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COVID-19 epidemic has changed many people's life. There has been an increase in cybercrime and cyber-attacks on infrastructure systems throughout the world. To reduce the impact of social alienation, a significant rise has been observed in the utilization and dependence on computers, handheld devices, and web to perform day-to-day activities like communication, work, online transactions for shopping, and medical diagnostics throughout the pandemic. Criminals were able to take advantage of new weaknesses generated because of movement of the work place to home for their own individual advantage. In a postpandemic world, ab roader and diverse cyber security approach is required to assure the well-being and continuation of crucial systems on which our mankind depends. This research work shows the preliminary design of the proposed solution, which is built based on the concept of Artificial Intelligence (AI) enabled self-replication system. © 2023 IEEE.
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The development of information technology in the modern world affects the public health sector on the one hand and accumulates enormous amounts of data on the other hand. The global COVID-19 pandemic has contributed to the digitalization of healthcare. Heart disease is a global problem that causes death worldwide. Therefore, this study proposes a model for determining the information content of signs of diagnostic data of heart diseases based on the cumulative frequency method. The software implementation of the model has been completed. A database of 303 patients, consisting of 14 attributes, was used for the experiments. As a result of the model's work, the features with the most significant information content were identified. The study is promising and can apply diagnostic models in public health practice. ©2022 Copyright for this paper by its authors.
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The present chapter is focused on the latest available technique and technology helpful in monitoring a large number of people having after corona disease effect. The most favorable way of monitoring a large number of people together can be possible only through the online wireless monitoring system. Artificial intelligence (AI) and machine learning (ML) technique-based systems can only handle this kind of post COVID scenario, as it is quick, accurate and many a time automatic. Thus present book chapter is focused on the review of the present latest AI/ML-based health monitoring systems. Separate sub-topics on cardiac, nephrology, and diabetics have been taken elaborately. The health monitoring system shall be capable of monitoring diseases such as cardiac, nephrology, and diabetes. Internet of things (IoT) wearable devices (medical sensors) are useful for recording various body parameters of the patient like comprehensive pressure, fever, physics activity, heart rate, etc. A real-time IoT-based system is capable to deliver the data to caregiving medical centers, doctors, or family members for proper treatment. IoT-based patient monitoring has a few drawbacks related to the error in analysis and acceptability among the medical fraternity. Other issues include security and privacy. Devices capture private health-related information and these data are highly vulnerable as being in the public domain through the internet. Thus it may attract unethical people for misuse. © 2023 Elsevier Inc. All rights reserved.
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Healthcare Informatics is a phenomenon being talked about from the early 21st century in the era in which we are living. With evolution of new computing technologies huge amount of data in healthcare is produced opening several research areas. Managing the massiveness of this data is required while extracting knowledge for decision making is the main concern of today. For this task researchers are doing explorations in big data analytics, deep learning (advanced form of machine learning known as deep neural nets), predictive analytics and various other algorithms to bring innovation in healthcare. Through all these innovations happening it is not wrong to establish that disease prediction with anticipation of its cure is no longer unrealistic. First, Dengue Fever (DF) and then Covid-19 likewise are new outbreak in infectious lethal diseases and diagnosing at all stages is crucial to decrease mortality rate. In case of Diabetes, clinicians and experts are finding challenging the timely diagnosis and analyzing the chances of developing underlying diseases. In this paper, Louvain Mani-Hierarchical Fold Learning healthcare analytics, a hybrid deep learning technique is proposed for medical diagnostics and is tested and validated using real-time dataset of 104 instances of patients with dengue fever made available by Holy Family Hospital, Pakistan and 810 instances found for infectious diseases including prognosis of; Covid-19, SARS, ARDS, Pneumocystis, Streptococcus, Chlamydophila, Klebsiella, Legionella, Lipoid, etc. on GitHub. Louvain Mani-Hierarchical Fold Learning healthcare analytics showed maximum 0.952 correlations between two clusters with Spearman when applied on 240 instances extracted from comorbidities diagnostic data model derived from 15696 endocrine records of multiple visits of 100 patients identified by a unique ID. Accuracy for induced rules is evaluated by Laplace (Fig. 8) as 0.727, 0.701 and 0.203 for 41, 18 and 24 rules, respectively. Endocrine diagnostic data is made available by Shifa International Hospital, Islamabad, Pakistan. Our results show that in future this algorithm may be tested for diagnostics on healthcare big data.
