Futuristic biosensors for cardiac health care: an artificial intelligence approach

Lab-on-a-chip models of the blood-brain barrier: evolution, problems, perspectives

A great progress has been made in the development and use of lab-on-a-chip devices to model and study the blood-brain barrier (BBB) in the last decade. We present the main types of BBB-on-chip models and their use for the investigation of BBB physiology, drug and nanoparticle transport, toxicology and pathology. The selection of the appropriate cell types to be integrated into BBB-on-chip devices is discussed, as this greatly impacts the physiological relevance and translatability of findings. We identify knowledge gaps, neglected engineering and cell biological aspects and point out problems and contradictions in the literature of BBB-on-chip models, and suggest areas for further studies to progress this highly interdisciplinary field. BBB-on-chip models have an exceptional potential as predictive tools and alternatives of animal experiments in basic and preclinical research. To exploit the full potential of this technique expertise from materials science, bioengineering as well as stem cell and vascular/BBB biology is necessary. There is a need for better integration of these diverse disciplines that can only be achieved by setting clear parameters for characterizing both the chip and the BBB model parts technically and functionally.

Artificial intelligence in pediatric surgery

Artificial intelligence (AI) is rapidly changing the landscape of medicine and is already being utilized in conjunction with medical diagnostics and imaging analysis. We hereby explore AI applications in surgery and examine its relevance to pediatric surgery, covering its evolution, current state, and promising future. The various fields of AI are explored including machine learning and applications to predictive analytics and decision support in surgery, computer vision and image analysis in preoperative planning, image segmentation, surgical navigation, and finally, natural language processing assist in expediting clinical documentation, identification of clinical indications, quality improvement, outcome research, and other types of automated data extraction. The purpose of this review is to familiarize the pediatric surgical community with the rise of AI and highlight the ongoing advancements and challenges in its adoption, including data privacy, regulatory considerations, and the imperative for interdisciplinary collaboration. We hope this review serves as a comprehensive guide to AI's transformative influence on surgery, demonstrating its potential to enhance pediatric surgical patient outcomes, improve precision, and usher in a new era of surgical excellence.

Diagnostic and Therapeutic Aptamers: A Promising Pathway to Improved Cardiovascular Disease Management

Despite advances in care, cardiovascular diseases remain the leading cause of death worldwide. As a result, identifying suitable biomarkers for early diagnosis and improving therapeutic and diagnostic strategies is crucial. Because of their significant advantages over other therapeutic approaches, nucleic-based therapies, particularly aptamers, are gaining increased attention. Aptamers are innovative synthetic polymers or oligomers of single-stranded DNA (ssDNA) or RNA molecules that can form 3-dimensional structures and thus interact with their targets with high specificity and affinity. Furthermore, they outperform classical protein-based antibodies in terms of in vitro selection, production, ease of modification and conjugation, high stability, low immunogenicity, and suitability for nanoparticle functionalization for targeted drug delivery. This work aims to review the advances made in the aptamers' field in biomarker detection, diagnosis, imaging, and targeted therapy, which highlight their huge potential in the management of cardiovascular diseases.

The Emergence of AI-Based Wearable Sensors for Digital Health Technology: A Review

Disease diagnosis and monitoring using conventional healthcare services is typically expensive and has limited accuracy. Wearable health technology based on flexible electronics has gained tremendous attention in recent years for monitoring patient health owing to attractive features, such as lower medical costs, quick access to patient health data, ability to operate and transmit data in harsh environments, storage at room temperature, non-invasive implementation, mass scaling, etc. This technology provides an opportunity for disease pre-diagnosis and immediate therapy. Wearable sensors have opened a new area of personalized health monitoring by accurately measuring physical states and biochemical signals. Despite the progress to date in the development of wearable sensors, there are still several limitations in the accuracy of the data collected, precise disease diagnosis, and early treatment. This necessitates advances in applied materials and structures and using artificial intelligence (AI)-enabled wearable sensors to extract target signals for accurate clinical decision-making and efficient medical care. In this paper, we review two significant aspects of smart wearable sensors. First, we offer an overview of the most recent progress in improving wearable sensor performance for physical, chemical, and biosensors, focusing on materials, structural configurations, and transduction mechanisms. Next, we review the use of AI technology in combination with wearable technology for big data processing, self-learning, power-efficiency, real-time data acquisition and processing, and personalized health for an intelligent sensing platform. Finally, we present the challenges and future opportunities associated with smart wearable sensors.

