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

Microfluidic platforms integrated with nano-sensors for point-of-care bioanalysis

Microfluidic technology provides a portable, cost-effective, and versatile tool for point-of-care (POC) bioanalysis because of its associated advantages such as fast analysis, low volumes of reagent consumption, and high portability. Along with microfluidics, the application of nanomaterials in biosensing has attracted lots of attention due to their unique physical and chemical properties for enhanced signal modulation such as signal amplification and signal transduction for POC bioanalysis. Hence, an enormous number of microfluidic devices integrated with nano-sensors have been developed for POC bioanalysis targeting low-resource settings. Herein, we review recent advances in POC bioanalysis on nano-sensor-based microfluidic platforms. We first briefly summarized the different types of cost-effective microfluidic platforms, followed by a concise introduction to nanomaterial-based biosensors. Then, we highlighted the application of microfluidic platforms integrated with nano-sensors for POC bioanalysis. Finally, we discussed the current limitations and perspective trends of the nano-sensor-based microfluidic platforms for POC bioanalysis.

Emerging 0D, 1D, 2D, and 3D nanostructures for efficient point-of-care biosensing

The recent COVID-19 infection outbreak has raised the demand for rapid, highly sensitive POC biosensing technology for intelligent health and wellness. In this direction, efforts are being made to explore high-performance nano-systems for developing novel sensing technologies capable of functioning at point-of-care (POC) applications for quick diagnosis, data acquisition, and disease management. A combination of nanostructures [i.e., 0D (nanoparticles & quantum dots), 1D (nanorods, nanofibers, nanopillars, & nanowires), 2D (nanosheets, nanoplates, nanopores) & 3D nanomaterials (nanocomposites and complex hierarchical structures)], biosensing prototype, and micro-electronics makes biosensing suitable for early diagnosis, detection & prevention of life-threatening diseases. However, a knowledge gap associated with the potential of 0D, 1D, 2D, and 3D nanostructures for the design and development of efficient POC sensing is yet to be explored carefully and critically. With this focus, this review highlights the latest engineered 0D, 1D, 2D, and 3D nanomaterials for developing next-generation miniaturized, portable POC biosensors development to achieve high sensitivity with potential integration with the internet of medical things (IoMT, for miniaturization and data collection, security, and sharing), artificial intelligence (AI, for desired analytics), etc. for better diagnosis and disease management at the personalized level.

Patterning Wettability for Open-Surface Fluidic Manipulation: Fundamentals and Applications

Effective manipulation of liquids on open surfaces without external energy input is indispensable for the advancement of point-of-care diagnostic devices. Open-surface microfluidics has the potential to benefit health care, especially in the developing world. This review highlights the prospects for harnessing capillary forces on surface-microfluidic platforms, chiefly by inducing smooth gradients or sharp steps of wettability on substrates, to elicit passive liquid transport and higher-order fluidic manipulations without off-the-chip energy sources. A broad spectrum of the recent progress in the emerging field of passive surface microfluidics is highlighted, and its promise for developing facile, low-cost, easy-to-operate microfluidic devices is discussed in light of recent applications, not only in the domain of biomedical microfluidics but also in the general areas of energy and water conservation.

A Novel Texture Extraction-Based Compressive Sensing for Lung Cancer Classification

Background

Lung cancer images require large memory storage and transmission bandwidth for sending the data. Compressive sensing (CS), as a method with a statistical approach in signal sampling, provides different output patterns based on information sources. Thus, it can be considered that CS can be used for feature extraction of compressed information.

Methods

In this study, we proposed a novel texture extraction-based CS for lung cancer classification. We classify three types of lung cancer, including adenocarcinoma (ACA), squamous cell carcinoma (SCC), and benign lung cancer (N). The classification is carried out based on texture extraction, which is processed in 2 stages, the first stage to detect N and the second to detect ACA and SCC.

Results

The simulation results show that two-stage texture extraction can improve accuracy by an average of 84%. The proposed system is expected to be decision support in assisting clinical diagnosis. In terms of technical storage, this system can save memory resources.

Conclusions

The proposed two-step texture extraction system combined with CS and K- Nearest Neighbor has succeeded in classifying lung cancer with high accuracy; the system can also save memory storage. It is necessary to examine the complexity of the proposed method so that it can be analyzed further.

Angiotensin-Converting Enzyme 2-Based Biosensing Modalities and Devices for Coronavirus Detection

Rapid and cost-effective diagnostic tests for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are a critical and valuable weapon for the coronavirus disease 2019 (COVID-19) pandemic response. SARS-CoV-2 invasion is primarily mediated by human angiotensin-converting enzyme 2 (hACE2). Recent developments in ACE2-based SARS-CoV-2 detection modalities accentuate the potential of this natural host-virus interaction for developing point-of-care (POC) COVID-19 diagnostic systems. Although research on harnessing ACE2 for SARS-CoV-2 detection is in its infancy, some interesting biosensing devices have been developed, showing the commercial viability of this intriguing new approach. The exquisite performance of the reported ACE2-based COVID-19 biosensors provides opportunities for researchers to develop rapid detection tools suitable for virus detection at points of entry, workplaces, or congregate scenarios in order to effectively implement pandemic control and management plans. However, to be considered as an emerging approach, the rationale for ACE2-based biosensing needs to be critically and comprehensively surveyed and discussed. Herein, we review the recent status of ACE2-based detection methods, the signal transduction principles in ACE2 biosensors and the development trend in the future. We discuss the challenges to development of ACE2-biosensors and delineate prospects for their use, along with recommended solutions and suggestions.

