Internet of medical things (IoMT)-integrated biosensors for point-of-care testing of infectious diseases

Abstract

On global scale, the current situation of pandemic is symptomatic of increased incidences of contagious diseases caused by pathogens. The faster spread of these diseases, in a moderately short timeframe, is threatening the overall population wellbeing and conceivably the economy. The inadequacy of conventional diagnostic tools in terms of time consuming and complex laboratory-based diagnosis process is a major challenge to medical care. In present era, the development of point-of-care testing (POCT) is in demand for fast detection of infectious diseases along with "on-site" results that are helpful in timely and early action for better treatment. In addition, POCT devices also play a crucial role in preventing the transmission of infectious diseases by offering real-time testing and lab quality microbial diagnosis within minutes. Timely diagnosis and further treatment optimization facilitate the containment of outbreaks of infectious diseases. Presently, efforts are being made to support such POCT by the technological development in the field of internet of medical things (IoMT). The IoMT offers wireless-based operation and connectivity of POCT devices with health expert and medical centre. In this review, the recently developed POC diagnostics integrated or future possibilities of integration with IoMT are discussed with focus on emerging and re-emerging infectious diseases like malaria, dengue fever, influenza A (H1N1), human papilloma virus (HPV), Ebola virus disease (EVD), Zika virus (ZIKV), and coronavirus (COVID-19). The IoMT-assisted POCT systems are capable enough to fill the gap between bioinformatics generation, big rapid analytics, and clinical validation. An optimized IoMT-assisted POCT will be useful in understanding the diseases progression, treatment decision, and evaluation of efficacy of prescribed therapy.

Keywords: Artificial intelligence; Biosensor; Infectious diseases; Intelligent healthcare; Internet of medical things; Point-of-care-testing.

Fig. 1

Timeline of emerging infectious diseases.

Fig. 2

Schematic representation of different factors determining the higher incidence of infectious diseases.

Fig. 3

Schematic representation of next generation biosensing, AI and IoT to explore POCT diagnostics to manage a targeted infectious disease.

Fig. 4

Schematic illustration of different POC tools to promote patient self-testing in resource-limited settings.

Fig. 5

Illustration of the impact of IoMT on patient health management via connecting various steps or gaps associated POCT including diagnostics, bioinformatics collection, date sharing, rapid analysis, and timely therapy decision.

Fig. 6

Schematic diagrams illustrating the recently developed POC settings for detection of malarial parasites. Reprinted with permission from ref (Choi et al., 2016; Ragavan et al., 2018; Singh et al., 2021).

Fig. 7

A) Illustration of non-optical multiplex PCR system (consisting a washing buffer component, microfluidic platform, and a paper based nucleic acid sensor) developed for DENV diagnostics. B) an integrated QCM and electrochemical sensing based DENV diagnostics system.

Fig. 8

A) Illustration of integrated self-driven microfluidic system to detect H1N1 virus based on reverse transcription loop-mediated isothermal amplification (Y. D. Ma et al., 2019). B) presentation of a self-enzyme chemiluminescence based immunoassay for rapid detection of H1N1 virus (Kyme et al., 2019). C) A dual channel glycin functionalized FET based biosensor developed for selective identification of pandemic influenza viral particles (Hideshima et al., 2019).

Fig. 9

A) Schematic representation of DNA-programming multicolor silver nanoclusters for detection of two HIV DNAs simultaneously. Reprinted with permission from Ref (Zou et al., 2019). B) A nano-enabled photoelectrochemical (PEC) biosensor for HIV-1 virus detection (Wang et al., 2019). C) Lab-on-a-chip approach to evaluate electrophysiology of cell during HIV infection and treatment using a Food and Drug Administration (FDA)-approved drug.

Fig. 10

A) HPV POCT screening reduced progression to different cancer and mortality. B) illustration of a CIALFB bioassay developed using CRISPR/Cas12a for the selective detection of HPV 18 and HPV 18 (Mukama et al., 2020). C) A Zn-dopped MoS2 QD based ECL biosensor developed for detecting HPV-16 DNA (Nie et al., 2020).

Fig. 11

Microscopic illustration of EBOV (A-C; Source NIAID of NIH) and strategy (D) along with its impact to manage EVD outbreak. It is predicted that use of advanced POCT approach, EBOV infection can be managed to reduce mortality via diseases management approach. E) illustration of a IoMT-assisted biosensing system developed for POCT of IgG antibodies for selective diagnostics of EBOV (Brangel et al., 2018). F) illustration of AgNPs of multicolor feature developed for multiplexed diagnostics of dengue fever, yellow fever, and EVD (Yen et al., 2015). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 12

A) Illustration of ZIKV protein (Cyro-TEM imaging a & b), B) interdigitated electrode and electrochemical immunosensing approach for detecting ZIKV protein at 10 pM level, C) a strategy to develop a miniaturized sensing system to POCT of ZIKV protein needed for personalized ZIKV infection in personalized manner.

Fig. 13

A) Illustration of SARS-CoV-2 virus structure sites useful for targeting to achieve COVID-19 diagnostics, B) schematic presentation of usefulness of using mask to avoid human-to-human SARS-CoV-2 virus transmission, C) proposed role of AI to manage COVID-19 infection successfully, D) electrochemical immunosensing of SARS-CoV-2 virus protein at pM level needed for selective diagnostics at early-stage, E) projected nano-enabled COVID-19 infection diagnostics supported by AI and IoMT.

Fig. 14

A) Au-Island based nano-system for selective detection of SARS-CoV-2 virus detection based on stimuli-responsive of plasmonic-photothermal effect. B) graphene modified FET immunosensor to detect SARS-CoV-2 virus protein, C) Nano-enabled LFA for detection of SARS-CoV-2 protein selectively within 10 min, D) toroidal plasmonic metasensors (magneto-plasmonic) for the detection of SARS-CoV-2 virus at femtomolar level (Ahmadivand et al., 2021).

Fig. 15

A) illustration of magnetic bead based electrochemical SARS-CoV-2 immunosensor for the selective detection of S and N terminal of virus protein (Fabiani et al., 2021). B) Schematic presentation of a graphene-based electrochemical sensing platform for detecting SARS-CoV-2 virus (projected as telemedicine platform i.e., SARS-CoV-2 RapidPlex) in saliva and blood, a) detection platform (with reference antibodies (IgG and IgM), and inflammatory biomarker C-reactive protein) and wirelessly data sharing, b) laser-engraved graphene sensor arrays, c) disposable and flexible graphene array, d) sensor array connected with a circuit board needed for signal processing and wireless communication (Torrente-Rodríguez et al., 2020).

Fig. 16

Illustration of a combinational diagnostics approach consisting a nano-sensor, AI and IoMT projected for intelligent healthcare management.