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

Detection strategies of infectious diseases via peptide-based electrochemical biosensors

Infectious diseases have threatened human life for as long as humankind has existed. One of the most crucial aspects of fighting against these infections is diagnosis to prevent disease spread. However, traditional diagnostic methods prove insufficient and time-consuming in the face of a pandemic. Therefore, studies focusing on detecting viruses causing these diseases have increased, with a particular emphasis on developing rapid, accurate, specific, user-friendly, and portable electrochemical biosensor systems. Peptides are used integral components in biosensor fabrication for several reasons, including various and adaptable synthesis protocols, long-term stability, and specificity. Here, we discuss peptide-based electrochemical biosensor systems that have been developed over the last decade for the detection of infectious diseases. In contrast to other reports on peptide-based biosensors, we have emphasized the following points i) the synthesis methods of peptides for biosensor applications, ii) biosensor fabrication approaches of peptide-based electrochemical biosensor systems, iii) the comparison of electrochemical biosensors with other peptide-based biosensor systems and the advantages and limitations of electrochemical biosensors, iv) the pros and cons of peptides compared to other biorecognition molecules in the detection of infectious diseases, v) different perspectives for future studies with the shortcomings of the systems developed in the past decade.

One-base-mismatch CRISPR-based transistors for single nucleotide resolution assay

An effective strategy for accurately detecting single nucleotide variants (SNVs) is of great significance for genetic research and diagnostics. However, strict amplification conditions, complex experimental instruments, and specialized personnel are required to obtain a satisfactory tradeoff between sensitivity and selectivity for SNV discrimination. In this study, we present a CRISPR-based transistor biosensor for the rapid and highly selective detection of SNVs in viral RNA. By introducing a synthetic mismatch in the crRNA, the CRISPR-Cas13a protein can be engineered to capture the target SNV RNA directly on the surface of the graphene channel. This process induces a fast electrical signal response in the transistor, obviating the need for amplification or reporter molecules. The biosensor exhibits a detection limit for target RNA as low as 5 copies in 100 μL, which is comparable to that of real-time quantitative polymerase chain reaction (PCR). Its operational range spans from 10 to 5 × 105 copy mL-1 in artificial saliva solution. This capability enables the biosensor to discriminate between wild-type and SNV RNA within 15 min. By introducing 10 μL of swab samples during clinical testing, the biosensor provides specific detection of respiratory viruses in 19 oropharyngeal specimens, including influenza A, influenza B, and variants of SARS-CoV-2. This study emphasizes the CRISPR-transistor technique as a highly accurate and sensitive approach for field-deployable nucleic acid screening or diagnostics.

Optical Image Sensors for Smart Analytical Chemiluminescence Biosensors

Optical biosensors have emerged as a powerful tool in analytical biochemistry, offering high sensitivity and specificity in the detection of various biomolecules. This article explores the advancements in the integration of optical biosensors with microfluidic technologies, creating lab-on-a-chip (LOC) platforms that enable rapid, efficient, and miniaturized analysis at the point of need. These LOC platforms leverage optical phenomena such as chemiluminescence and electrochemiluminescence to achieve real-time detection and quantification of analytes, making them ideal for applications in medical diagnostics, environmental monitoring, and food safety. Various optical detectors used for detecting chemiluminescence are reviewed, including single-point detectors such as photomultiplier tubes (PMT) and avalanche photodiodes (APD), and pixelated detectors such as charge-coupled devices (CCD) and complementary metal-oxide-semiconductor (CMOS) sensors. A significant advancement discussed in this review is the integration of optical biosensors with pixelated image sensors, particularly CMOS image sensors. These sensors provide numerous advantages over traditional single-point detectors, including high-resolution imaging, spatially resolved measurements, and the ability to simultaneously detect multiple analytes. Their compact size, low power consumption, and cost-effectiveness further enhance their suitability for portable and point-of-care diagnostic devices. In the future, the integration of machine learning algorithms with these technologies promises to enhance data analysis and interpretation, driving the development of more sophisticated, efficient, and accessible diagnostic tools for diverse applications.

High-Throughput and Integrated CRISPR/Cas12a-Based Molecular Diagnosis Using a Deep Learning Enabled Microfluidic System

CRISPR/Cas-based molecular diagnosis demonstrates potent potential for sensitive and rapid pathogen detection, notably in SARS-CoV-2 diagnosis and mutation tracking. Yet, a major hurdle hindering widespread practical use is its restricted throughput, limited integration, and complex reagent preparation. Here, a system, microfluidic multiplate-based ultrahigh throughput analysis of SARS-CoV-2 variants of concern using CRISPR/Cas12a and nonextraction RT-LAMP (mutaSCAN), is proposed for rapid detection of SARS-CoV-2 and its variants with limited resource requirements. With the aid of the self-developed reagents and deep-learning enabled prototype device, our mutaSCAN system can detect SARS-CoV-2 in mock swab samples below 30 min as low as 250 copies/mL with the throughput up to 96 per round. Clinical specimens were tested with this system, the accuracy for routine and mutation testing (22 wildtype samples, 26 mutational samples) was 98% and 100%, respectively. No false-positive results were found for negative (n = 24) samples.

Panoramic view of artificial fruit ripening agents sensing technologies and the exigency of developing smart, rapid, and portable detection devices: A review

Recently, the availability of point-of-care sensor systems has led to the rapid development of smart and portable devices for the detection of hazardous analytes. The rapid flow of artificially ripened fruits into the market is associated with an elevated risk to human life, agriculture, and the ecosystem due to the use of artificial fruit ripening agents (AFRAs). Accordingly, there is a need for the development of "Point-of-care Sensors" to detect AFRAs due to several advantages, such as simple operation, promising detection mechanism, higher selectivity and sensitivity, compact, and portable. Traditional detection approaches are time-consuming and inappropriate for on-the-spot analyses. Presented comprehensive review aimed to reveal how such technology has systematically evolved over time (through conventional, advanced, and portable smart techniques) detection detect AFRA, till date. Moreover, focuses and highlights a framework of initiatives undertaken for technological advancements in the development of smart the portable detection techniques (kits) for the onsite detection of AFRAs in fruits with in-depth discussion over sensing mechanism and analytical performance of the sensing technology. Notably, colorimetric detection methods have the greatest potential for real-time monitoring of AFRA and its residues because they are easy to assemble, have a high level of selectivity and sensitivity, and can be read by the human eye independently. This study sought to differentiate between traditional credible strategies by presenting new prospects, perceptions, and challenges related to portable devices. This review provides systematic framework of advances in portable field recognition strategies for the on-spot AFRA detection in fruits and critical information for development of new paper-based portable sensors for fruit diagnostic sectors.

Bioelectrochemical biosensors for water quality assessment and wastewater monitoring

Bioelectrochemical biosensors offer a promising approach for real-time monitoring of industrial bioprocesses. Many bioelectrochemical biosensors do not require additional labelling reagents for target molecules. This simplifies the monitoring process, reduces costs, and minimizes potential contamination risks. Advancements in materials science and microfabrication technologies are paving the way for smaller, more portable bioelectrochemical biosensors. This opens doors for integration into existing bioprocessing equipment and facilitates on-site, real-time monitoring capabilities. Biosensors can be designed to detect specific heavy metals such as lead, mercury, or chromium in wastewater. Early detection allows for the implementation of appropriate removal techniques before they reach the environment. Despite these challenges, bioelectrochemical biosensors offer a significant leap forward in wastewater monitoring. As research continues to improve their robustness, selectivity, and cost-effectiveness, they have the potential to become a cornerstone of efficient and sustainable wastewater treatment practices.