State-of-art high-performance Nano-systems for mutated coronavirus infection management: From Lab to Clinic

The emerging severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants made emerging novel coronavirus diseases (COVID-19) pandemic/endemic/or both more severe and difficult to manage due to increased worry about the efficacy and efficiency of present preventative, therapeutic, and sensing measures. To deal with these unexpected circumstances, the development of novel nano-systems with tuneable optical, electrical, magnetic, and morphological properties can lead to novel research needed for (1) COVID-19 infection (anti-microbial systems against SARS-CoV-2), (2) early detection of mutated SARS-CoV-2, and (3) targeted delivery of therapeutics using nano-systems, i.e., nanomedicine. However, there is a knowledge gap in understanding all these nano-biotechnology potentials for managing mutated SARS-CoV-2 on a single platform. To bring up the aspects of nanotechnology to tackle SARS-CoV-2 variants related COVID-19 pandemic, this article emphasizes improvements in the high-performance of nano-systems to combat SARS-CoV-2 strains/variants with a goal of managing COVID-19 infection via trapping, eradication, detection/sensing, and treatment of virus. The potential of state-of-the-art nano-assisted approaches has been demonstrated as an efficient drug delivery systems, viral disinfectants, vaccine productive cargos, anti-viral activity, and biosensors suitable for point-of-care (POC) diagnostics. Furthermore, the process linked with the efficacy of nanosystems to neutralize and eliminate SARS-CoV-2 is extensively highligthed in this report. The challenges and opportunities associated with managing COVID-19 using nanotechnology as part of regulations are also well-covered. The outcomes of this review will help researchers to design, investigate, and develop an appropriate nano system to manage COVID-19 infection, with a focus on the detection and eradication of SARS-CoV-2 and its variants. This article is unique in that it discusses every aspect of high-performance nanotechnology for ideal COVID pandemic management.

Calibration-free, high-precision, and robust terahertz ultrafast metasurfaces for monitoring gastric cancers

Optical sensors, with great potential to convert invisible bioanalytical response into readable information, have been envisioned as a powerful platform for biological analysis and early diagnosis of diseases. However, the current extraction of sensing data is basically processed via a series of complicated and time-consuming calibrations between samples and reference, which inevitably introduce extra measurement errors and potentially annihilate small intrinsic responses. Here, we have proposed and experimentally demonstrated a calibration-free sensor for achieving high-precision biosensing detection, based on an optically controlled terahertz (THz) ultrafast metasurface. Photoexcitation of the silicon bridge enables the resonant frequency shifting from 1.385 to 0.825 THz and reaches the maximal phase variation up to 50° at 1.11 THz. The typical environmental measurement errors are completely eliminated in theory by normalizing the Fourier-transformed transmission spectra between ultrashort time delays of 37 ps, resulting in an extremely robust sensing device for monitoring the cancerous process of gastric cells. We believe that our calibration-free sensors with high precision and robust advantages can extend their implementation to study ultrafast biological dynamics and may inspire considerable innovations in the field of medical devices with nondestructive detection.

Current and Perspective Sensing Methods for Monkeypox Virus

The outbreak of the monkeypox virus (MPXV) in non-endemic countries is an emerging global health threat and may have an economic impact if proactive actions are not taken. As shown by the COVID-19 pandemic, rapid, accurate, and cost-effective virus detection techniques play a pivotal role in disease diagnosis and control. Considering the sudden multicountry MPXV outbreak, a critical evaluation of the MPXV detection approaches would be a timely addition to the endeavors in progress for MPXV control and prevention. Herein, we evaluate the current MPXV detection methods, discuss their pros and cons, and provide recommended solutions to the problems. We review the traditional and emerging nucleic acid detection approaches, immunodiagnostics, whole-particle detection, and imaging-based MPXV detection techniques. The insights provided in this article will help researchers to develop novel techniques for the diagnosis of MPXV.

Nanomaterials-based sensors for the detection of COVID-19: A review

With the threat of increasing SARS-CoV-2 cases looming in front of us and no effective and safest vaccine available to curb this pandemic disease due to its sprouting variants, many countries have undergone a lockdown 2.0 or planning a lockdown 3.0. This has upstretched an unprecedented demand to develop rapid, sensitive, and highly selective diagnostic devices that can quickly detect coronavirus (COVID-19). Traditional techniques like polymerase chain reaction have proven to be time-inefficient, expensive, labor intensive, and impracticable in remote settings. This shifts the attention to alternative biosensing devices that can be successfully used to sense the COVID-19 infection and curb the spread of coronavirus cases. Among these, nanomaterial-based biosensors hold immense potential for rapid coronavirus detection because of their noninvasive and susceptible, as well as selective properties that have the potential to give real-time results at an economical cost. These diagnostic devices can be used for mass COVID-19 detection to understand the rapid progression of the infection and give better-suited therapies. This review provides an overview of existing and potential nanomaterial-based biosensors that can be used for rapid SARS-CoV-2 diagnostics. Novel biosensors employing different detection mechanisms are also highlighted in different sections of this review. Practical tools and techniques required to develop such biosensors to make them reliable and portable have also been discussed in the article. Finally, the review is concluded by presenting the current challenges and future perspectives of nanomaterial-based biosensors in SARS-CoV-2 diagnostics.

The Path of Rural Industry Revitalization Based on Improved Genetic Algorithm in the Internet Era

With the implementation of the Rural Revitalization Strategy, China has entered a critical stage of transformation and development. The Fifth Plenary Session of the 19th CPC Central Committee made a major strategic deployment for solidly promoting common prosperity, and clearly put forward that by 2035, the common prosperity of all people will make more obvious substantive progress. In addition, this study also proposes to continue to strengthen the construction of rural grass-roots party organizations; select and strengthen township leading groups, members of the village "two committees," especially the Secretary of the grass-roots party organization; give full play to the role of the fighting fortress of the rural grass-roots party organization; and improve the villagers' autonomy mechanism under the leadership of the party organization. In places where conditions permit, we should actively promote the Secretary of the village Party organization to serve as the director of the villagers' committee through legal procedures and strengthen the supervision of "one shoulder" cadres of the village "two committees". We will continue to send the first secretaries and task forces to key villages. We should put more resources in towns and villages, effectively reduce the burden on grass-roots organizations, and let grass-roots cadres devote more energy to helping farmers solve practical difficulties.