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.

Machine Learning for Improved Detection of Pathogenic E. coli in Hydroponic Irrigation Water Using Impedimetric Aptasensors: A Comparative Study

Reuse of alternative water sources for irrigation (e.g., untreated surface water) is a sustainable approach that has the potential to reduce water gaps, while increasing food production. However, when growing fresh produce, this practice increases the risk of bacterial contamination. Thus, rapid and accurate identification of pathogenic organisms such as Shiga-toxin producing Escherichia coli (STEC) is crucial for resource management when using alternative water(s). Although many biosensors exist for monitoring pathogens in food systems, there is an urgent need for data analysis methodologies that can be applied to accurately predict bacteria concentrations in complex matrices such as untreated surface water. In this work, we applied an impedimetric electrochemical aptasensor based on gold interdigitated electrodes for measuring E. coliO157:H7 in surface water for hydroponic lettuce irrigation. We developed a statistical machine-learning (SML) framework for assessing different existing SML methods to predict the E. coliO157:H7 concentration. In this study, three classes of statistical models were evaluated for optimizing prediction accuracy. The SML framework developed here facilitates selection of the most appropriate analytical approach for a given application. In the case of E. coliO157:H7 prediction in untreated surface water, selection of the optimum SML technique led to a reduction of test set RMSE by at least 20% when compared with the classic analytical technique. The statistical framework and code (open source) include a portfolio of SML models, an approach which can be used by other researchers using electrochemical biosensors to measure pathogens in hydroponic irrigation water for rapid decision support.

The Use of Sensors in Blood-Brain Barrier-on-a-Chip Devices: Current Practice and Future Directions

The application of lab-on-a-chip technologies in in vitro cell culturing swiftly resulted in improved models of human organs compared to static culture insert-based ones. These chip devices provide controlled cell culture environments to mimic physiological functions and properties. Models of the blood-brain barrier (BBB) especially profited from this advanced technological approach. The BBB represents the tightest endothelial barrier within the vasculature with high electric resistance and low passive permeability, providing a controlled interface between the circulation and the brain. The multi-cell type dynamic BBB-on-chip models are in demand in several fields as alternatives to expensive animal studies or static culture inserts methods. Their combination with integrated biosensors provides real-time and noninvasive monitoring of the integrity of the BBB and of the presence and concentration of agents contributing to the physiological and metabolic functions and pathologies. In this review, we describe built-in sensors to characterize BBB models via quasi-direct current and electrical impedance measurements, as well as the different types of biosensors for the detection of metabolites, drugs, or toxic agents. We also give an outlook on the future of the field, with potential combinations of existing methods and possible improvements of current techniques.

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).

Machine Learning in Cardiology: A Potential Real-World Solution in Low- and Middle-Income Countries

Artificial intelligence (AI) and machine learning (ML) is a promising field of cardiovascular medicine. Many AI tools have been shown to be efficacious with a high level of accuracy. Yet, their use in real life is not well established. In the era of health technology and data science, it is crucial to consider how these tools could improve healthcare delivery. This is particularly important in countries with limited resources, such as low- and middle-income countries (LMICs). LMICs have many barriers in the care continuum of cardiovascular diseases (CVD), and big portion of these barriers come from scarcity of resources, mainly financial and human power constraints. AI/ML could potentially improve healthcare delivery if appropriately applied in these countries. Expectedly, the current literature lacks original articles about AI/ML originating from these countries. It is important to start early with a stepwise approach to understand the obstacles these countries face in order to develop AI/ML-based solutions. This could be detrimental to many patients' lives, in addition to other expected advantages in other sectors, including the economy sector. In this report, we aim to review what is known about AI/ML in cardiovascular medicine, and to discuss how it could benefit LMICs.