An integrated electronic tag-based vertical flow assay (e-VFA) with micro-sieve and AlGaN/GaN HEMT sensors for multi-target detection in actual saliva

Vertical flow assay (VFA) is an effective point-of-care (POC) diagnostic tool for widespread application. Nevertheless, the lack of multi-target detection and multi-signal readout capability still remains a challenge. Herein, a brand new VFA scheme for multi-target saliva detection based on electronic tags was proposed, where AlGaN/GaN HEMT sensors modified with different bio-receptors as electronic tags endowed the VFA with multi-target detection capability. In addition, the use of electronic tags instead of optical tags allowed the VFA to simultaneously carry out direct multi-target readouts, which ensure effective POC diagnostics for saliva analysis. Moreover, by integrating a hydrophilically optimized micro-sieve, impurities like sticky filaments, epidermal cells and other large-scale charged particles in saliva were effectively screened, which enabled the direct detection of saliva using AlGaN/GaN HEMT sensors. Glucose, urea, and cortisol were selected to verify the feasibility of the multi-target e-VFA scheme, and the results showed that the limit of detection (LOD) was as low as 100 aM. The linear response was demonstrated in the dynamic range of 100 aM to 100 μM, and the specificity, long-term stability and validity of the actual saliva test were also verified. These results demonstrated that the as-proposed e-VFA has potential for application in saliva detection for simultaneous multi-target detection, and it is expected to achieve the real-time detection of more biological targets in saliva.

Beyond diagnostic connectivity: Leveraging digital health technology for the real-time collection and provision of high-quality actionable data on infectious diseases in Uganda

Automated data transmission from diagnostic instrument networks to a central database at the Ministries of Health has the potential of providing real-time quality data not only on diagnostic instrument performance, but also continuous disease surveillance and patient care. We aimed at sharing how a locally developed novel diagnostic connectivity solution channels actionable data from diagnostic instruments to the national dashboards for disease control in Uganda between May 2022 and May 2023. The diagnostic connectivity solution was successfully configured on a selected network of multiplexing diagnostic instruments at 260 sites in Uganda, providing a layered access of data. Of these, 909,674 test results were automatically collected from 269 "GeneXpert" machines, 5597 test results from 28 "Truenat" and >12,000 were from 3 digital x-ray devices to different stakeholder levels to ensure optimal use of data for their intended purpose. The government and relevant stakeholders are empowered with usable and actionable data from the diagnostic instruments. The successful implementation of the diagnostic connectivity solution depended on some key operational strategies namely; sustained internet connectivity and short message services, stakeholder engagement, a strong in-country laboratory coordination network, human resource capacity building, establishing a network for the diagnostic instruments, and integration with existing health data collection tools. Poor bandwidth at some locations was a major hindrance for the successful implementation of the connectivity solution. Maintaining stakeholder engagement at the clinical level is key for sustaining diagnostic data connectivity. The locally developed diagnostic connectivity solution as a digital health technology offers the chance to collect high-quality data on a number of parameters for disease control, including error analysis, thereby strengthening the quality of data from the networked diagnostic sites to relevant stakeholders.

Adjunctive Testing Using Biospectral Emission Sequencing: Bioregulatory Intelligence Technology in Parallel With the Goals of Artificial Intelligence in Medicine

The many advancements in medical technology of the last century have continually sought to improve the sensitivity of testing and the specificity of treatment of human maladies. Conventional physical and pharmaceutical treatment is largely an imprecise process, stimulating the impetus for the advancement of machine learning-enhanced artificial intelligence (AI) medical technologies. Biospectral Emission Sequencing (BES) is a bioregulatory intelligence (BI) technology already in use as an adjunct to conventional testing. Biospectral Emission Sequencing provides a functional system of dynamic real-time adjunctive testing and treatment selection. This paper discusses the parallel technologies of present and future AI and BI technologies in medicine.

A Highly Sensitive Dual-Drive Microfluidic Device for Multiplexed Detection of Respiratory Virus Antigens

Conventional microfluidic systems that rely on capillary force have a fixed structure and limited sensitivity, which cannot meet the demands of clinical applications. Herein, we propose a dual-drive microfluidic device for sensitive and flexible detection of multiple pathogenic microorganisms antigens/antibodies. The device comprises a portable microfluidic analyzer and a dual-drive microfluidic chip. Along with capillary force, a second active driving force is provided by a removable self-driving valve in the waste chamber. The interval between these two driving forces can be adjusted to control the reaction time in the microchannel, optimizing the formation of antigen-antibody complexes and enhancing sensitivity. Moreover, the material used in the self-driving valve can be changed to adjust the active force strength needed for different tests. The device offers quantitative analysis for respiratory syncytial virus antigen and SARS-CoV-2 antigen using a 35 μL sample, delivering results within 5 min. The detection limits of the system were 1.121 ng/mL and 0.447 ng/mL for respiratory syncytial virus recombinant fusion protein and SARS-CoV-2 recombinant nucleoprotein, respectively. Although the dual-drive microfluidic device has been used for immunoassay for respiratory syncytial virus and SARS-CoV-2 in this study, it can be easily adapted to other immunoassay applications by changing the critical reagents.

Fluorophore and nanozyme-functionalized DNA walking: A dual-mode DNA logic biocomputing platform for microRNA sensing in clinical samples

Inspired by the programmability and modifiability of nucleic acids, point-of-care (POC) diagnostics for nucleic acid target detection is evolving to become more diversified and intelligent. In this study, we introduce a fluorescent and photothermal dual-mode logic biosensing platform that integrates catalytic hairpin assembly (CHA), toehold-mediated stand displacement reaction (SDR) and a DNA walking machine. Dual identification and signal reporting modules are incorporated into DNA circuits, orchestrated by an AND Boolean logic gate operator and magnetic beads (MBs). In the presence of bispecific microRNAs (miRNAs), the AND logic gate activates, driving the DNA walking machine, and facilitating the collection of hairpin DNA stands modified with FAM fluorescent group and CeO2@Au nanoparticles. The CeO2@Au nanoparticles, served as a nanozyme, can oxidize TMB into oxidation TMB (TMBox), enabling a near-infrared (NIR) laser-driven photothermal effect following the magnetic separation of MBs. This versatile platform was employed to differentiate between plasma samples from breast cancer patients, lung cancer patients, and healthy donors. The thermometer-readout transducers, derived from the CeO2@Au@DNA complexes, provided reliable results, further corroborated by fluorescence assays, enhancing the confidence in the diagnostics compared to singular detection method. The dual-mode logic biosensor can be easily customized to various nucleic acid biomarkers and other POC signal readout modalities by adjusting recognition sequences and modification strategies, heralding a promising future in the development of intelligent, flexible diagnostics for POC testing.

Membrane-Free Lateral Flow Assay with the Active Control of Fluid Transport for Ultrasensitive Cardiac Biomarker Detection

Membrane-based lateral flow immunoassays (LFAs) have been employed as early point-of-care (POC) testing tools in clinical settings. However, the varying membrane properties, uncontrollable sample transport in LFAs, visual readout, and required large sample volumes have been major limiting factors in realizing needed sensitivity and desirable precise quantification. Addressing these challenges, we designed a membrane-free system in which the desirable three-dimensional (3D) structure of the detection zone is imitated and used a small pump for fluid flow and fluorescence as readout, all the while maintaining a one-step assay protocol. A hydrogel-like protein-polyelectrolyte complex (PPC) within a polyelectrolyte multilayer (PEM) was developed as the test line by complexing polystreptavidin (pSA) with poly(diallyldimethylammonium chloride) (PDDA), which in turn was layered with poly(acrylic acid) (PAA) resulting in a superior 3D streptavidin-rich test line. Since the remainder of the microchannel remains material-free, good flow control is achieved, and with the total volume of 20 μL, 7.5-fold smaller sample volumes can be used in comparison to conventional LFAs. High sensitivity with desirable reproducibility and a 20 min total assay time were achieved for the detection of NT-proBNP in plasma with a dynamic range of 60-9000 pg·mL-1 and a limit of detection of 56 pg·mL-1 using probe antibody-modified fluorescence nanoparticles. While instrument-free visual detection is no longer possible, the developed lateral flow channel platform has the potential to dramatically expand the LFA applicability, as it overcomes the limitations of membrane-based immunoassays, ultimately improving the accuracy and reducing the sample volume so that finger-prick analyses can easily be done in a one-step assay for analytes present at very low concentrations.