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Drying of biologically-relevant sessile droplets, including passive systems such as DNA, proteins, plasma, and blood, as well as active microbial systems comprising bacterial and algal dispersions, has garnered considerable attention over the last decades. Distinct morphological patterns emerge when bio-colloids undergo evaporative drying, with significant potential in a wide range of biomedical applications, spanning bio-sensing, medical diagnostics, drug delivery, and antimicrobial resistance. Consequently, the prospects of novel and thrifty bio-medical toolkits based on drying bio-colloids have driven tremendous progress in the science of morphological patterns and advanced quantitative image-based analysis. This review presents a comprehensive overview of bio-colloidal droplets drying on solid substrates, focusing on the experimental progress during the last ten years. We provide a summary of the physical and material properties of relevant bio-colloids and link their native composition (constituent particles, solvent, and concentrations) to the patterns emerging due to drying. We specifically examined the drying patterns generated by passive bio-colloids (e.g., DNA, globular, fibrous, composite proteins, plasma, serum, blood, urine, tears, and saliva). This article highlights how the emerging morphological patterns are influenced by the nature of the biological entities and the solvent, micro- and global environmental conditions (temperature and relative humidity), and substrate attributes like wettability. Crucially, correlations between emergent patterns and the initial droplet compositions enable the detection of potential clinical abnormalities when compared with the patterns of drying droplets of healthy control samples, offering a blueprint for the diagnosis of the type and stage of a specific disease (or disorder). Recent experimental investigations of pattern formation in the bio-mimetic and salivary drying droplets in the context of COVID-19 are also presented. We further summarized the role of biologically active agents in the drying process, including bacteria, algae, spermatozoa, and nematodes, and discussed the coupling between self-propulsion and hydrodynamics during the drying process. We wrap up the review by highlighting the role of cross-scale in situ experimental techniques for quantifying sub-micron to micro-scale features and the critical role of cross-disciplinary approaches (e.g., experimental and image processing techniques with machine learning algorithms) to quantify and predict the drying-induced features. We conclude the review with a perspective on the next generation of research and applications based on drying droplets, ultimately enabling innovative solutions and quantitative tools to investigate this exciting interface of physics, biology, data sciences, and machine learning.
Subject(s)
COVID-19 , Male , Humans , COVID-19/diagnosis , Colloids/chemistry , Drug Delivery Systems , Solvents , Blood ProteinsABSTRACT
Amount of X-ray and Computed Tomography (CT) lung examinations drastically increased due to COVID-19 pandemic. Unfortunately, the both of this type of medical studies have their own disadvantage. X-ray has low sensitivity approximately 0,4 for pneumonia diagnosis. CT has high radiation dose about 3-4 mSv, that in 3-4 times more than value recommends as maximum for annual radiation dose. In the long run it can be cause of increase of oncological diseases. Digital X-ray tomosynthesis is relatively new method of medical diagnostic stands between X-ray and CT and has benefits of both. However, there are no evident that quality of images of digital X-ray tomosynthesis met the requirements for tomographic image quality. To solve this problem we checked the quality of thirty four tomosynthesis images series for the requirements for tomography images. © 2022 IEEE.
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In Ukraine, COVID-19 has contributed over 104,106 deaths. Multiple risk factors for COVID-19 almost have been identified. The new PRINCIPLE methodology for selecting indicators of patient screening using medical equipment is developed. The name of this methodology is an acronym of the criteria for selecting indicators: 'Provability', 'Reproducibility', 'Informativeness', 'Numerical', 'Clinical', 'Importance', 'Prevalence', 'Lungs', 'Electrocardiography'. Tools based on the Bayesian network to predict the high risk of patient mortality based on these indicators are developed. An example of application of the proposed methodology, construction of the model, and formation of conclusions by them are given for anonymized data consisting of 22 features from adults 280 alive and 140 dead patients, diagnosed with COVID-19 at the hospital in Vinnytsia. The work offers an improved method for processing and analyzing the biomedical indicators and medical diagnostic data for the clinical decision-making tool for COVID-19 inpatients construction. © 2022 IEEE.