Performance Optimization of Surface Plasmon Resonance Imaging Sensor Network Based on the Multi-Objective Optimization Algorithm

In this work, we report performance optimization of a wireless sensor network (WSN) based on the plain silver surface plasmon resonance imaging (SPRi) sensor. At the sensor node level, we established the refractive index-thickness models for both gold and silver in the sensor and calculated the depth-width ratio (DWR) and penetration depth (PD) values of the sensor of different gold and silver thicknesses by the Jones transfer matrix and Kriging interpolation. We optimized the DWR and PD simultaneously by using the multi-objective optimization genetic algorithm (MOGA). In the following performance optimization of WSN, we simultaneously optimized the transmission success rate and information dimension with the number of nodes and transmission failure rate of the sensor node as variables by the same algorithm. By calculating the information dimension and the transmission success rate of each Pareto optimal solution, we obtained the number of nodes and transmission failure probability of the node available for practical deployment of WSN. The above results indicate that the Pareto optimal solution set obtained from MOGA can help to provide the best solution for the optimization of some certain performance parameters and also assist us in making the trade-off decision in the structure design and network deployment if optimal values of all the performance parameters can be obtained simultaneously.

Smart Approach for the Design of Highly Selective Aptamer-Based Biosensors

Aptamers are chemically synthesized single-stranded DNA or RNA oligonucleotides widely used nowadays in sensors and nanoscale devices as highly sensitive biorecognition elements. With proper design, aptamers are able to bind to a specific target molecule with high selectivity. To date, the systematic evolution of ligands by exponential enrichment (SELEX) process is employed to isolate aptamers. Nevertheless, this method requires complex and time-consuming procedures. In silico methods comprising machine learning models have been recently proposed to reduce the time and cost of aptamer design. In this work, we present a new in silico approach allowing the generation of highly sensitive and selective RNA aptamers towards a specific target, here represented by ammonium dissolved in water. By using machine learning and bioinformatics tools, a rational design of aptamers is demonstrated. This “smart” SELEX method is experimentally proved by choosing the best five aptamer candidates obtained from the design process and applying them as functional elements in an electrochemical sensor to detect, as the target molecule, ammonium at different concentrations. We observed that the use of five different aptamers leads to a significant difference in the sensor’s response. This can be explained by considering the aptamers’ conformational change due to their interaction with the target molecule. We studied these conformational changes using a molecular dynamics simulation and suggested a possible explanation of the experimental observations. Finally, electrochemical measurements exposing the same sensors to different molecules were used to confirm the high selectivity of the designed aptamers. The proposed in silico SELEX approach can potentially reduce the cost and the time needed to identify the aptamers and potentially be applied to any target molecule.

Artificial Intelligence (AI) and Internet of Medical Things (IoMT) Assisted Biomedical Systems for Intelligent Healthcare

Artificial intelligence (AI) is a modern approach based on computer science that develops programs and algorithms to make devices intelligent and efficient for performing tasks that usually require skilled human intelligence. AI involves various subsets, including machine learning (ML), deep learning (DL), conventional neural networks, fuzzy logic, and speech recognition, with unique capabilities and functionalities that can improve the performances of modern medical sciences. Such intelligent systems simplify human intervention in clinical diagnosis, medical imaging, and decision-making ability. In the same era, the Internet of Medical Things (IoMT) emerges as a next-generation bio-analytical tool that combines network-linked biomedical devices with a software application for advancing human health. In this review, we discuss the importance of AI in improving the capabilities of IoMT and point-of-care (POC) devices used in advanced healthcare sectors such as cardiac measurement, cancer diagnosis, and diabetes management. The role of AI in supporting advanced robotic surgeries developed for advanced biomedical applications is also discussed in this article. The position and importance of AI in improving the functionality, detection accuracy, decision-making ability of IoMT devices, and evaluation of associated risks assessment is discussed carefully and critically in this review. This review also encompasses the technological and engineering challenges and prospects for AI-based cloud-integrated personalized IoMT devices for designing efficient POC biomedical systems suitable for next-generation intelligent healthcare.

Implementation of Combined Machine Learning with the Big Data Model in IoMT Systems for the Prediction of Network Resource Consumption and Improving the Data Delivery

In recent years, health applications based on the internet of medical things have exploded in popularity in smart cities (IoMT). Many real-time systems help both patients and professionals by allowing remote data access and appropriate responses. The major research problems include making timely medical judgments and efficiently managing massive data utilising IoT-based resources. Furthermore, in many proposed solutions, the dispersed nature of data processing openly raises the risk of information leakage and compromises network integrity. Medical sensors are burdened by such solutions, which reduce the stability of real-time transmission systems. As a result, this study provides a machine-learning approach with SDN-enabled security to forecast network resource usage and enhance sensor data delivery. With a low administration cost, the software define network (SDN) design allows the network to resist dangers among installed sensors. It provides an unsupervised machine learning approach that reduces IoT network communication overheads and uses dynamic measurements to anticipate link status and refines its tactics utilising SDN architecture. Finally, the SDN controller employs a security mechanism to efficiently regulate the consumption of IoT nodes while also protecting them against unidentified events. When the number of nodes and data production rate varies, the suggested approach enhances network speed. As a result, to organise the nodes in a cluster, the suggested model uses an iterative centroid technique. By balancing network demand via durable connections, the multihop transmission technique for routing IoT data improves speed while simultaneously lowering the energy hole problem.

Internet of Medical Things (IoMT)-Based Smart Healthcare System: Trends and Progress

Internet of Medical Thing (IoMT) is the most emerging era of the Internet of Thing (IoT), which is exponentially gaining researchers' attention with every passing day because of its wide applicability in Smart Healthcare systems (SHS). Because of the current pandemic situation, it is highly risky for an individual to visit the doctor for every small problem. Hence, using IoMT devices, we can easily monitor our day-to-day health records, and thereby initial precautions can be taken on our own. IoMT is playing a crucial role within the healthcare industry to increase the accuracy, reliability, and productivity of electronic devices. This research work provides an overview of IoMT with emphasis on various enabling techniques used in smart healthcare systems (SHS), such as radio frequency identification (RFID), artificial intelligence (AI), and blockchain. We are providing a comparative analysis of various IoMT architectures proposed by several researchers. Also, we have defined various health domains of IoMT, including the analysis of different sensors with their application environment, merits, and demerits. In addition, we have figured out key protocol design challenges, which are to be considered during the implementation of an IoMT network-based smart healthcare system. Considering these challenges, we prepared a comparative study for different data collection techniques that can be used to maintain the accuracy of collected data. In addition, this research work also provides a comprehensive study for maintaining the energy efficiency of an AI-based IoMT framework based on various parameters, such as the amount of energy consumed, packet delivery ratio, battery lifetime, quality of service, power drain, network throughput, delay, and transmission rate. Finally, we have provided different correlation equations for finding the accuracy and efficiency within the IoMT-based healthcare system using artificial intelligence. We have compared different data collection algorithms graphically based on their accuracy and error rate. Similarly, different energy efficiency algorithms are also graphically compared based on their energy consumption and packet loss percentage. We have analyzed our references used in this study, which are graphically represented based on their distribution of publication year and publication avenue.