Engineered two-dimensional nanomaterials based diagnostics integrated with internet of medical things (IoMT) for COVID-19

More than four years have passed since an inimitable coronavirus disease (COVID-19) pandemic hit the globe in 2019 after an uncontrolled transmission of the severe acute respiratory syndrome (SARS-CoV-2) infection. The occurrence of this highly contagious respiratory infectious disease led to chaos and mortality all over the world. The peak paradigm shift of the researchers was inclined towards the accurate and rapid detection of diseases. Since 2019, there has been a boost in the diagnostics of COVID-19 via numerous conventional diagnostic tools like RT-PCR, ELISA, etc., and advanced biosensing kits like LFIA, etc. For the same reason, the use of nanotechnology and two-dimensional nanomaterials (2DNMs) has aided in the fabrication of efficient diagnostic tools to combat COVID-19. This article discusses the engineering techniques utilized for fabricating chemically active E2DNMs that are exceptionally thin and irregular. The techniques encompass the introduction of heteroatoms, intercalation of ions, and the design of strain and defects. E2DNMs possess unique characteristics, including a substantial surface area and controllable electrical, optical, and bioactive properties. These characteristics enable the development of sophisticated diagnostic platforms for real-time biosensors with exceptional sensitivity in detecting SARS-CoV-2. Integrating the Internet of Medical Things (IoMT) with these E2DNMs-based advanced diagnostics has led to the development of portable, real-time, scalable, more accurate, and cost-effective SARS-CoV-2 diagnostic platforms. These diagnostic platforms have the potential to revolutionize SARS-CoV-2 diagnosis by making it faster, easier, and more accessible to people worldwide, thus making them ideal for resource-limited settings. These advanced IoMT diagnostic platforms may help with combating SARS-CoV-2 as well as tracking and predicting the spread of future pandemics, ultimately saving lives and mitigating their impact on global health systems.

2D graphene-based advanced nanoarchitectonics for electrochemical biosensors: Applications in cancer biomarker detection

Low-cost, rapid, and easy-to-use biosensors for various cancer biomarkers are of utmost importance in detecting cancer biomarkers for early-stage metastasis control and efficient diagnosis. The molecular complexity of cancer biomarkers is overwhelming, thus, the repeatability and reproducibility of measurements by biosensors are critical factors. Electrochemical biosensors are attractive alternatives in cancer diagnosis due to their low cost, simple operation, and promising analytical figures of merit. Recently graphene-derived nanostructures have been used extensively for the fabrication of electrochemical biosensors because of their unique physicochemical properties, including the high electrical conductivity, adsorption capacity, low cost and ease of mass production, presence of oxygen-containing functional groups that facilitate the bioreceptor immobilization, increased flexibility and mechanical strength, low cellular toxicity. Indeed, these properties make them advantageous compared to other alternatives. However, some drawbacks must be overcome to extend their use, such as poor and uncontrollable deposition on the substrate due to the low dispersity of some graphene materials and irreproducibility of the results because of the differences in various batches of the produced graphene materials. This review has documented the most recently developed strategies for electrochemical sensor fabrication. It differs in the categorization method compared to published works to draw greater attention to the wide opportunities of graphene nanomaterials for biological applications. Limitations and future scopes are discussed to advance the integration of novel technologies such as artificial intelligence, the internet of medical things, and triboelectric nanogenerators to eventually increase efficacy and efficiency.

Multirole of the internet of medical things (IoMT) in biomedical systems for managing smart healthcare systems: An overview of current and future innovative trends

Internet of Medical Things (IoMT) is an emerging subset of Internet of Things (IoT), often called as IoT in healthcare, refers to medical devices and applications with internet connectivity, is exponentially gaining researchers' attention due to its wide-ranging applicability in biomedical systems for Smart Healthcare systems. IoMT facilitates remote health biomedical system and plays a crucial role within the healthcare industry to enhance precision, reliability, consistency and productivity of electronic devices used for various healthcare purposes. It comprises a conceptualized architecture for providing information retrieval strategies to extract the data from patient records using sensors for biomedical analysis and diagnostics against manifold diseases to provide cost-effective medical solutions, quick hospital treatments, and personalized healthcare. This article provides a comprehensive overview of IoMT with special emphasis on its current and future trends used in biomedical systems, such as deep learning, machine learning, blockchains, artificial intelligence, radio frequency identification, and industry 5.0.

Light-M: An efficient lightweight medical image segmentation framework for resource-constrained IoMT

The Internet of Medical Things (IoMT) is being incorporated into current healthcare systems. This technology intends to connect patients, IoMT devices, and hospitals over mobile networks, allowing for more secure, quick, and convenient health monitoring and intelligent healthcare services. However, existing intelligent healthcare applications typically rely on large-scale AI models, and standard IoMT devices have significant resource constraints. To alleviate this paradox, in this paper, we propose a Knowledge Distillation (KD)-based IoMT end-edge-cloud orchestrated architecture for medical image segmentation tasks, called Light-M, aiming to deploy a lightweight medical model in resource-constrained IoMT devices. Specifically, Light-M trains a large teacher model in the cloud server and employs computation in local nodes through imitation of the performance of the teacher model using knowledge distillation. Light-M contains two KD strategies: (1) active exploration and passive transfer (AEPT) and (2) self-attention-based inter-class feature variation (AIFV) distillation for the medical image segmentation task. The AEPT encourages the student model to learn undiscovered knowledge/features of the teacher model without additional feature layers, aiming to explore new features and outperform the teacher. To improve the distinguishability of the student for different classes, the student learns the self-attention-based feature variation (AIFV) between classes. Since the proposed AEPT and AIFV only appear in the training process, our framework does not involve any additional computation burden for a student model during the segmentation task deployment. Extensive experiments on cardiac images and public real-scene datasets demonstrate that our approach improves student model learning representations and outperforms state-of-the-art methods by combining two knowledge distillation strategies. Moreover, when deployed on the IoT device, the distilled student model takes only 29.6 ms for one sample at the inference step.

Point-Of-Care Ultra-Portable Single-Molecule Bioassays for One-Health

Screening asymptomatic organisms (humans, animals, plants) with a high-diagnostic accuracy using point-of-care-testing (POCT) technologies, though still visionary holds great potential. Convenient surveillance requires easy-to-use, cost-effective, ultra-portable but highly reliable, in-vitro-diagnostic devices that are ready for use wherever they are needed. Currently, there are not yet such devices available on the market, but there are a couple more promising technologies developed at readiness-level 5: the Clustered-Regularly-Interspaced-Short-Palindromic-Repeats (CRISPR) lateral-flow-strip tests and the Single-Molecule-with-a-large-Transistor (SiMoT) bioelectronic palmar devices. They both hold key features delineated by the World-Health-Organization for POCT systems and an occurrence of false-positive and false-negative errors <1-5% resulting in diagnostic-selectivity and sensitivity >95-99%, while limit-of-detections are of few markers. CRISPR-strip is a molecular assay that, can detect down to few copies of DNA/RNA markers in blood while SiMoT immunometric and molecular test can detect down to a single oligonucleotide, protein marker, or pathogens in 0.1mL of blood, saliva, and olive-sap. These technologies can prospectively enable the systematic and reliable surveillance of asymptomatic ones prior to worsening/proliferation of illnesses allowing for timely diagnosis and swift prognosis. This could establish a proactive healthcare ecosystem that results in effective treatments for all living organisms generating diffuse and well-being at efficient costs.