Smart Wearables for the Detection of Cardiovascular Diseases: A Systematic Literature Review

Background: The advancement of information and communication technologies and the growing power of artificial intelligence are successfully transforming a number of concepts that are important to our daily lives. Many sectors, including education, healthcare, industry, and others, are benefiting greatly from the use of such resources. The healthcare sector, for example, was an early adopter of smart wearables, which primarily serve as diagnostic tools. In this context, smart wearables have demonstrated their effectiveness in detecting and predicting cardiovascular diseases (CVDs), the leading cause of death worldwide. Objective: In this study, a systematic literature review of smart wearable applications for cardiovascular disease detection and prediction is presented. After conducting the required search, the documents that met the criteria were analyzed to extract key criteria such as the publication year, vital signs recorded, diseases studied, hardware used, smart models used, datasets used, and performance metrics. Methods: This study followed the PRISMA guidelines by searching IEEE, PubMed, and Scopus for publications published between 2010 and 2022. Once records were located, they were reviewed to determine which ones should be included in the analysis. Finally, the analysis was completed, and the relevant data were included in the review along with the relevant articles. Results: As a result of the comprehensive search procedures, 87 papers were deemed relevant for further review. In addition, the results are discussed to evaluate the development and use of smart wearable devices for cardiovascular disease management, and the results demonstrate the high efficiency of such wearable devices. Conclusions: The results clearly show that interest in this topic has increased. Although the results show that smart wearables are quite accurate in detecting, predicting, and even treating cardiovascular disease, further research is needed to improve their use.

Novel Nanotechnology-Driven Prototypes for AI-Enriched Implanted Prosthetics Following Organ Failure

Meeting medical challenges posed by global burdens is proven to be of primary interest. One example is the COVID-19 epidemic that humankind is currently experiencing, since around December 2019. Innovation is key to respond rapidly and effectively to sanitary and health emergencies, when human lives are severely threatened. In this scenery, medical devices that can be rapidly launched in the market and manufactured at scale are crucial for saving lives. One example is a lifesaving respiratory device launched in about 10 days (Mercedes F1 team's new device based on continuous positive airway pressure devices) and rapidly approved by international agencies responsible for assuring drug and medical devices safety, in response to the COVID-19 burden. Remarkably, it is the first time in history that mankind observes disease spread reaching such high proportions, globally, in such short time scale. However, while this epidemic had, in March 2020, reached the critical figures of about 38,000 deaths and c. 738,000 infected, organ donation and transplantation patients are suffering for years, accounting for an increasing number of affected, annually. These patients are invisible for the general public. Therefore, this chapter approaches the organ donation and transplantation burden, proposing effective solutions to leverage the suffering, improving life quality of patients enduring several underlying issues, from hemodialysis complications and critical organ failure to lacking compatible donors. This, on the basis of technology repurposing, to speed up approval processes followed by international agencies responsible for assuring drug and medical devices safety, while adding innovative methods to existing technology and reducing invasiveness.

Tailored point-of-care biosensors for liquid biopsy in the field of oncology

In the field of cancer detection, technologies to analyze tumors using biomarkers circulating in fluids such as blood have developed rapidly based on liquid biopsy. A proactive approach to early cancer detection can lead to more effective treatments with minimal side effects and better long-term patient survival. However, early detection of cancer is hindered by the existing limitations of conventional cancer diagnostic methods. To enable early diagnosis and regular monitoring and improve automation, the development of integrated point-of-care (POC) and biosensors is needed. This is expected to fundamentally change the diagnosis, management, and monitoring of response to treatment of cancer. POC-based techniques will provide a way to avoid complications that occur after invasive tissue biopsy, such as bleeding, infection, and pain. The aim of this study is to provide a comprehensive view of biosensors and their clinical relevance in oncology for the detection of biomarkers with liquid biopsies of proteins, miRNA, ctDNA, exosomes, and cancer cells. The preceding discussion also illustrates the changing landscape of liquid biopsy-based cancer diagnosis through nanomaterials, machine learning, artificial intelligence, wearable devices, and sensors, many of which apply POC design principles. With the advent of sensitive, selective, and timely detection of cancer, we see the field of POC technology for cancer detection and treatment undergoing a positive paradigm shift in the foreseeable future.

Geometry design for a fully insertable glucose biosensor with multimodal optical readout

Significance

Insertable optical continuous glucose monitors (CGMs) with wearable readers are a strong option for monitoring individuals with diabetes. However, a fully insertable CGM requires a small form factor while still delivering sufficient signal to be read through tissue by an external device. Previous work has suggested that a multimodal repeating unit (barcode) approach may meet these requirements, but the biosensor geometry must be optimized to meet performance criteria.