Microfluidic Sliding Paper-Based Device for Point-of-Care Determination of Albumin-to-Creatine Ratio in Human Urine

A novel assay platform consisting of a microfluidic sliding double-track paper-based chip and a hand-held Raspberry Pi detection system is proposed for determining the albumin-to-creatine ratio (ACR) in human urine. It is a clinically important parameter and can be used for the early detection of related diseases, such as renal insufficiency. In the proposed method, the sliding layer of the microchip is applied and the sample diffuses through two parallel filtration channels to the reaction/detection areas of the microchip to complete the detection reaction, which is a simple method well suited for self-diagnosis of ACR index in human urine. The RGB (red, green, and blue) value intensity signals of the reaction complexes in these two reaction zones are analyzed by a Raspberry Pi computer to derive the ACR value (ALB and CRE concentrations). It is shown that the G + B value intensity signal is linearly related to the ALB and CRE concentrations with the correlation coefficients of R2 = 0.9919 and R2 = 0.9923, respectively. It is additionally shown that the ALB and CRE concentration results determined using the proposed method for 23 urine samples were collected from real suffering chronic kidney disease (CKD) patients are in fine agreement with those acquired operating a traditional high-reliability macroscale method. Overall, for point-of-care (POC) CKD diagnosis and monitoring in clinical applications, the results prove that the proposed method offers a convenient, real time, reliable, and low-spending solution for POC CKD diagnosis.

Current Scenario of Pathogen Detection Techniques in Agro-Food Sector

Over the past-decade, agricultural products (such as vegetables and fruits) have been reported as the major vehicles for foodborne diseases, which are limiting food resources. The spread of infectious diseases due to foodborne pathogens poses a global threat to human health and the economy. The accurate and timely detection of infectious disease and of causative pathogens is crucial in the prevention and treatment of disease. Negligence in the detection of pathogenic substances can be catastrophic and lead to a pandemic. Despite the revolution in health diagnostics, much attention has been paid to the agro-food sector regarding the detection of food contaminants (such as pathogens). The conventional analytical techniques for pathogen detection are reliable and still in operation. However, laborious procedures and time-consuming detection via these approaches emphasize the need for simple, easy-to-use, and affordable detection techniques. The rapid detection of pathogens from food is essential to avoid the morbidity and mortality originating from the suboptimal nature of empiric pathogen treatment. This review critically discusses both the conventional and emerging bio-molecular approaches for pathogen detection in agro-food.

Research Progress and Future Trends of Microfluidic Paper-Based Analytical Devices in In-Vitro Diagnosis

In vitro diagnosis (IVD) has become a hot topic in laboratory research and achievement transformation. However, due to the high cost, and time-consuming and complex operation of traditional technologies, some new technologies are being introduced into IVD, to solve the existing problems. As a result, IVD has begun to develop toward point-of-care testing (POCT), a subdivision field of IVD. The pandemic has made governments and health institutions realize the urgency of accelerating the development of POCT. Microfluidic paper-based analytical devices (μPADs), a low-cost, high-efficiency, and easy-to-operate detection platform, have played a significant role in advancing the development of IVD. μPADs are composed of paper as the core material, certain unique substances as reagents for processing the paper, and sensing devices, as auxiliary equipment. The published reviews on the same topic lack a comprehensive and systematic introduction to μPAD classification and research progress in IVD segmentation. In this paper, we first briefly introduce the origin of μPADs and their role in promoting IVD, in the introduction section. Then, processing and detection methods for μPADs are summarized, and the innovative achievements of μPADs in IVD are reviewed. Finally, we discuss and prospect the upgrade and improvement directions of μPADs, in terms of portability, sensitivity, and automation, to help researchers clarify the progress and overcome the difficulties in subsequent μPAD research.

Emerging Biosensing Technologies for the Diagnostics of Viral Infectious Diseases

Several viral infectious diseases appear limitless since the beginning of the 21^st century, expanding into pandemic lengths. Thus, there are extensive efforts to provide more efficient means of diagnosis, a better understanding of acquired immunity, and improved monitoring of inflammatory biomarkers, as these are all crucial for controlling the spread of infection while aiding in vaccine development and improving patient outcomes. In this regard, various biosensors have been developed recently to streamline pathogen and immune response detection by addressing the limitations of traditional methods, including isothermal amplification-based systems and lateral flow assays. This review explores state-of-the-art biosensors for detecting viral pathogens, serological assays, and inflammatory biomarkers from the material perspective, by discussing their advantages, limitations, and further potential regarding their analytical performance, clinical utility, and point-of-care adaptability. Additionally, next-generation biosensing technologies that offer better sensitivity and selectivity, and easy handling for end-users are highlighted. An emerging example of these next-generation biosensors are those powered by novel synthetic biology tools, such as clustered regularly interspaced short palindromic repeats (CRISPR) with CRISPR-associated proteins (Cas), in combination with integrated point-of-care devices. Lastly, the current challenges are discussed and a roadmap for furthering these advanced biosensing technologies to manage future pandemics is provided.