Advancements in biosensors for cancer detection: revolutionizing diagnostics

Cancer stands as the reigning champion of life-threatening diseases, casting a shadow with the highest global mortality rate. Unleashing the power of early cancer treatment is a vital weapon in the battle for efficient and positive outcomes. Yet, conventional screening procedures wield limitations of exorbitant costs, time-consuming endeavors, and impracticality for repeated testing. Enter bio-marker-based cancer diagnostics, which emerge as a formidable force in the realm of early detection, disease progression assessment, and ultimate cancer therapy. These remarkable devices boast a reputation for their exceptional sensitivity, streamlined setup requirements, and lightning fast response times. In this study, we embark on a captivating exploration of the most recent advancements and enhancements in the field of electrochemical marvels, targeting the detection of numerous cancer biomarkers. With each breakthrough, we inch closer to a future where cancer's grip on humanity weakens, guided by the promise of personalized treatment and improved patient outcomes. Together, we unravel the mysteries that cancer conceals and illuminate a path toward triumph against this daunting adversary. This study celebrates the relentless pursuit of progress, where electrochemical innovations take center stage in the quest for a world free from the clutches of carcinoma.

Barriers and enablers for implementation of digital-linked diagnostics models at point-of-care in South Africa: stakeholder engagement

The integration of digital technologies holds significant promise in enhancing accessibility to disease diagnosis and treatment at point-of-care (POC) settings. Effective implementation of such interventions necessitates comprehensive stakeholder engagements. This study presents the outcomes of a workshop conducted with key stakeholders, aiming to discern barriers and enablers in implementing digital-connected POC diagnostic models in South Africa. The workshop, a component of the 2022 REASSURED Diagnostics symposium, employed the nominal group technique (NGT) and comprised two phases: Phase 1 focused on identifying barriers, while Phase 2 centered on enablers for the implementation of digital-linked POC diagnostic models. Stakeholders identified limited connectivity, restricted offline functionality, and challenges related to load shedding or rolling electricity blackouts as primary barriers. Conversely, ease of use, subsidies provided by the National Health Insurance, and 24-h assistance emerged as crucial enablers for the implementation of digital-linked POC diagnostic models. The NGT workshop proved to be an effective platform for elucidating key barriers and enablers in implementing digital-linked POC diagnostic models. Subsequent research endeavors should concentrate on identifying optimal strategies for implementing these advanced diagnostic models in underserved populations.

Nanobiosensors for procalcitonin (PCT) analysis

BACKGROUND

Procalcitonin (PCT) is a critical biomarker that is released in response to bacterial infections and can be used to differentiate the pathogenesis of the infectious process.

OBJECTIVE

In this article, we provide an overview of recent advances in PCT biosensors, highlighting different approaches for biosensor construction, different immobilization methods, advantages and roles of different matrices used, analytical performance, and PCT biosensor construction. Also, we will explain PCT biosensors sensible limits of detection (LOD), linearity, and other analytical characteristics. Future prospects for the development of better PCT biosensor systems are also discussed.

METHODS

Traditional methods such as capillary electrophoresis, high-performance liquid chromatography, and mass spectrometry are effective in analyzing PCT in the medical field, but they are complicated, time-consuming sample preparation, and require expensive equipment and skilled personnel.

RESULTS

In the past decades, PCT biosensors have emerged as simple, fast, and sensitive tools for PCT analysis in various fields, especially medical fields.

CONCLUSION

These biosensors have the potential to accompany or replace traditional analytical methods by simplifying or reducing sample preparation and making field testing easier and faster, while significantly reducing the cost per analysis.

Ultrasensitive ImmunoMag-CRISPR Lateral Flow Assay for Point-of-Care Testing of Urinary Biomarkers

Rapid, accurate, and noninvasive detection of biomarkers in saliva, urine, or nasal fluid is essential for the identification, early diagnosis, and monitoring of cancer, organ failure, transplant rejection, vascular diseases, autoimmune disorders, and infectious diseases. We report the development of an Immuno-CRISPR-based lateral flow assay (LFA) using antibody-DNA barcode complexes with magnetic enrichment of the target urinary biomarkers CXCL9 and CXCL10 for naked eye detection (ImmunoMag-CRISPR LFA). An intermediate approach involving a magnetic bead-based Immuno-CRISPR assay (ImmunoMag-CRISPR) resulted in a limit of detection (LOD) of 0.6 pg/mL for CXCL9. This value surpasses the detection limits achieved by previously reported assays. The highly sensitive detection method was then re-engineered into an LFA format with an LOD of 18 pg/mL for CXCL9, thereby enabling noninvasive early detection of acute kidney transplant rejection. The ImmunoMag-CRISPR LFA was tested on 42 clinical urine samples from kidney transplant recipients, and the assay could determine 11 positive and 31 negative urinary samples through a simple visual comparison of the test line and the control line of the LFA strip. The LFA system was then expanded to quantify the CXCL9 and CXCL10 levels in clinical urine samples from images. This approach has the potential to be extended to a wide range of point-of-care tests for highly sensitive biomarker detection.

Recent Advances in Electrochemical Detection of Cell Energy Metabolism

Cell energy metabolism is a complex and multifaceted process by which some of the most important nutrients, particularly glucose and other sugars, are transformed into energy. This complexity is a result of dynamic interactions between multiple components, including ions, metabolic intermediates, and products that arise from biochemical reactions, such as glycolysis and mitochondrial oxidative phosphorylation (OXPHOS), the two main metabolic pathways that provide adenosine triphosphate (ATP), the main source of chemical energy driving various physiological activities. Impaired cell energy metabolism and perturbations or dysfunctions in associated metabolites are frequently implicated in numerous diseases, such as diabetes, cancer, and neurodegenerative and cardiovascular disorders. As a result, altered metabolites hold value as potential disease biomarkers. Electrochemical biosensors are attractive devices for the early diagnosis of many diseases and disorders based on biomarkers due to their advantages of efficiency, simplicity, low cost, high sensitivity, and high selectivity in the detection of anomalies in cellular energy metabolism, including key metabolites involved in glycolysis and mitochondrial processes, such as glucose, lactate, nicotinamide adenine dinucleotide (NADH), reactive oxygen species (ROS), glutamate, and ATP, both in vivo and in vitro. This paper offers a detailed examination of electrochemical biosensors for the detection of glycolytic and mitochondrial metabolites, along with their many applications in cell chips and wearable sensors.

Self-Powered Microfluidics for Point-of-Care Solutions: From Sampling to Detection of Proteins and Nucleic Acids

Self-powered microfluidics presents a revolutionary approach to address the challenges of healthcare in decentralized and point-of-care settings where limited access to resources and infrastructure prevails or rapid clinical decision-making is critical. These microfluidic systems exploit physical and chemical phenomena, such as capillary forces and surface tension, to manipulate tiny volumes of fluids without the need for external power sources, making them cost-effective and highly portable. Recent technological advancements have demonstrated the ability to preprogram complex multistep liquid operations within the microfluidic circuit of these standalone systems, which enabled the integration of sensitive detection and readout principles. This chapter first addresses how the accessibility to in vitro diagnostics can be improved by shifting toward decentralized approaches like remote microsampling and point-of-care testing. Next, the crucial role of self-powered microfluidic technologies to enable this patient-centric healthcare transition is emphasized using various state-of-the-art examples, with a primary focus on applications related to biofluid collection and the detection of either proteins or nucleic acids. This chapter concludes with a summary of the main findings and our vision of the future perspectives in the field of self-powered microfluidic technologies and their use for in vitro diagnostics applications.

