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Review
. 2021 Jun:20:100443.
doi: 10.1016/j.mtchem.2021.100443. Epub 2021 Feb 11.

The perspectives of biomarker-based electrochemical immunosensors, artificial intelligence and the Internet of Medical Things toward COVID-19 diagnosis and management

Affiliations
Review

The perspectives of biomarker-based electrochemical immunosensors, artificial intelligence and the Internet of Medical Things toward COVID-19 diagnosis and management

A K Yadav et al. Mater Today Chem. 2021 Jun.

Abstract

The World Health Organization (WHO) has declared the COVID-19 an international health emergency due to the severity of infection progression, which became more severe due to its continuous spread globally and the unavailability of appropriate therapy and diagnostics systems. Thus, there is a need for efficient devices to detect SARS-CoV-2 infection at an early stage. Nowadays, the reverse transcription polymerase chain reaction (RT-PCR) technique is being applied for detecting this virus around the globe; however, factors such as stringent expertise, long diagnostic times, invasive and painful screening, and high costs have restricted the use of RT-PCR methods for rapid diagnostics. Therefore, the development of cost-effective, portable, sensitive, prompt and selective sensing systems to detect SARS-CoV-2 in biofluids at fM/pM/nM concentrations would be a breakthrough in diagnostics. Immunosensors that show increased specificity and sensitivity are considerably fast and do not imply costly reagents or instruments, reducing the cost for COVID-19 detection. The current developments in immunosensors perhaps signify the most significant opportunity for a rapid assay to detect COVID-19, without the need of highly skilled professionals and specialized tools to interpret results. Artificial intelligence (AI) and the Internet of Medical Things (IoMT) can also be equipped with this immunosensing approach to investigate useful networking through database management, sharing, and analytics to prevent and manage COVID-19. Herein, we represent the collective concepts of biomarker-based immunosensors along with AI and IoMT as smart sensing strategies with bioinformatics approach to monitor non-invasive early stage SARS-CoV-2 development, with fast point-of-care (POC) diagnostics as the crucial goal. This approach should be implemented quickly and verified practicality for clinical samples before being set in the present times for mass-diagnostic research.