A Secure Framework toward IoMT-Assisted Data Collection, Modeling, and Classification for Intelligent Dermatology Healthcare Services

The abnormal growth of the skin cells is known as skin cancer. It is one of the main problems in the dermatology area. Skin lesions or malignancies have been a source of worry for many individuals in recent years. Irrespective of the skin tone, there exist three major classes of skin lesions, i.e., basal cell carcinoma, squamous cell carcinoma, and melanoma. The early diagnosis of these lesions is equally important for human life. In the proposed work, a secure IoMT-Assisted framework is introduced that can help the patients to do the initial screening of skin lesions remotely. The initially proposed approach uses an IoMT-based data collection device which is accessible by patients to capture skin lesions images. Next, the captured skin sample is encrypted and sent to the collected image toward cloud storage. Later, the received sample image is classified into appropriate class labels using an ensemble classifier. In the proposed framework, four CNN models were ensemble i.e., VGG-16, DenseNet-201, Inception-V3, and Efficient-B7. The framework has experimented with the “HAM10000” dataset having 7 different kinds of skin lesions data. Although DenseNet-201 performed well, the ensemble model provides the highest accuracy with 87.22 percent as well as its test loss/error is lower than others with 0.4131. Moreover, the ensemble model's classification ability is much higher with an AUC score of 0.9745. Moreover, A recommendation team has been assigned to assess the sample of the patient as well as suggest the patient according to classified results by the CAD.

Innovations and Challenges in Electroanalytical Tools for Rapid Biosurveillance of SARS-CoV-2

Since the onset of the coronavirus disease 2019 (COVID-19) pandemic, preventive social paradigms and vaccine development have undergone serious renovations, which drastically reduced the viral spread and increased collective immunity. Although the technological advancements in diagnostic systems for severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) detection are groundbreaking, the lack of sensitive, robust, and consumer-end point-of-care (POC) devices with smartphone connectivity are conspicuously felt. Despite its revolutionary impact on biotechnology and molecular diagnostics, the reverse transcription polymerase chain reaction technique as the gold standard in COVID-19 diagnosis is not suitable for rapid testing. Today's POC tests are dominated by the lateral flow assay technique, with inadequate sensitivity and lack of internet connectivity. Herein, the biosensing advancements in Internet of Things (IoT)-integrated electroanalytical tools as superior POC devices for SARS-CoV-2 detection will be demonstrated. Meanwhile, the impeding factors pivotal for the successful deployment of such novel bioanalytical devices, including the incongruous standards, redundant guidelines, and the limitations of IoT modules will be discussed.

Advances in Nanomaterial-Based Platforms to Combat COVID-19: Diagnostics, Preventions, Therapeutics, and Vaccine Developments

The COVID-19 pandemic caused by the SARS-CoV-2, a ribonucleic acid (RNA) virus that emerged less than two years ago but has caused nearly 6.1 million deaths to date. Recently developed variants of the SARS-CoV-2 virus have been shown to be more potent and expanded at a faster rate. Until now, there is no specific and effective treatment for SARS-CoV-2 in terms of reliable and sustainable recovery. Precaution, prevention, and vaccinations are the only ways to keep the pandemic situation under control. Medical and scientific professionals are now focusing on the repurposing of previous technology and trying to develop more fruitful methodologies to detect the presence of viruses, treat the patients, precautionary items, and vaccine developments. Nanomedicine or nanobased platforms can play a crucial role in these fronts. Researchers are working on many effective approaches by nanosized particles to combat SARS-CoV-2. The role of a nanobased platform to combat SARS-CoV-2 is extremely diverse (i.e., mark to personal protective suit, rapid diagnostic tool to targeted treatment, and vaccine developments). Although there are many theoretical possibilities of a nanobased platform to combat SARS-CoV-2, until now there is an inadequate number of research targeting SARS-CoV-2 to explore such scenarios. This unique mini-review aims to compile and elaborate on the recent advances of nanobased approaches from prevention, diagnostics, treatment to vaccine developments against SARS-CoV-2, and associated challenges.

Design and simulation of a millifluidic device for differential detection of SARS-CoV-2 and H1N1 based on triboelectricity

Differential diagnosis of pathogenic diseases, presently coronavirus disease 2019 (COVID-19) and influenza, is crucial with due attention to their superspreading events, presumably long incubation period, particular complications, and treatments. In this paper, a label-free, self-powered, and ultrafast immunosensor device working based on triboelectric effect is proposed. Equilibrium constants of specific antibody-antigen reactions are accompanied by IEP-relevant electric charges of antigens to recognize SARS-CoV-2 and H1N1. Simulation attributes including fluid flow and geometrical parameters are optimized so that the maximum capture efficiency of 85.63% is achieved. Accordingly, antibody-antigen complexes form electric double layers (EDLs) across the channel interfaces. The resultant built-in electric field affects the following external electric field derived from triboelectricity, leading to the variation of open-circuit voltage as a sensing metric. The device is flexible to operate in conductor-to-dielectric single-electrode and contact-separation modes simultaneously. While the detection limit is reduced utilizing the single-electrode mode compared to the latter one, surface treatment of the triboelectric pair contributes to the sensitivity enhancement. A threshold value equal to −4.113 V is featured to discriminate these two viruses in a vast detectable region; however, further surface engineering can allow the on-site detection of any electrically-charged pathogen applying the emerging triboelectric immunosensor enjoying a lower detection limit.

Diagnostic accuracy of fluorescence flow-cytometry technology using Sysmex XN-31 for imported malaria in a non-endemic setting

Malaria diagnosis based on microscopy is impaired by the gradual disappearance of experienced microscopists in non-endemic areas. Aside from the conventional diagnostic methods, fluorescence flow cytometry technology using Sysmex XN-31, an automated haematology analyser, has been registered to support malaria diagnosis. The aim of this prospective, monocentric, non-interventional study was to evaluate the diagnostic accuracy of the XN-31 for the initial diagnosis or follow-up of imported malaria cases compared to the reference malaria tests including microscopy, loop mediated isothermal amplification, and rapid diagnostic tests. Over a one-year period, 357 blood samples were analysed, including 248 negative and 109 positive malaria samples. Compared to microscopy, XN-31 showed sensitivity of 100% (95% CI: 97.13–100) and specificity of 98.39% (95% CI: 95.56–100) for the initial diagnosis of imported malaria cases. Moreover, it provided accurate species identification asfalciparumor non-falciparumand parasitaemia determination in a very short time compared to other methods. We also demonstrated that XN-31 was a reliable method for patient follow-up on days 3, 7, and 28. Malaria diagnosis can be improved in non-endemic areas by the use of dedicated haematology analysers coupled with standard microscopy or other methods in development, such as artificial intelligence for blood slide reading. Given that XN-31 provided an accurate diagnosis in 1 min, it may reduce the time interval before treatment and thus improve the outcome of patient who have malaria.