Impact of nanotechnology on conventional and artificial intelligence-based biosensing strategies for the detection of viruses

Recent years have witnessed the emergence of several viruses and other pathogens. Some of these infectious diseases have spread globally, resulting in pandemics. Although biosensors of various types have been utilized for virus detection, their limited sensitivity remains an issue. Therefore, the development of better diagnostic tools that facilitate the more efficient detection of viruses and other pathogens has become important. Nanotechnology has been recognized as a powerful tool for the detection of viruses, and it is expected to change the landscape of virus detection and analysis. Recently, nanomaterials have gained enormous attention for their value in improving biosensor performance owing to their high surface-to-volume ratio and quantum size effects. This article reviews the impact of nanotechnology on the design, development, and performance of sensors for the detection of viruses. Special attention has been paid to nanoscale materials, various types of nanobiosensors, the internet of medical things, and artificial intelligence-based viral diagnostic techniques.

Nanomaterial-based biosensors for avian influenza virus: A new way forward

Avian influenza virus (AIV) is a zoonotic virus that can be transmitted from animals to humans. Although human infections are rare, the virus has a high mortality rate when contracted. Appropriate detection methods are thus crucial for combatting this pathogen. There is a growing demand for rapid, selective, and accurate methods of identifying the virus. Numerous biosensors have been designed and commercialized to detect AIV. However, they all have considerable shortcomings. Nanotechnology offers a new way forward. Nanomaterials produce more eco-friendly, rapid, and portable diagnostic systems. They also exhibit high sensitivity and selectivity while achieving a low detection limit (LOD). This paper reviews state-of-the-art nanomaterial-based biosensors for AIV detection, such as those composed of quantum dots, gold, silver, carbon, silica, nanodiamond, and other nanoparticles. It also offers insight into potential trial protocols for creating more effective methods of identifying AIV and discusses key issues associated with developing nanomaterial-based biosensors.

An Optimized Model Based on Deep Learning and Gated Recurrent Unit for COVID-19 Death Prediction

The COVID-19 epidemic poses a worldwide threat that transcends provincial, philosophical, spiritual, radical, social, and educational borders. By using a connected network, a healthcare system with the Internet of Things (IoT) functionality can effectively monitor COVID-19 cases. IoT helps a COVID-19 patient recognize symptoms and receive better therapy more quickly. A critical component in measuring, evaluating, and diagnosing the risk of infection is artificial intelligence (AI). It can be used to anticipate cases and forecast the alternate incidences number, retrieved instances, and injuries. In the context of COVID-19, IoT technologies are employed in specific patient monitoring and diagnosing processes to reduce COVID-19 exposure to others. This work uses an Indian dataset to create an enhanced convolutional neural network with a gated recurrent unit (CNN-GRU) model for COVID-19 death prediction via IoT. The data were also subjected to data normalization and data imputation. The 4692 cases and eight characteristics in the dataset were utilized in this research. The performance of the CNN-GRU model for COVID-19 death prediction was assessed using five evaluation metrics, including median absolute error (MedAE), mean absolute error (MAE), root mean squared error (RMSE), mean square error (MSE), and coefficient of determination (R2). ANOVA and Wilcoxon signed-rank tests were used to determine the statistical significance of the presented model. The experimental findings showed that the CNN-GRU model outperformed other models regarding COVID-19 death prediction.

Heart failure patients monitoring using IoT-based remote monitoring system

Intelligent health monitoring systems are becoming more important and popular as technology advances. Nowadays, online services are replacing physical infrastructure in several domains including medical services as well. The COVID-19 pandemic has also changed the way medical services are delivered. Intelligent appliances, smart homes, and smart medical systems are some of the emerging concepts. The Internet of Things (IoT) has changed the way communication occurs alongside data collection sources aided by smart sensors. It also has deployed artificial intelligence (AI) methods for better decision-making provided by efficient data collection, storage, retrieval, and data management. This research employs health monitoring systems for heart patients using IoT and AI-based solutions. Activities of heart patients are monitored and reported using the IoT system. For heart disease prediction, an ensemble model ET-CNN is presented which provides an accuracy score of 0.9524. The investigative data related to this system is very encouraging in real-time reporting and classifying heart patients with great accuracy.

A Multichannel Fluorescence Isothermal Amplification Device with Integrated Internet of Medical Things for Rapid Sensing of Pathogens through Deep Learning

The landscape of diagnostic assessments has experienced a paradigm shift driven by the advent of isothermal amplification techniques on point-of-care testing (POCT). The development of compact, portable isothermal amplification devices further emphasizes their transformative influence on diagnostic approaches. However, in prioritizing portability, these devices may exhibit limitations in functionality, rendering them less effective in addressing urgent public health emergencies during sudden pathogen outbreaks. In this paper, an efficient isothermal fluorescence amplification device has been fabricated for the rapid detection of pathogens during public health crises. The device features multichannel capability for simultaneous detection of various targets, integrates with the Internet of Medical Things (IoMT) for remote control and data uploading, and includes a deep learning-based batch processing system for rapid (9.4 ms) and accurate discrimination of pathogen type with excellent accuracy. The device has been successfully employed to simultaneously detect Staphylococcus aureus (SA) and methicillin-resistant Staphylococcus aureus (MRSA) with limits of detection (LODs) of 18 CFU/mL (SA) and 20 CFU/mL (MRSA) within 35 min by multiplex RPA assay and CRISPR/Cas12a-mediated nucleic acid detection assay.

Emerging Applications of Nanobiosensors in Pathogen Detection in Water and Food

Food and waterborne illnesses are still a major concern in health and food safety areas. Every year, almost 0.42 million and 2.2 million deaths related to food and waterborne illness are reported worldwide, respectively. In foodborne pathogens, bacteria such as Salmonella, Shiga-toxin producer Escherichia coli, Campylobacter, and Listeria monocytogenes are considered to be high-concern pathogens. High-concern waterborne pathogens are Vibrio cholerae, leptospirosis, Schistosoma mansoni, and Schistosima japonicum, among others. Despite the major efforts of food and water quality control to monitor the presence of these pathogens of concern in these kinds of sources, foodborne and waterborne illness occurrence is still high globally. For these reasons, the development of novel and faster pathogen-detection methods applicable to real-time surveillance strategies are required. Methods based on biosensor devices have emerged as novel tools for faster detection of food and water pathogens, in contrast to traditional methods that are usually time-consuming and are unsuitable for large-scale monitoring. Biosensor devices can be summarized as devices that use biochemical reactions with a biorecognition section (isolated enzymes, antibodies, tissues, genetic materials, or aptamers) to detect pathogens. In most cases, biosensors are based on the correlation of electrical, thermal, or optical signals in the presence of pathogen biomarkers. The application of nano and molecular technologies allows the identification of pathogens in a faster and high-sensibility manner, at extremely low-pathogen concentrations. In fact, the integration of gold, silver, iron, and magnetic nanoparticles (NP) in biosensors has demonstrated an improvement in their detection functionality. The present review summarizes the principal application of nanomaterials and biosensor-based devices for the detection of pathogens in food and water samples. Additionally, it highlights the improvement of biosensor devices through nanomaterials. Nanomaterials offer unique advantages for pathogen detection. The nanoscale and high specific surface area allows for more effective interaction with pathogenic agents, enhancing the sensitivity and selectivity of the biosensors. Finally, biosensors' capability to functionalize with specific molecules such as antibodies or nucleic acids facilitates the specific detection of the target pathogens.