Aim

This work details in silico trials conducted to evaluate the geometry of a fully insertable multimodal optical biosensor with respect to both optical output and species diffusion in vivo.

Approach

Monte Carlo modeling is used to evaluate the luminescent output of three presupposed biosensor designs based on size constraints for an injectable and logical placement of the bar code compartments. Specifically, the sensitivity of the luminescent output to displacement of the biosensor in the X and Y directions, overall size of the selected design, and size of an individual repeating unit are analyzed. Further, an experimentally validated multiphysics model is used to evaluate the diffusion and reaction of glucose and oxygen within the biosensor to estimate the occurrence of chemical crosstalk between the assay components.

Results

A stacked cylinder multimodal biosensor 4.4 mm in length with repeating units 0.36 mm in length was found to yield a greater luminescent output than the current "barcode" biosensor design. In addition, it was found that a biosensor with enzymatic elements does not significantly deplete glucose locally and thus does not impact the diffusion profile of glucose in adjacent compartments containing nonenzymatic assays.

Conclusions

Computational modeling was used to design the geometry of a multimodal, insertable, and optical CGM to ensure that the optical output and chemical diffusion profile are sufficient for this device to function in vivo.

A Systematic Review of Internet of Things in Clinical Laboratories: Opportunities, Advantages, and Challenges

The Internet of Things (IoT) is the network of physical objects embedded with sensors, software, electronics, and online connectivity systems. This study explores the role of IoT in clinical laboratory processes; this systematic review was conducted adhering to the PRISMA Statement 2020 guidelines. We included IoT models and applications across preanalytical, analytical, and postanalytical laboratory processes. PubMed, Cochrane Central, CINAHL Plus, Scopus, IEEE, and A.C.M. Digital library were searched between August 2015 to August 2022; the data were tabulated. Cohen's coefficient of agreement was calculated to quantify inter-reviewer agreements; a total of 18 studies were included with Cohen's coefficient computed to be 0.91. The included studies were divided into three classifications based on availability, including preanalytical, analytical, and postanalytical. The majority (77.8%) of the studies were real-tested. Communication-based approaches were the most common (83.3%), followed by application-based approaches (44.4%) and sensor-based approaches (33.3%) among the included studies. Open issues and challenges across the included studies included scalability, costs and energy consumption, interoperability, privacy and security, and performance issues. In this study, we identified, classified, and evaluated IoT applicability in clinical laboratory systems. This study presents pertinent findings for IoT development across clinical laboratory systems, for which it is essential that more rigorous and efficient testing and studies be conducted in the future.

Blood sorbitol measurement in diabetic rats treated with an aldose reductase inhibitor using an improved fiber-optic sorbitol biosensor

Sorbitol is known as a biomarker for the evaluation of the progress of diabetic complications. We have developed a sorbitol biosensor using an optical fiber for rapid diagnosis and pathological evaluation of diabetic complications. In this paper, we measured blood sorbitol in diabetic rats using an improved biosensor, and discussed the effectiveness of the developed biosensor and the significance of sorbitol measurement. In order to investigate the effectiveness of the developed biosensor, the blood sorbitol level of type II diabetic rats prepared by streptozotocin administration was measured with the developed sensor. The values of sorbitol were highly correlated with the values measured by the F-kit of food analysis and that we confirmed the sorbitol concentration could be quantified using the developed biosensor. Furthermore, the aldose reductase inhibitor "eparlrestat", which is a therapeutic drug that suppresses the accumulation of sorbitol, was administered to diabetic rats, and the blood sorbitol level was measured with the developed biosensor. As a result, the blood glucose level was high in both the treated group and the non-treated group, but the blood sorbitol level in the treated group decreased. The results suggest that the measurement of the sorbitol level with the developed biosensor in addition to the blood glucose level enables evaluation of complications like diabetic neuropathy. In the future, we expected that the developed sorbitol biosensor will be miniaturized, the pretreatment method for blood samples will be simplified, and it will be applied to the development of therapeutic agents for diabetic complications and personalized medicine.