Recent Advances in Electrochemical Biosensors for the Detection of Salmonellosis: Current Prospective and Challenges

Salmonellosis is a major cause of foodborne infections, caused by Salmonella, posing a major health risk. It possesses the ability to infiltrate the food supply chain at any point throughout the manufacturing, distribution, processing or quality control process. Salmonella infection has increased severely and requires effective and efficient methods for early monitoring and detection. Traditional methods, such as real-time polymerase chain reaction and culture plate, consume a lot of time and are labor-intensive. Therefore, new quick detection methods for on-field applications are urgently needed. Biosensors provide consumer-friendly approaches for quick on-field diagnoses. In the last few years, there has been a surge in research into the creation of reliable and advanced electrochemical sensors for the detection of Salmonella strains in food samples. Electrochemical sensors provide extensive accuracy and reproducible results. Herein, we present a comprehensive overview of electrochemical sensors for the detection of Salmonella by focusing on various mechanisms of electrochemical transducer. Further, we explain new-generation biosensors (microfluidics, CRISPR- and IOT-based) for point-of care applications. This review also highlights the limitations of developing biosensors in Salmonella detection and future possibilities.

Searching for Sustainability in Health Systems: Toward a Multidisciplinary Evaluation of Mobile Health Innovations

Mobile health (mHealth) innovations are considered by governments as game changers toward more sustainable health systems. The existing literature focuses on the clinical aspects of mHealth but lacks an integrated framework on its sustainability. The foundational idea for this paper is to include disciplinary complementarities into a multi-dimensional vision to evaluate the non-clinical aspects of mHealth innovations. We performed a targeted literature review to find how the sustainability of mHealth innovations was appraised in each discipline. We found that each discipline considers a different outcome of interest and adopts different time horizons and perspectives for the evaluation. This article reflects on how the sustainability of mHealth innovation can be assessed at both the level of the device itself as well as the level of the health system. We identify some of the challenges ahead of researchers working on mobile health innovations in contributing to shaping a more sustainable health system.

Toward smart diagnosis of pandemic infectious diseases using wastewater-based epidemiology

COVID-19 outbreak revealed fundamental weaknesses of current diagnostic systems, particularly in prediction and subsequently prevention of pandemic infectious diseases (PIDs). Among PIDs detection methods, wastewater-based epidemiology (WBE) has been demonstrated to be a favorable mean for estimation of community-wide health. Besides, by going beyond purely sensing usages of WBE, it can be efficiently exploited in Healthcare 4.0/5.0 for surveillance, monitoring, control, and above all prediction and prevention, thereby, resulting in smart sensing and management of potential outbreaks/epidemics/pandemics. Herein, an overview of WBE sensors for PIDs is presented. The philosophy behind the smart diagnosis of PIDs using WBE with the help of digital technologies is then discussed, as well as their characteristics to be met. Analytical techniques that are pushing the frontiers of smart sensing and have a high potential to be used in the smart diagnosis of PIDs via WBE are surveyed. In this context, we underscore key challenges ahead and provide recommendations for implementing and moving faster toward smart diagnostics.

Translation of aptamers toward clinical diagnosis and commercialization

The dominance of antibodies in diagnostics has gradually changed following the discovery of aptamers in the early 1990s. Aptamers offer inherent advantages over traditional antibodies, including higher specificity, higher affinity, smaller size, greater stability, ease of manufacture, and low immunogenicity, rendering them the best candidates for point-of-care testing (POCT). In the past 20 years, the research community and pharmaceutical companies have made great efforts to promote the development of aptamer technology. Macugen® (pegaptanib) was the first aptamer drug approved by the US Food and Drug Administration (FDA), and various aptamer-based diagnostics show great promise in preclinical research and clinical trials. In this review, we introduce recent literature, ongoing clinical trials, commercial reagents of aptamer-based diagnostics, discuss the FDA regulatory mechanisms, and highlight the prospects and challenges in translating these studies into viable clinical diagnostic tools.

Highly Stable Buffer-Based Zinc Oxide/Reduced Graphene Oxide Nanosurface Chemistry for Rapid Immunosensing of SARS-CoV-2 Antigens

The widespread and long-lasting effect of the COVID-19 pandemic has called attention to the significance of technological advances in the rapid diagnosis of SARS-CoV-2 virus. This study reports the use of a highly stable buffer-based zinc oxide/reduced graphene oxide (bbZnO/rGO) nanocomposite coated on carbon screen-printed electrodes for electrochemical immuno-biosensing of SARS-CoV-2 nuelocapsid (N-) protein antigens in spiked and clinical samples. The incorporation of a salt-based (ionic) matrix for uniform dispersion of the nanomixture eliminates multistep nanomaterial synthesis on the surface of the electrode and enables a stable single-step sensor nanocoating. The immuno-biosensor provides a limit of detection of 21 fg/mL over a linear range of 1-10 000 pg/mL and exhibits a sensitivity of 32.07 ohms·mL/pg·mm² for detection of N-protein in spiked samples. The N-protein biosensor is successful in discriminating positive and negative clinical samples within 15 min, demonstrating its proof of concept used as a COVID-19 rapid antigen test.