Nanoengineered phosphorus doped graphitic carbon nitride based ultrasensitive biosensing platform for Swine flu detection

In the present study, we developed an amino-polyindole modified phosphorus doped graphitic carbon nitride nanomaterial (APIN/P-g-C3N4) based immunosensing biochip for Serum amyloid A (SAA) biomarker towards early diagnosis of Swine flu. The P-g-C3N4 was synthesis via polycondensation and functionalized with APIN. Further, the biochip was fabricated by modifying the working area of SPE with APIN/P-g-C3N4 using drop cast method, APIN introduced the larger loading of -NH2 group moieties onto P-g-C3N4 matrix and benefitted to reinforced the biomolecules via covalent linkages. The monoclonal anti-SAA was conjugated onto APIN/P-g-C3N4/SPE using EDC-NHS chemistry and BSA was added for non-specific site blocking. The structural, chemical, composition and morphological characteristics of the synthesized, functionalized nanomaterial and fabricated biochips were investigated by XRD, XPS, FT-IR spectroscopy, SEM, FE-SEM and TEM techniques. Further, the electrochemical characterization and response studies of fabricated biochip were analyzed using the CV and DPV techniques. Based on the analytical performance of the proposed immunosensing biochip i.e. BSA/anti-SAA/APIN/P-g-C3N4/SPE, it is capable to detect SAA protein with ultra sensitivity of 79.5 μA log (mL ng-1) cm-2, ultralow limit of detection of 5 ng mL-1 and wider linear detection range of 5 ng mL-1-500 μg mL-1 with quick response time of 10 min. Moreover, the fabricated immunosensing biochips was used to analyse SAA protein in spiked serum samples and the achieved results demonstrated the good agreement with the electrochemical response observed in standard SAA protein samples in analytical solution. The proposed biochip can provide insights for developing a wide range of clinical screening tools for detecting various contagious diseases.

Organic electrochemical transistors printed from degradable materials as disposable biochemical sensors

Transient electronics hold promise in reducing electronic waste, especially in applications that require only a limited lifetime. While various degradable electronic and physical sensing devices have been proposed, there is growing interest in the development of degradable biochemical sensors. In this work, we present the development of an organic electrochemical transistor (OECT) with degradable electrodes, printed on an eco- and bioresorbable substrate. The influence of the design and materials for the contacts, channel and gate of the transducer, namely poly(3,4-ethylene dioxythiophene):polystyrene sulfonate (PEDOT:PSS) and carbon, is systematically evaluated for the development of OECT-based transient biosensors. The sensing capabilities of the electrochemical transistors are demonstrated with ionic solutions as well as for the enzyme-based detection of glucose. The disposable OECTs show comparable performance to their non-degradable counterparts. Their integration with highly conductive degradable and printable zinc tracks is studied for the realization of interconnects. These eco-friendly OECTs may find applications as disposable and sustainable biochemical sensors, and constitute a step towards bioresorbable biosensors.

Scope of Onsite, Portable Prevention Diagnostic Strategies for Alternaria Infections in Medicinal Plants

Medicinal plants are constantly challenged by different biotic inconveniences, which not only cause yield and economic losses but also affect the quality of products derived from them. Among them, Alternaria pathogens are one of the harmful fungal pathogens in medicinal plants across the globe. Therefore, a fast and accurate detection method in the early stage is needed to avoid significant economic losses. Although traditional methods are available to detect Alternaria, they are more time-consuming and costly and need good expertise. Nevertheless, numerous biochemical- and molecular-based techniques are available for the detection of plant diseases, but their efficacy is constrained by differences in their accuracy, specificity, sensitivity, dependability, and speed in addition to being unsuitable for direct on-field studies. Considering the effect of Alternaria on medicinal plants, the development of novel and early detection measures is required to detect causal Alternaria species accurately, sensitively, and rapidly that can be further applied in fields to speed up the advancement process in detection strategies. In this regard, nanotechnology can be employed to develop portable biosensors suitable for early and correct pathogenic disease detection on the field. It also provides an efficient future scope to convert innovative nanoparticle-derived fabricated biomolecules and biosensor approaches in the diagnostics of disease-causing pathogens in important medicinal plants. In this review, we summarize the traditional methods, including immunological and molecular methods, utilized in plant-disease diagnostics. We also brief advanced automobile and efficient sensing technologies for diagnostics. Here we are proposing an idea with a focus on the development of electrochemical and/or colorimetric properties-based nano-biosensors that could be useful in the early detection of Alternaria and other plant pathogens in important medicinal plants. In addition, we discuss challenges faced during the fabrication of biosensors and new capabilities of the technology that provide information regarding disease management strategies.

Electrochemical biosensors in healthcare services: bibliometric analysis and recent developments

Biosensors are nowadays being used in various fields including disease diagnosis and clinical analysis. The ability to detect biomolecules associated with disease is vital not only for accurate diagnosis of disease but also for drug discovery and development. Among the different types of biosensors, electrochemical biosensor is most widely used in clinical and health care services especially in multiplex assays due to its high susceptibility, low cost and small in size. This article includes comprehensive review of biosensors in medical field with special emphasis on electrochemical biosensors for multiplex assays and in healthcare services. Also, the publications on electrochemical biosensors are increasing rapidly; therefore, it is crucial to be aware of any latest developments or trends in this field of research. We used bibliometric analyses to summarize the progress of this research area. The study includes global publication counts on electrochemical biosensors for healthcare along with various bibliometric data analyses by VOSviewer software. The study also recognizes the top authors and journals in the related area, and determines proposal for monitoring research.

Recent Advances in 2D Material Theory, Synthesis, Properties, and Applications

Two-dimensional (2D) material research is rapidly evolving to broaden the spectrum of emergent 2D systems. Here, we review recent advances in the theory, synthesis, characterization, device, and quantum physics of 2D materials and their heterostructures. First, we shed insight into modeling of defects and intercalants, focusing on their formation pathways and strategic functionalities. We also review machine learning for synthesis and sensing applications of 2D materials. In addition, we highlight important development in the synthesis, processing, and characterization of various 2D materials (e.g., MXnenes, magnetic compounds, epitaxial layers, low-symmetry crystals, etc.) and discuss oxidation and strain gradient engineering in 2D materials. Next, we discuss the optical and phonon properties of 2D materials controlled by material inhomogeneity and give examples of multidimensional imaging and biosensing equipped with machine learning analysis based on 2D platforms. We then provide updates on mix-dimensional heterostructures using 2D building blocks for next-generation logic/memory devices and the quantum anomalous Hall devices of high-quality magnetic topological insulators, followed by advances in small twist-angle homojunctions and their exciting quantum transport. Finally, we provide the perspectives and future work on several topics mentioned in this review.

Recent Advances in Metaphotonic Biosensors

Metaphotonic devices, which enable light manipulation at a subwavelength scale and enhance light-matter interactions, have been emerging as a critical pillar in biosensing. Researchers have been attracted to metaphotonic biosensors, as they solve the limitations of the existing bioanalytical techniques, including the sensitivity, selectivity, and detection limit. Here, we briefly introduce types of metasurfaces utilized in various metaphotonic biomolecular sensing domains such as refractometry, surface-enhanced fluorescence, vibrational spectroscopy, and chiral sensing. Further, we list the prevalent working mechanisms of those metaphotonic bio-detection schemes. Furthermore, we summarize the recent progress in chip integration for metaphotonic biosensing to enable innovative point-of-care devices in healthcare. Finally, we discuss the impediments in metaphotonic biosensing, such as its cost effectiveness and treatment for intricate biospecimens, and present a prospect for potential directions for materializing these device strategies, significantly influencing clinical diagnostics in health and safety.