Emerging Bioelectronic Strategies for Cardiovascular Tissue Engineering and Implantation

Heart diseases are currently the leading cause of death worldwide. The ability to create cardiovascular tissue has numerous applications in understanding tissue development, disease progression, pharmacological testing, bio-actuators, and transplantation; yet current cardiovascular tissue engineering (CTE) methods are limited. However, there have been emerging developments in the bioelectronics field, with the creation of biomimetic devices that can intimately interact with cardiac cells, provide monitoring capabilities, and regulate tissue formation. Combining bioelectronics with cardiac tissue engineering can overcome current limitations and produce physiologically relevant tissue that can be used in various areas of cardiovascular research and medicine. This review highlights the recent advances in cardiovascular-based bioelectronics. First, cardiac tissue engineering and the potential of bioelectronic therapies for cardiovascular diseases are discussed. Second, advantageous bioelectronic materials for CTE and implantation and their properties are reviewed. Third, several representative cardiovascular tissue-bioelectronic interface models and the beneficial functions that bioelectronics can demonstrate in in vitro and in vivo applications are explored. Finally, the prospects and remaining challenges for clinical application are discussed.

Futuristic CRISPR-based biosensing in the cloud and internet of things era: an overview

Biosensors-based devices are transforming medical diagnosis of diseases and monitoring of patient signals. The development of smart and automated molecular diagnostic tools equipped with biomedical big data analysis, cloud computing and medical artificial intelligence can be an ideal approach for the detection and monitoring of diseases, precise therapy, and storage of data over the cloud for supportive decisions. This review focused on the use of machine learning approaches for the development of futuristic CRISPR-biosensors based on microchips and the use of Internet of Things for wireless transmission of signals over the cloud for support decision making. The present review also discussed the discovery of CRISPR, its usage as a gene editing tool, and the CRISPR-based biosensors with high sensitivity of Attomolar (10^-18 M), Femtomolar (10^-15 M) and Picomolar (10^-12 M) in comparison to conventional biosensors with sensitivity of nanomolar 10^-9 M and micromolar 10^-3 M. Additionally, the review also outlines limitations and open research issues in the current state of CRISPR-based biosensing applications.

Functional Ionic Liquids Decorated Carbon Hybrid Nanomaterials for the Electrochemical Biosensors

Ionic liquids are gaining high attention due to their extremely unique physiochemical properties and are being utilized in numerous applications in the field of electrochemistry and bio-nanotechnology. The excellent ionic conductivity and the wide electrochemical window open a new avenue in the construction of electrochemical devices. On the other hand, carbon nanomaterials, such as graphene (GR), graphene oxide (GO), carbon dots (CDs), and carbon nanotubes (CNTs), are highly utilized in electrochemical applications. Since they have a large surface area, high conductivity, stability, and functionality, they are promising in biosensor applications. Nevertheless, the combination of ionic liquids (ILs) and carbon nanomaterials (CNMs) results in the functional ILs-CNMs hybrid nanocomposites with considerably improved surface chemistry and electrochemical properties. Moreover, the high functionality and biocompatibility of ILs favor the high loading of biomolecules on the electrode surface. They extremely enhance the sensitivity of the biosensor that reaches the ability of ultra-low detection limit. This review aims to provide the studies of the synthesis, properties, and bonding of functional ILs-CNMs. Further, their electrochemical sensors and biosensor applications for the detection of numerous analytes are also discussed.

A comprehensive review on the applications of nano-biosensor-based approaches for non-communicable and communicable disease detection

The outstretched applications of biosensors in diverse domains has become the reason for their attraction for scientific communities. Because they are analytical devices, they can detect both quantitative and qualitative biological components through the generation of detectable signals. In the recent past, biosensors witnessed significant changes and developments in their design as well as features. Nanotechnology has revolutionized sensing phenomena by increasing biodiagnostic capacity in terms of specificity, size, and cost, resulting in exceptional sensitivity and flexibility. The steep increase of non-communicable diseases across the world has emerged as a matter of concern. In parallel, the abrupt outbreak of communicable diseases poses a serious threat to mankind. For decreasing the morbidity and mortality associated with various communicable and non-communicable diseases, early detection and subsequent treatment are indispensable. Detection of different biological markers generates quantifiable signals that can be electrochemical, mass-based, optical, thermal, or piezoelectric. Speculating on the incumbent applicability and versatility of nano-biosensors in large disciplines, this review highlights different types of biosensors along with their components and detection mechanisms. Moreover, it deals with the current advancements made in biosensors and the applications of nano-biosensors in detection of various non-communicable and communicable diseases, as well as future prospects of nano-biosensors for diagnostics.

