the development of various electrochemical biosensors with a broad range of applications in healthcare settings, food control and monitoring, and environmental monitoring. In the past, conventional biosensors that have employed bioreceptors, such as enzymes, antibodies, Nucleic Acid (NA), etc., and used different transduction methods such as optical, thermal, electrochemical, electrical and magnetic detection, have been developed. Yet, with all the progresses made so far, these biosensors are clouded with many challenges, such as interference with undesirable compound, low sensitivity, specificity, selectivity, and longer processing time. In order to address these challenges, there is high need for developing novel, fast, highly sensitive biosensors with high accuracy and specificity. Scientists explore these gaps by incorporating nanoparticles (NPs) and nanocomposites (NCs) to enhance the desired properties. Graphene nanostructures have emerged as one of the ideal materials for biosensing technology due to their excellent dispersity, ease of functionalization, physiochemical properties, optical properties, good electrical conductivity, etc. The Integration of the Internet of Medical Things (IoMT) in the development of biosensors has the potential to improve diagnosis and treatment of diseases through early diagnosis and on time monitoring. The outcome of this comprehensive review will be useful to understand the significant role of graphene-based electrochemical biosensor integrated with Artificial Intelligence AI and IoMT for clinical diagnostics. The review is further extended to cover open research issues and future aspects of biosensing technology for diagnosis and management of clinical diseases and performance evaluation based on Linear Range (LR) and Limit of Detection (LOD) within the ranges of Micromolar µM (10

Indexed as

Artificial IntelligenceGraphiteAntibodiesElectric ConductivityElectricityAntibodiesGraphiteartificial intelligencebiosensorselectrochemical sensorsgrapheneInternet of Medical Things (IoMT)nanocompositesnanoparticles

Identifiers

PMID36850837
PMCPMC9964617
OpenAlexW4321237001

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.