Aptamer-Based Point-of-Care Devices: Emerging Technologies and Integration of Computational Methods

Recent innovations in point-of-care (POC) diagnostic technologies have paved a critical road for the improved application of biomedicine through the deployment of accurate and affordable programs into resource-scarce settings. The utilization of antibodies as a bio-recognition element in POC devices is currently limited due to obstacles associated with cost and production, impeding its widespread adoption. One promising alternative, on the other hand, is aptamer integration, i.e., short sequences of single-stranded DNA and RNA structures. The advantageous properties of these molecules are as follows: small molecular size, amenability to chemical modification, low- or nonimmunogenic characteristics, and their reproducibility within a short generation time. The utilization of these aforementioned features is critical in developing sensitive and portable POC systems. Furthermore, the deficiencies related to past experimental efforts to improve biosensor schematics, including the design of biorecognition elements, can be tackled with the integration of computational tools. These complementary tools enable the prediction of the reliability and functionality of the molecular structure of aptamers. In this review, we have overviewed the usage of aptamers in the development of novel and portable POC devices, in addition to highlighting the insights that simulations and other computational methods can provide into the use of aptamer modeling for POC integration.

Internet-of-medical-things integrated point-of-care biosensing devices for infectious diseases: Toward better preparedness for futuristic pandemics

Microbial pathogens have threatened the world due to their pathogenicity and ability to spread in communities. The conventional laboratory-based diagnostics of microbes such as bacteria and viruses need bulky expensive experimental instruments and skilled personnel which limits their usage in resource-limited settings. The biosensors-based point-of-care (POC) diagnostics have shown huge potential to detect microbial pathogens in a faster, cost-effective, and user-friendly manner. The use of various transducers such as electrochemical and optical along with microfluidic integrated biosensors further enhances the sensitivity and selectivity of detection. Additionally, microfluidic-based biosensors offer the advantages of multiplexed detection of analyte and the ability to deal with nanoliters volume of fluid in an integrated portable platform. In the present review, we discussed the design and fabrication of POCT devices for the detection of microbial pathogens which include bacteria, viruses, fungi, and parasites. The electrochemical techniques and current advances in this field in terms of integrated electrochemical platforms that include mainly microfluidic- based approaches and smartphone and Internet-of-things (IoT) and Internet-of-Medical-Things (IoMT) integrated systems have been highlighted. Further, the availability of commercial biosensors for the detection of microbial pathogens will be briefed. In the end, the challenges while fabrication of POC biosensors and expected future advances in the field of biosensing have been discussed. The integrated biosensor-based platforms with the IoT/IoMT usually collect the data to track the community spread of infectious diseases which would be beneficial in terms of better preparedness for current and futuristic pandemics and is expected to prevent social and economic losses.

Coupling ELISA to smartphones for POCT of chronic and congenital Chagas disease

Chagas disease (CD) affects about 7 million people worldwide, presents a large prevalence in Latin America, and is growing in the rest of the world, where congenital CD is the main mode of transmission. Point-of-care testing (POCT) methods are increasingly required to ease early diagnostics and increase treatment success. This work presents the development and validation of a smartphone-integrated ELISA-based POCT system for the detection of both chronic and congenital CD. Expensive and bulky equipment used for ELISA in conventional laboratories was replaced as follows. A miniaturized device was fabricated for incubation of commercial ELISA plates, achieving ∼±1 °C uniformity and stability. The ELISA plate reader was replaced by smartphone camera and image processing, comprising algorithms to account for variability sources and spatial light non-uniformity; thus, additional hardware like a dark-box is not required. The agreement between samples classified with this novel reading method vs. ELISA plate reader was found to be 99.7% and 95.4% for chronic and congenital CD, respectively. Furthermore, a smartphone application was designed and implemented to guide the user during the assay, provide connectivity, and access databases, facilitating patient monitoring and health-policy making. The whole system is aimed to be used as a practical diagnostic tool in primary health care settings, as well as to facilitate patients' follow-up to provide better treatment. Concerning the technology itself, the proposed POCT platform is versatile enough to be readily adapted for the detection of other infectious diseases.

Bibliometric analysis of the use of artificial intelligence in COVID-19 based on scientific studies

Background and Aims

One such strategy is citation analysis used by researchers for research planning an article referred to by another article receives a "citation." By using bibliometric analysis, the development of research areas and authors' influence can be investigated. The current study aimed to identify and analyze the characteristics of 100 highly cited articles on the use of artificial intelligence concerning COVID-19.

Methods

On July 27, 2022, this database was searched using the keywords "artificial intelligence" and "COVID-19" in the topic. After extensive searching, all retrieved articles were sorted by the number of citations, and 100 highly cited articles were included based on the number of citations. The following data were extracted: year of publication, type of study, name of journal, country, number of citations, language, and keywords.

Results

The average number of citations for 100 highly cited articles was 138.54. The top three cited articles with 745, 596, and 549 citations. The top 100 articles were all in English and were published in 2020 and 2021. China was the most prolific country with 19 articles, followed by the United States with 15 articles and India with 10 articles.

Conclusion

The current bibliometric analysis demonstrated the significant growth of the use of artificial intelligence for COVID-19. Using these results, research priorities are more clearly defined, and researchers can focus on hot topics.

Paper-Based Radial Flow Assay Integrated to Portable Isothermal Amplification Chip Platform for Colorimetric Detection of Target DNA

A novel integrated detection system that introduces a paper-chip-based molecular detection strategy into a polydimethylsiloxane (PDMS) microchip and temperature control system was developed for on-site colorimetric detection of DNA. For the paper chip-based detection strategy, a padlock probe DNA (PLP)-mediated rolling circle amplification (RCA) reaction for signal amplification and a radial flow assay according to the Au-probe labeling strategy for visualization were optimized and applied for DNA detection. In the PDMS chip, the reactions for ligation of target-dependent PLP, RCA, and labeling were performed one-step under isothermal temperature in a single chamber, and one drop of the final reaction solution was loaded onto the paper chip to form a radial colorimetric signal. To create an optimal analysis environment, not only the optimization of molecular reactions for DNA detection but also the chamber shape of the PDMS chip and temperature control system were successfully verified. Our results indicate that a detection limit of 14.7 nM of DNA was achieved, and non-specific DNAs with a single-base mismatch at the target DNA were selectively discriminated. This integrated detection system can be applied not only for single nucleotide polymorphism identification, but also for pathogen gene detection. The adoption of inexpensive paper and PDMS chips allows the fabrication of cost-effective detection systems. Moreover, it is very suitable for operation in various resource-limited locations by adopting a highly portable and user-friendly detection method that minimizes the use of large and expensive equipment.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13206-023-00101-7.

Investigating the performance of machine learning algorithms in predicting the survival of COVID-19 patients: A cross section study of Iran

Background and Aims

Like early diagnosis, predicting the survival of patients with Coronavirus Disease 2019 (COVID-19) is of great importance. Survival prediction models help doctors be more cautious to treat the patients who are at high risk of dying because of medical conditions. This study aims to predict the survival of hospitalized patients with COVID-19 by comparing the accuracy of machine learning (ML) models.

Methods

It is a cross-sectional study which was performed in 2022 in Fasa city in Iran country. The research data set was extracted from the period February 18, 2020 to February 10, 2021, and contains 2442 hospitalized patients' records with 84 features. A comparison was made between the efficiency of five ML algorithms to predict survival, includes Naive Bayes (NB), K-nearest neighbors (KNN), random forest (RF), decision tree (DT), and multilayer perceptron (MLP). Modeling steps were done with Python language in the Anaconda Navigator 3 environment.

Results

Our findings show that NB algorithm had better performance than others with accuracy, precision, recall, F-score, and area under receiver operating characteristic curve of 97%, 96%, 96%, 96%, and 97%, respectively. Based on the analysis of factors affecting survival, heart disease, pulmonary diseases and blood related disease were the most important disease related to death.

Conclusion

The development of software systems based on NB will be effective to predict the survival of COVID-19 patients.