Keywords: Diagnostics; Electrochemical biosensors; Pandemic; Point-of-care; SARS-CoV-2.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
This map shows the global distribution of confirmed cases of COVID-19. (This was reproduced from situation reports of the WHO coronavirus disease [4]. Copyright 2020 WHO.)
Fig. 2
Fig. 2
The systemic and respiratory disorders caused by the SARS-CoV-2. The incubation period of COVID-19 infection is approximately 5.2 days. COVID-19 showed some unique clinical features that include the targeting of the lower airway, as evidenced by upper respiratory tract symptoms like rhinorrhea, sneezing, and sore throat. Additionally, patients infected with COVID-19 developed intestinal symptoms like diarrhea (adapted with permission from Ref. [19], Copyright 2020 Elsevier).
Fig. 3
Fig. 3
[A]. A schematic structural view of human SARS-CoV-2 with its surface protein (adapted with permission from Ref. [22], Copyright 2020 Cleveland Clinic [B]. Visualization of SARS-CoV-2 with transmission electron microscopy. The virus is shown in blue color (adapted with permission from Ref. [23], Copyright 2020 American chemical society) [C]. The life cycle of highly pathogenic human SARS-CoV-2. These CoVs enter host cells by first binding to their respective cellular receptors [angiotensin-converting enzyme 2(ACE2) for the severe acute respiratory syndrome (SARS)-CoV-2 or SARS-CoV and dipeptidyl peptidase 4 (DPP4) for the Middle East respiratory syndrome (MERS)-CoV] on the membranes of host cells expressing ACE2 (e.g., pneumocytes, enterocytes) or DPP4 (e.g. liver or lung cells including Huh-7, MRC-5, and Calu-3) via the surface spike (S) protein, which mediates virus–cell membrane fusion and viral entry. Viral genomic RNA is released and translated into viral polymerase proteins. The negative (−) sense genomic RNA is synthesized and used as a template to form subgenomic or genomic positive (+) sense RNA. Viral RNA and nucleocapsid (N) structural protein is replicated, transcribed, or synthesized in the cytoplasm. By contrast, other viral structural proteins, including S, membrane (M), and envelope (E), are transcribed, then translated in the endoplasmic reticulum (ER) and transported to the Golgi. The viral RNA–N complex and S, M, and E proteins are further assembled in the ER–Golgi intermediate compartment (ERGIC) to form a mature virion, then released from host cells (adapted with permission from Ref. [34], Copyright 2020 Cell Press).
Fig. 4
Fig. 4
A generalized representation of all different areas of conventional detection methods for viruses.
Fig. 5
Fig. 5
The effect of rapid detection of infectious diseases in preventing and controlling an outbreak (adapted with permission from Ref. [70], Copyright 2020 Elsevier).
Fig. 6
Fig. 6
[1] Transverse thin-section CT scans in patients with COVID-19 disease (A) 56-year-old man, Day 3 after symptom onset, (B) 74-year-old woman, Day 10 after symptom onset, (C) 61-year-old woman, Day 20 after symptom onset (D) 63-year-old woman, Day 17 after symptom onset (adapted with permission from Ref. [88], Copyright 2020 The Lancet) [2]. Contact tracing application: using GPS contacts of Individual A and all individuals using the app, infections are traced. This is further supplemented by scanning QR codes displayed on high-traffic public amenities where GPS is too coarse. Using this application, Individual A requests a test for COVID-19 infection, and their positive test result is shared as an instant notification to individuals who have been in close contact (adapted with permission from Ref. [23], Copyright 2020 American chemical society).
Fig. 7
Fig. 7
[A] A schematic presentation of the principle of a biosensor (reproduced from Ref. [105]); [B] Features of an ideal biosensor required to be developed for practical use in pandemics (adapted with permission from Ref. [23], Copyright 2020 American chemical society).
Fig. 8
Fig. 8
[1] Detection of SARS-CoV-2 using field-effect transistors: The schematic shows collection of biological samples from the patient and its application on the graphene-based sensing area of the FET biosensor. The sensor in real time can capture binding events associated with the SAR-CoV-2 (adapted with permission from Ref. [23], Copyright 2020 American chemical society) [2]. LSPR detection of nucleic acid sequences from the SARS-CoV2. The schematic shows the architecture of the LSPR substrate consisting of gold nanoparticles in which light is illuminated on the substrate to generate local heat and detect nucleic acid-binding events. The graph also shows the LSPR response to their plasmonic effect and the detection of nucleic acid sequences at low concentrations (adapted with permission from Ref. [23], Copyright 2020 American chemical society) [3]. Home Test for COVID-19. (A) Visual detection of COVID-19 with one-tube Penn-RAMP with LCV dye. Negative: 0 copies of COVID-19 synthesized DNA; positive: 100 copies of synthesized DNA. (B) The sequence of operations of the home test. The reactions can be incubated either in a block heater or in a domestic oven with temperature control (adapted with permission from Ref. [84], Copyright 2020 Elsevier) [4]. Schematic representation for the selective naked-eye detection of SARS-CoV-2 RNA-mediated by the suitably designed ASO-capped AuNPs (adapted with permission from Ref. [109], Copyright 2020 American chemical society).
Fig. 9
Fig. 9
[1]. Schematic illustration of the microfluidics-integrated electrochemical immunosensing chip coated with RGO, followed by antibody immobilization using EDC/NHS coupling to detect influenza virus H1N1 (adapted with permission from Ref. [102], Copyright 2017 Nature) [2]. Schematic illustration of the biosensing system. (A) Throat swab culture acquisition. (B) Boron-doped diamond electrode surface modification with polyclonal anti-M1 antibodies that can identify the universal biomarker for influenza virus, the M1 protein (adapted with permission from Ref. [162], Copyright 2017 Nature).
Scheme 1
Scheme 1
A schematic illustration of the hypothetical workflow of nano-enabled biomarker-based immunosensor for COVID-19 detection. A conducting microelectrode can be modified with various nanostructures for high loading of SARS-CoV-2-specific antibodies to detect SARS-CoV-2 proteins or multiple biomarkers at picomolar/nanomolar concentrations using an appropriate electrochemical transduction technique.
Fig. 10
Fig. 10
Use of machine learning and artificial intelligence in battle against COVID-19 (adapted with permission from Ref. [207], Copyright©2020 the American Physiological Society).
Fig. 11
Fig. 11
[A] Role of IoMT in various major areas to tackle COVID-19. [B] Schematic for combating COVID-19 with combined implementation of POC diagnostics and the IoMT (adapted with permission from Ref. [190], Copyright©2020 Elsevier).
Fig. 12
Fig. 12
Rapid turnaround of results with POCT compared to the current testing procedure for COVID-19 detection (adapted with permission from Ref. [169], Copyright 2015 Scimago).

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