Potentialities of graphene and its allied derivatives to combat against SARS-CoV-2 infection

Graphene is a two-dimensional material with sp2 hybridization that has found its broad-spectrum potentialities in various domains like electronics, robotics, aeronautics, etc.; it has recently gained its utilities in the biomedical domain. The unique properties of graphene and its derivatives of graphene have helped them find their utilities in the biomedical domain. Additionally, the sudden outbreak of SARS-CoV-2 has immensely expanded the research field, which has also benefitted graphene and its derivatives. Currently, the world is facing a global pandemic due to the sudden outbreak of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), also known as COVID-19, from its major onset in Wuhan city, China, in December 2019. Presently, many new variants and mutants appear, which is more harmful than previous strains. However, researchers and scientists are focused on understanding the target structure of coronavirus, mechanism, causes and transmission mode, treatment, and alternatives to cure these diseases in this critical pandemic situation; many findings are achieved, but much more is unknown and pending to be explored. This review paper is dedicated to exploring the utilities of graphene and its derivatives in combating the SARS-CoV-2 by highlighting their mechanism and applications in the fabrication of biosensors, personal protection equipment (PPE) kits, 3-D printing, and antiviral coatings. Further, the paper also covers the cytotoxicity caused by graphene and its derivatives and highlights the graphene-based derivatives market aspects in biomedical domains. Thus, graphene and graphene-derived materials are our new hope in this pandemic time, and this review helps acquire broad knowledge about them.

SARS-CoV-2 Omicron variant: A next phase of the COVID-19 pandemic and a call to arms for system sciences and precision medicine

Since early 2020, coronavirus diseases 2019 (COVID-19) infection pandemic/endemic is constantly surprising health experts because of continuous variations in the structures of severe acute respiratory coronavirus 2 (SARS-CoV-2) in the form of newly emerged variants. Such mutations have exhibited high mortality and severity due to the newly emerged more infectious sites of SARS-CoV-2, making viral infection more transmissible, infectious, and severe. Recently, SARS-CoV-2 mutated to another variant, namely, Omicron (B.1.1.529), which is many times more transmissible and infectious than existed deadly Delta variants of the virus. This severity is closely correlated to a larger number of mutations observed in the receptor-binding domain of the spike protein of the Omicron-SARS-CoV-2. Considering severity, Omicron has been declared as variant of concerns by the World Health Organization and within days from its emergence, Omicron infection has spread globally, increased hospitalization, exhibited more severity for the young generation, invaded defense mechanism of natural immunity, not responsive to the available vaccines. Such circumstances resonated with the efficiency of available strategies established to manage COVID-19 intelligently and successfully. To explore these aspects, this perspective article carefully and critically summarizes the Omicron's origin, structure, pathogenesis, impact health along with health systems, and experts' recommendations to manage it successfully.

Multi-reagents dispensing centrifugal microfluidics for point-of-care testing

Point-of-care testing (POCT) has shown great advantages for public health monitoring in resource-limited settings. However, developing of POCT tools with automated and accurate quantitative dispensing of multiple reagents and samples is challenging. Here, we demonstrate a novel multi-reagents dispensing centrifugal microfluidics (MDCM) that allows rapid and automated dispensing of multiple reagents and samples with high throughput and accuracy. The MDCM was designed with multiple aliquoting units with the hydrophobic valve at different radial positions. All reagents and samples were loaded simultaneously, dispensed in parallel by centrifugation at low speed, and then introduced into the reaction chamber sequentially by centrifugation at high speed. Two MDCM chips are demonstrated, including a uniform concentration generator and a gradient concentration generator. The concentration coefficient of variation (CV) among the independent reaction chambers was lower than 0.56%, and the theoretical quantitative concentration gradient was strongly correlated with the actual concentration gradient (R² = 0.9938). We have successfully applied the MDCM to loop-mediated isothermal amplification (LAMP)-based nucleic acid detection for multiple infectious pathogens and antimicrobial susceptibility testing (AST) for kanamycin sulfate against E. coli. To further extend the applications, the MDCM has also been applied to bicinchoninic acid (BCA) protein assays with online calibration, reducing the detection time from 2 h to 10 min with a twenty-fold reduction in reagent consumption. These results indicated that the MDCM is a high potential platform for POCT.

Open-Source, Adaptable, All-in-One Smartphone-Based System for Quantitative Analysis of Point-of-Care Diagnostics

Point-of-care (POC) diagnostics, in particular lateral flow assays (LFA), represent a great opportunity for rapid, precise, low-cost and accessible diagnosis of disease. Especially with the ongoing coronavirus disease 2019 (COVID-19) pandemic, rapid point-of-care tests are becoming everyday tools for identification and prevention. Using smartphones as biosensors can enhance POC devices as portable, low-cost platforms for healthcare and medicine, food and environmental monitoring, improving diagnosis and documentation in remote, low-resource locations. We present an open-source, all-in-one smartphone-based system for quantitative analysis of LFAs. It consists of a 3D-printed photo box, a smartphone for image acquisition, and an R Shiny software package with modular, customizable analysis workflow for image editing, analysis, data extraction, calibration and quantification of the assays. This system is less expensive than commonly used hardware and software, so it could prove very beneficial for diagnostic testing in the context of pandemics, as well as in low-resource countries.

Borophene as an emerging 2D flatland for biomedical applications: current challenges and future prospects

Recently, two-dimensional (2D)-borophene has emerged as a remarkable translational nanomaterial substituting its predecessors in the field of biomedical sensors, diagnostic tools, high-performance healthcare devices, super-capacitors, and energy storage devices. Borophene justifies its demand due to high-performance and controlled optical, electrical, mechanical, thermal, and magnetic properties as compared with other 2D-nanomaterials. However, continuous efforts are being made to translate theoretical and experimental knowledge into pragmatic platforms. To cover the associated knowledge gap, this review explores the computational and experimental chemistry needed to optimize borophene with desired properties. High electrical conductivity due to destabilization of the highest occupied molecular orbital (HOMO), nano-engineering at the monolayer level, chemistry-oriented biocompatibility, and photo-induced features project borophene for biosensing, bioimaging, cancer treatment, and theragnostic applications. Besides, the polymorphs of borophene have been useful to develop specific bonding for DNA sequencing and high-performance medical equipment. In this review, an overall critical and careful discussion of systematic advancements in borophene-based futuristic biomedical applications including artificial intelligence (AI), Internet-of-Things (IoT), and Internet-of-Medical Things (IoMT) assisted smart devices in healthcare to develop high-performance biomedical systems along with challenges and prospects is extensively addressed. Consequently, this review will serve as a key supportive platform as it explores borophene for next-generation biomedical applications. Finally, we have proposed the potential use of borophene in healthcare management strategies.