An IoMT-Based Melanoma Lesion Segmentation Using Conditional Generative Adversarial Networks

Currently, Internet of medical things-based technologies provide a foundation for remote data collection and medical assistance for various diseases. Along with developments in computer vision, the application of Artificial Intelligence and Deep Learning in IOMT devices aids in the design of effective CAD systems for various diseases such as melanoma cancer even in the absence of experts. However, accurate segmentation of melanoma skin lesions from images by CAD systems is necessary to carry out an effective diagnosis. Nevertheless, the visual similarity between normal and melanoma lesions is very high, which leads to less accuracy of various traditional, parametric, and deep learning-based methods. Hence, as a solution to the challenge of accurate segmentation, we propose an advanced generative deep learning model called the Conditional Generative Adversarial Network (cGAN) for lesion segmentation. In the suggested technique, the generation of segmented images is conditional on dermoscopic images of skin lesions to generate accurate segmentation. We assessed the proposed model using three distinct datasets including DermQuest, DermIS, and ISCI2016, and attained optimal segmentation results of 99%, 97%, and 95% performance accuracy, respectively.

Self-directed molecular diagnostics (SdMDx) system for COVID-19 via one-pot processing

Rapid, sensitive, and specific detection of the severe acute respiratory syndrome coronavirus (SARS-CoV)- 2 during early infection is pivotal in controlling the spread and pathological progression of Coronavirus Disease 2019 (COVID-19). Thus, highly accurate, affordable, and scalable point-of-care (POC) diagnostic technologies are necessary. Herein, we developed a rapid and efficient self-directed molecular diagnostic (SdMDx) system for SARS-CoV-2. This system combines the sample preparation step, including virus enrichment and extraction processes, which involve dimethyl suberimidate dihydrochloride and diatomaceous earth functionalized with 3-aminopropyl(diethoxy)methylsilane, and the detection step using loop-mediated isothermal amplification-lateral flow assay (LAMP-LFA). Using the SdMDx system, SARS-CoV-2 could be detected within 47 min by hand without the need for any larger instruments. The SdMDx system enabled detection as low as 0.05 PFU in the culture fluid of SARS-CoV-2-infected VeroE6 cells. We validated the accuracy of the SdMDx system on 38 clinical nasopharyngeal specimens. The clinical utility of the SdMDx system for targeting the S gene of SARS-CoV-2 showed 94.4% sensitivity and 100% specificity. This system is more sensitive than antigen and antibody assays, and it minimizes the use of complicated processes and reduces contamination risks. Accordingly, we demonstrated that the SdMDx system enables a rapid, accurate, simple, efficient, and inexpensive detection of SARS-CoV-2 at home, in emergency facilities, and in low-resource sites as a pre-screening platform and POC testing through self-operation and self-diagnosis.

Moving toward smart biomedical sensing

The development of novel biomedical sensors as highly promising devices/tools in early diagnosis and therapy monitoring of many diseases and disorders has recently witnessed unprecedented growth; more and faster than ever. Nonetheless, on the eve of Industry 5.0 and by learning from defects of current sensors in smart diagnostics of pandemics, there is still a long way to go to achieve the ideal biomedical sensors capable of meeting the growing needs and expectations for smart biomedical/diagnostic sensing through eHealth systems. Herein, an overview is provided to highlight the importance and necessity of an inevitable transition in the era of digital health/Healthcare 4.0 towards smart biomedical/diagnostic sensing and how to approach it via new digital technologies including Internet of Things (IoT), artificial intelligence, IoT gateways (smartphones, readers), etc. This review will bring together the different types of smartphone/reader-based biomedical sensors, which have been employing for a wide variety of optical/electrical/electrochemical biosensing applications and paving the way for future eHealth diagnostic devices by moving towards smart biomedical sensing. Here, alongside highlighting the characteristics/criteria that should be met by the developed sensors towards smart biomedical sensing, the challenging issues ahead are delineated along with a comprehensive outlook on this extremely necessary field.

A Novel Peptide-Based Detection of SARS-CoV-2 Antibodies

The need for rapidly developed diagnostic tests has gained significant attention after the recent pandemic. Production of neutralizing antibodies for vaccine development or antibodies to be used in diagnostic tests usually require the usage of recombinant proteins representing the infectious agent. However, peptides that can mimic these recombinant proteins may be rapidly utilized, especially in emergencies such as the recent outbreak. Here, we report two peptides that mimic the receptor binding domain of the spike protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and investigate their binding behavior against the corresponding human immunoglobulin G and immunoglobulin M (IgG and IgM) antibodies in a clinical sample using a quartz crystal microbalance (QCM) sensor. These peptides were immobilized on a QCM sensor surface, and their binding behavior was studied against a clinical serum sample that was previously determined to be IgG and IgM-positive. It was determined that designed peptides bind to SARS-CoV-2 antibodies in a clinical sample. These peptides might be useful for the detection of SARS-CoV-2 antibodies using different methods such as enzyme-linked immunosorbent assay (ELISA) or lateral flow assays. A similar platform might prove to be useful for the detection and development of antibodies in other infections.

Medical Image Classifications for 6G IoT-Enabled Smart Health Systems

As day-to-day-generated data become massive in the 6G-enabled Internet of medical things (IoMT), the process of medical diagnosis becomes critical in the healthcare system. This paper presents a framework incorporated into the 6G-enabled IoMT to improve prediction accuracy and provide a real-time medical diagnosis. The proposed framework integrates deep learning and optimization techniques to render accurate and precise results. The medical computed tomography images are preprocessed and fed into an efficient neural network designed for learning image representations and converting each image to a feature vector. The extracted features from each image are then learned using a MobileNetV3 architecture. Furthermore, we enhanced the performance of the arithmetic optimization algorithm (AOA) based on the hunger games search (HGS). In the developed method, named AOAHG, the operators of the HGS are applied to enhance the AOA's exploitation ability while allocating the feasible region. The developed AOAG selects the most relevant features and ensures the overall model classification improvement. To assess the validity of our framework, we conducted evaluation experiments on four datasets, including ISIC-2016 and PH2 for skin cancer detection, white blood cell (WBC) detection, and optical coherence tomography (OCT) classification, using different evaluation metrics. The framework showed remarkable performance compared to currently existing methods in the literature. In addition, the developed AOAHG provided results better than other FS approaches according to the obtained accuracy, precision, recall, and F1-score as performance measures. For example, AOAHG had 87.30%, 96.40%, 88.60%, and 99.69% for the ISIC, PH2, WBC, and OCT datasets, respectively.

Smart Graphene-Based Electrochemical Nanobiosensor for Clinical Diagnosis: Review

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^-6), Nanomolar nM (10^-9), Picomolar pM (10^-12), femtomolar fM (10^-15), and attomolar aM (10^-18).

A Hybrid Multi-Criteria Approach to the Vendor Selection Problem for Sensor-Based Medical Devices

Sensors for health are a dynamic technology and sensor-based medical devices (SMD) are becoming an important part of health monitoring systems in healthcare centers and ambulatory care. The rapid growth in the number, diversity and costs of medical devices and Internet of Things (IoT) healthcare platforms imposes a challenge for healthcare managers: making a rational choice of SMD vendor from a set of potential SMD vendors. The aim of this paper is to develop a hybrid approach that combines a performance evaluation model and a multi-objective model for the SMD vendor selection problem. For determining the criteria weights in the performance evaluation model, an original version of the best worst method (BWM) is applied, which we call the flexible best worst method (FBWM). The multi-objective model has two objective functions; one is to maximize the SMD performance and the other is to minimize the SMD cost. A case study for the application of the hybrid approach for SMD procurement in a healthcare center is analyzed. The hybrid approach can support healthcare decision makers in their SMD procurement decisions.