Applications of Artificial Intelligence (AI) for cardiology during COVID-19 pandemic

Background and aims

Artificial Intelligence (AI) shows extensive capabilities to impact different healthcare areas during the COVID-19 pandemic positively. This paper tries to assess the capabilities of AI in the field of cardiology during the COVID-19 pandemic. This technology is useful to provide advanced technology-based treatment in cardiology as it can help analyse and measure the functioning of the human heart.

Methods

We have studied a good number of research papers on Artificial Intelligence on cardiology during the COVID-19 pandemic to identify its significant benefits, applications, and future scope. AI uses artificial neuronal networks (ANN) to predict. In cardiology, it is used to predict the survival of a COVID-19 patient from heart failure.

Results

AI involves complex algorithms for predicting somewhat successful diagnosis and treatments. This technology uses different techniques, such as cognitive computing, deep learning, and machine learning. It is incorporated to make a decision and resolve complex challenges. It can focus on a large number of diseases, their causes, interactions, and prevention during the COVID-19 pandemic. This paper introduces AI-based care and studies its need in the field of cardiology. Finally, eleven major applications of AI in cardiology during the COVID-19 pandemic are identified and discussed.

Conclusions

Cardiovascular diseases are one of the major causes of death in human beings, and it is increasing for the last few years. Cardiology patients' treatment is expensive, so this technology is introduced to provide a new pathway and visualise cardiac anomalies. AI is used to identify novel drug therapies and improve the efficiency of a physician. It is precise to predict the outcome of the COVID-19 patient from cardiac-based algorithms. Artificial Intelligence is becoming a popular feature of various engineering and healthcare sectors, is thought for providing a sustainable treatment platform. During the COVID-19 pandemic, this technology digitally controls some processes of treatments.

Answering the Challenge of COVID-19 Pandemic Through Innovation and Ingenuity

The novel coronavirus disease 2019 (COVID-19) pandemic has created a maelstrom of challenges affecting virtually every aspect of global healthcare system. Critical hospital capacity issues, depleted ventilator and personal protective equipment stockpiles, severely strained supply chains, profound economic slowdown, and the tremendous human cost all culminated in what is questionably one of the most profound challenges that humanity faced in decades, if not centuries. Effective global response to the current pandemic will require innovation and ingenuity. This chapter discusses various creative approaches and ideas that arose in response to COVID-19, as well as some of the most impactful future trends that emerged as a result. Among the many topics discussed herein are telemedicine, blockchain technology, artificial intelligence, stereolithography, and distance learning.

Optimal dynamic empirical therapy in a health care facility: A Monte-Carlo look-ahead method

BACKGROUND AND OBJECTIVES

Empirical antimicrobial prescription strategies have been proposed to counteract the selection of resistant pathogenic strains. The respective merits of such strategies have been debated. Rather than comparing a finite number of policies, we take an optimization approach and propose a solution to the problem of finding an empirical therapy policy in a health care facility that minimizes the cumulative infected patient-days over a given time horizon.

METHODS

We assume that the parameters of the model are known and that when the policy is implemented, all patients receive the same treatment at a given time. We model the emergence and spread of antimicrobial resistance at the population level with the stochastic version of a compartmental model. The model features two drugs and the possibility of double resistance. Our solution method is a rollout algorithm.

RESULTS

In our example, the optimal policy computed with this method allows to reduce the average cumulative infected patient-days over two years by 22% compared to the best standard therapy. Considering regularity constraints, we could derive a policy with a fixed period and a performance close to that of the optimal policy. The average cumulative infected patient-days over two years obtained with the optimal policy is 6% lower (significantly at the 95% threshold) than that obtained with the fixed period policy.

CONCLUSION

Our results illustrate the performance of a highly flexible solution method that will contribute to the development of implementable empirical therapy policies.