Increasing demand for point-of-care testing and the potential to incorporate the Internet of medical things in an integrated health management system

As the number of people with COVID-19 increases daily around the world, point-of-care testing (POCT) is gaining attention as a tool that can provide immediate test results and greatly help to deter infection and determine what to do next. POCT has several drawbacks such as a low sensitivity and specificity, but according to studies POCT has increased sensitivity on par with that of polymerase chain reaction testing. The advantage of POCT is that the results can be obtained quickly, regardless of the location. To further enhance its benefits, POCT is being developed and researched in conjunction with the Internet of medical things (IoMT), which allows POCT results to be collected, recorded, and managed over a network. IoMT will be beneficial not only for the use of POCT simply as a testing tool but also for its integration into diagnostic and health management systems. IoMT will enable people to regularly receive their test results in their daily lives and to provide personalized diagnosis and treatment of individual conditions, which will be beneficial in terms of disease prevention and maintenance of health.

Crystal-reconstructed BiVO4 semiconductor photoelectrochemical sensor for ultra-sensitive tumor biomarker detection

In this study, we developed a crystal-reconstructed-BiVO₄ aptamer photoelectrochemical (PEC) biosensor by a high-energy laser treatment technique. This biosensor achieves a limit of detection (LOD) (0.82 ag mL^-1), linear detection range (1 ag mL^-1 to 2 ng mL^-1), and resolution ratio (∼18 molecules per mL) for prostate-specific antigen (PSA) tumor biomarker detection. Furthermore, reconstructed surface microstructure and oxygen vacancy doping energy formation after crystal reconstruction induce the stereo-hindrance effect and photogenerated hole energy is reduced during PSA target detection. In this case, a photocurrent inhibition phenomenon for PSA detection is noticed. Based on this photocurrent inversion phenomenon, some dysoxidizable nucleonic acid tumor (miRNA-21) and virus biomarkers (RdRp-COVID) can be detected with a LOD level of ∼10^-16 M by linking the corresponding base paring probe on the surface of the crystal-reconstructed photoanode. In addition to high sensitivity, this PEC biosensor presents high detection specificity, stability, and accuracy in clinical verification. Thus, this crystal-reconstructed PEC biosensor shows application potential in the fields of multi-tumor or viral biomarker detection.

Analysis of Cardiovascular Disease Angiography Process Based on Rough Set and Internet of Things

The angiography image enhancement technology has the potential to enhance the vascular structure in the image while suppressing the background and nonvascular structures simultaneously. This technology has the ability to enhance the result as close to the real structure of blood vessels as possible. Angiographic image processing is one of the essential contents in the field of medical image processing and analysis. However, the existing cardiovascular angiography schemes suffer from various issues. In this paper, the detection process of cardiovascular angiography is studied by combining the Internet of Things and rough set technology. Firstly, this paper designs the architecture design of the cardiovascular angiography process combined with the Internet of Things technology. Secondly, this paper uses a rough set algorithm to optimize the background noise and boundary shrinkage because of the sensitivity of the contrast background noise and boundary shrinkage. Simulation results verified the applicability and efficiency of the proposed model in the cardiovascular angiography scheme. The model has been optimized during implementation. Compared with the traditional algorithm, the same image data processing speed is significantly improved to ensure the enhancement effect.

Advances and applications of nanophotonic biosensors

Nanophotonic devices, which control light in subwavelength volumes and enhance light-matter interactions, have opened up exciting prospects for biosensing. Numerous nanophotonic biosensors have emerged to address the limitations of the current bioanalytical methods in terms of sensitivity, throughput, ease-of-use and miniaturization. In this Review, we provide an overview of the recent developments of label-free nanophotonic biosensors using evanescent-field-based sensing with plasmon resonances in metals and Mie resonances in dielectrics. We highlight the prospects of achieving an improved sensor performance and added functionalities by leveraging nanostructures and on-chip and optoelectronic integration, as well as microfluidics, biochemistry and data science toolkits. We also discuss open challenges in nanophotonic biosensing, such as reducing the overall cost and handling of complex biological samples, and provide an outlook for future opportunities to improve these technologies and thereby increase their impact in terms of improving health and safety.

Exploring nano-enabled CRISPR-Cas-powered strategies for efficient diagnostics and treatment of infectious diseases

Biomedical researchers have subsequently been inspired the development of new approaches for precisely changing an organism's genomic DNA in order to investigate customized diagnostics and therapeutics utilizing genetic engineering techniques. Clustered Regulatory Interspaced Short Palindromic Repeats (CRISPR) is one such technique that has emerged as a safe, targeted, and effective pharmaceutical treatment against a wide range of disease-causing organisms, including bacteria, fungi, parasites, and viruses, as well as genetic abnormalities. The recent discovery of very flexible engineered nucleic acid binding proteins has changed the scientific area of genome editing in a revolutionary way. Since current genetic engineering technique relies on viral vectors, issues about immunogenicity, insertional oncogenesis, retention, and targeted delivery remain unanswered. The use of nanotechnology has the potential to improve the safety and efficacy of CRISPR/Cas9 component distribution by employing tailored polymeric nanoparticles. The combination of two (CRISPR/Cas9 and nanotechnology) offers the potential to open new therapeutic paths. Considering the benefits, demand, and constraints, the goal of this research is to acquire more about the biology of CRISPR technology, as well as aspects of selective and effective diagnostics and therapies for infectious illnesses and other metabolic disorders. This review advocated combining nanomedicine (nanomedicine) with a CRISPR/Cas enabled sensing system to perform early-stage diagnostics and selective therapy of specific infectious disorders. Such a Nano-CRISPR-powered nanomedicine and sensing system would allow for successful infectious illness control, even on a personal level. This comprehensive study also discusses the current obstacles and potential of the predicted technology.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s40097-022-00472-7.