On the Challenges for the Diagnosis of SARS-CoV-2 Based on a Review of Current Methodologies

Diagnosis of COVID-19 has been challenging owing to the need for mass testing and for combining distinct types of detection to cover the different stages of the infection. In this review, we have surveyed the most used methodologies for diagnosis of COVID-19, which can be basically categorized into genetic-material detection and immunoassays. Detection of genetic material with real-time polymerase chain reaction (RT-PCR) and similar techniques has been achieved with high accuracy, but these methods are expensive and require time-consuming protocols which are not widely available, especially in less developed countries. Immunoassays for detecting a few antibodies, on the other hand, have been used for rapid, less expensive tests, but their accuracy in diagnosing infected individuals has been limited. We have therefore discussed the strengths and limitations of all of these methodologies, particularly in light of the required combination of tests owing to the long incubation periods. We identified the bottlenecks that prevented mass testing in many countries, and proposed strategies for further action, which are mostly associated with materials science and chemistry. Of special relevance are the methodologies which can be integrated into point-of-care (POC) devices and the use of artificial intelligence that do not require products from a well-developed biotech industry.

Gold Nanoparticle-Based Platforms for Diagnosis and Treatment of Myocardial Infarction

In recent years, an increasing rate of mortality due to myocardial infarction (MI) has led to the development of nanobased platforms, especially gold nanoparticles (AuNPs), as promising nanomaterials for diagnosis and treatment of MI. These promising NPs have been used to develop different nanobiosensors, mainly optical sensors for early detection of biomarkers as well as biomimetic/bioinspired platforms for cardiac tissue engineering (CTE). Therefore, in this Review, we presented an overview on the potential application of AuNPs as optical (surface plasmon resonance, colorimetric, fluorescence, and chemiluminescence) nanobiosensors for early diagnosis and prognosis of MI. On the other hand, we discussed the potential application of AuNPs either alone or with other NPs/polymers as promising three-dimensional (3D) scaffolds to regulate the microenvironment and mimic the morphological and electrical features of cardiac cells for potential application in CTE. Furthermore, we presented the challenges and ongoing efforts associated with the application of AuNPs in the diagnosis and treatment of MI. In conclusion, this Review may provide outstanding information regarding the development of AuNP-based technology as a promising platform for current MI treatment approaches.

Biosensors for rapid detection of Salmonella in food: A review

Salmonella is one of the main causes of foodborne infectious diseases, posing a serious threat to public health. It can enter the food supply chain at various stages of production, processing, distribution, and marketing. High prevalence of Salmonella necessitates efficient and effective approaches for its identification, detection, and monitoring at an early stage. Because conventional methods based on plate counting and real-time polymerase chain reaction are time-consuming and laborious, novel rapid detection methods are urgently needed for in-field and on-line applications. Biosensors provide many advantages over conventional laboratory assays in terms of sensitivity, specificity, and accuracy, and show superiority in rapid response and potential portability. They are now recognized as promising alternative tools and one of the most on-site applicable and end user-accessible methods for rapid detection. In recent years, we have witnessed a flourishing of studies in the development of robust and elaborate biosensors for detection of Salmonella in food. This review aims to provide a comprehensive overview on Salmonella biosensors by highlighting different signal-transducing mechanisms (optical, electrochemical, piezoelectric, etc.) and critically analyzing its recent trends, particularly in combination with nanomaterials, microfluidics, portable instruments, and smartphones. Furthermore, current challenges are emphasized and future perspectives are discussed.

Development of a Diatom-Based Photoluminescent Immunosensor for the Early Detection of Karnal Bunt Disease of Wheat Crop

In India, the major crop is wheat. Its production is severely hampered by seed-borne diseases such as smut and bunt which are responsible for the reduction of crop yield with poor grain quality. In the current study, an attempt was made to prepare a photoluminescence (PL)-based immunosensor for early detection of Karnal bunt (KB) disease. The KB disease-causing pathogen Tilletia indica was detected using functionalized diatom frustules as a sensing platform. The teliospore-covered platform, on exposure to light, showed enhanced intensity of PL in comparison to control. This response was directly proportional to the concentration of spores. For the development of a stable frustule-based immunosensor platform, gluteraldehyde was added for the covalent immobilization of the T. indica antibody onto amine-functionalized diatom substrates. Frustules of diatom consisting of a nanoporous three-dimensional biogenic silica material exhibit a unique property of emitting strong, visible blue PL under ultraviolet (UV) excitation. PL studies were done to reveal the specificity and binding of the conjugated diatom platform that will distinguish between the T. indica (complementary) and A. niger (noncomplementary) antigens. Four times better intensity of PL was observed against the complementary one in comparison to a noncomplementary setup (control). The immunocomplex frustule-based platform serves as a suitable sensor platform for early detection of KB.

Assessment of diagnostic accuracy of SanketLife - A wireless, pocket-sized ECG biosensor, in comparison to standard 12 lead ECG in the detection of cardiovascular diseases in a tertiary care setting

Background

The SanketLife is a low cost, portable, pocket sized 12 lead ECG mechanised by SanketLife app running on compatible iOS and Android phones that connect wirelessly via Bluetooth technology to the device.

Objective

The current study was conducted to assess the diagnostic accuracy of SanketLife ECG in comparison to standard 12 lead ECG (GE-2000) in detection of cardiovascular diseases.

Research design and methods

This was a prospective diagnostic test accuracy trial conducted in outpatient settings of a tertiary cardiac care centre in India. A total of 100 patients, attended cardiology OPD, were included in the study. Consecutive ECGs were taken by 12 lead standard ECG as well as by SanketLife ECG. Diagnostic accuracy variables such as sensitivity, specificity, negative and positive predictive value, negative and positive likelihood ratios were estimated. Ethical permission was taken from the Institutional ethical committee.

Results & conclusion

The analysis showed a high degree of agreement and accuracy of SanketLife in detecting major cardiovascular conditions (Major Minnesota codes) such as Left and right bundle branch block, ST-segment elevation and ST-segment depression, AV conduction block. SanketLife showed high sensitivity (98.15%) and specificity (100%) in diagnosing major cardiovascular conditions.

From stars to stripes: RNA-directed shaping of plant viral protein templates-structural synthetic virology for smart biohybrid nanostructures

The self-assembly of viral building blocks bears exciting prospects for fabricating new types of bionanoparticles with multivalent protein shells. These enable a spatially controlled immobilization of functionalities at highest surface densities-an increasing demand worldwide for applications from vaccination to tissue engineering, biocatalysis, and sensing. Certain plant viruses hold particular promise because they are sustainably available, biodegradable, nonpathogenic for mammals, and amenable to in vitro self-organization of virus-like particles. This offers great opportunities for their redesign into novel "green" carrier systems by spatial and structural synthetic biology approaches, as worked out here for the robust nanotubular tobacco mosaic virus (TMV) as prime example. Natural TMV of 300 x 18 nm is built from more than 2,100 identical coat proteins (CPs) helically arranged around a 6,395 nucleotides ssRNA. In vitro, TMV-like particles (TLPs) may self-assemble also from modified CPs and RNAs if the latter contain an Origin of Assembly structure, which initiates a bidirectional encapsidation. By way of tailored RNA, the process can be reprogrammed to yield uncommon shapes such as branched nanoobjects. The nonsymmetric mechanism also proceeds on 3'-terminally immobilized RNA and can integrate distinct CP types in blends or serially. Other emerging plant virus-deduced systems include the usually isometric cowpea chlorotic mottle virus (CCMV) with further strikingly altered structures up to "cherrybombs" with protruding nucleic acids. Cartoon strips and pictorial descriptions of major RNA-based strategies induct the reader into a rare field of nanoconstruction that can give rise to utile soft-matter architectures for complex tasks. This article is categorized under: Biology-Inspired Nanomaterials > Protein and Virus-Based Structures Nanotechnology Approaches to Biology > Nanoscale Systems in Biology Biology-Inspired Nanomaterials > Nucleic Acid-Based Structures.

Artificial Intelligence and Personalized Medicine

The development of high-throughput, data-intensive biomedical research assays and technologies has created a need for researchers to develop strategies for analyzing, integrating, and interpreting the massive amounts of data they generate. Although a wide variety of statistical methods have been designed to accommodate 'big data,'  experiences with the use of artificial intelligence (AI) techniques suggest that they might be particularly appropriate. In addition,  the results of the application of these assays reveal a great heterogeneity in the pathophysiologic factors and processes that contribute to disease, suggesting that there is a need to tailor, or 'personalize,' medicines to the nuanced and often unique features possessed by individual patients. Given how important data-intensive assays are to revealing appropriate intervention targets and strategies for  treating an individual with a disease, AI can play an important role in the development of personalized medicines. We describe many areas where AI can play such a role and argue that AI's ability to advance personalized medicine will depend critically on not only the refinement of relevant assays, but also on ways of storing, aggregating, accessing, and ultimately integrating, the data they produce. We also point out the limitations of many AI techniques in developing personalized medicines as well as consider areas for further research.