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Over the past few years, most medical diagnostics and treatments have shifted to digital content. COVID-19 is a viral disease first identified in Wuhan, China 2019. Its pandemic caused a dramatic loss in human health, work, and food systems worldwide. WHO recommended social distancing as a preventive measure to protect ourselves from corona viral infections. Hence, now many avail hospitals facilities are online. It enables telemedicine where patients, doctors, and medical research units can easily share their digital medical information through various communication channels. At the receiver's end, the patient's record must not be lost or altered during transmission. As medical imaging contains many fine features, even small changes cause confusion among medical staff for diagnosis. One of the best techniques for image authentication is digital image watermarking. When developing an effective watermark method, it's necessary to have a balanced trade-off among imperceptibility, capacity, and robustness. The work gives a comprehensive survey of cryptography, biometrics, and blockchain-based on various watermarking schemes in medical images that gives new ideas to improve the already existing techniques. © 2022 IEEE.
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Paper microfluidics has had a rich history in medical diagnostics owing to their portability, low-cost and capacity for mass manufacture. While nitrocellulose has widespread use in commercial paper-based assays, shortages can become a bottleneck for deployment. Here, we seek to overcome this limitation by enabling swift and efficient production of cellulose-based paper assays with minimal substrate processing via protein engineering. We demonstrate good clinical and lab-based performance for both serological and antigen rapid tests and their compatibility with roll-to-roll mass manufacturing, which validates our proposed workflow for commercialization. © 2021 MicroTAS 2021 - 25th International Conference on Miniaturized Systems for Chemistry and Life Sciences. All rights reserved.
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Interferometric scattering microscopy is a newly emerging alternative to fluorescence microscopy in biomedical research and diagnostic testing due to its ability to detect nano-objects such as individual proteins, extracellular vesicles, and virions individually through their intrinsic elastic light scattering. To improve the signal-to-noise ratio, we developed photonic resonator interferometric scattering microscopy (PRISM) in which a photonic crystal (PC) resonator is used as the sample substrate. The scattered light is amplified by the PC through resonant near-field enhancement, which then interferes with the <1% transmitted light to create intensity contrast. Importantly, the scattered photons assume the wavevectors defined by PC's photonic band structure, resulting in the ability to utilize a non-immersion objective without significant loss at illumination density as low as 25 W/cm2. We demonstrate virus and protein detection, including highly selective capture and counting of intact pseudotype SARS-CoV-2 from saliva with sensitivity equivalent to conventional nucleic acid tests. The results showcase the promise of nanophotonic surfaces in the development of resonance-enhanced interferometric microscopies, and as a single step, room temperature, and rapid viral detection technology. © 2022 SPIE.
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During this pandemic situation most of the people's health are in the need of medicine and doctors suggestions to improve and protect their health. Also, have seen many such cases where many people have been infected by COVID. To reduce the physical contact and help the people from the spread of diseases the proposed methodology is to implement the medibot in hospitals. A medical bot is a Chatbot which uses NLP (Natural Language Processing) by text-format. The medibot is supported by AI and Deep Learning for Medical Diagnostics. The goal of the project is to create a medibot that overcomes the proposed methodology. There were many people who could not meet the doctors for simple problems such as cold and fever. To reduce these cases will implement the medibot. This medibot can communicate with the patients and understand the symptoms, it will also give them medicines. © 2022 IEEE.
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We present an open source, fully wireless potentiostat (the “NanoStat”) for applications in electrochemistry, sensing, biomedical diagnostics, and nanotechnology, based on only 2 integrated circuit chips: A digital microcontroller with integrated on board WiFi and file/web server hardware/software, and an analog front end. This versatile platform is fully capable of all modern electrochemisty assays, including cyclic voltammetry, square wave voltammetry, chronoamperometry, and normal pulse voltammetry. The user interface is a web browser connected over http. All the code (firmware, HTML5, JavaScript) is hosted by the NanoStat itself without the need for any additional software. The total size is 4×40×20 mm and battery operation for 6 h is demonstrated, possible to extend to weeks or months in sleep mode. We anticipate that the applications of this could be very broad, from biomedical sensing in the clinic, to remote monitoring of unattended “motes”, to even possibly sensing aerial pathogens such as COVID in large public spaces without the need for anything other than a web browser for remote monitoring from anywhere in the world. Finally, we propose to use this software suite as a basis (kernel) of a fully open source, general purpose, web based electrochemistry software suite, ed from the hardware, which we call “OpenEChem”. © 2022
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The importance of medical diagnostics, and specifically, tests for infectious agents such as viruses, is well recognized, and in fact, are a crucial component of efforts to control pandemics and mitigate their effects on public health. A long-established trend is the development of low-cost, easy-to-use point-of-care (POC) diagnostics to provide pervasive, timely testing independent of laboratories and other medical infrastructure. The technology of POC tests is widely accessible to engineering students, and there are many opportunities and avenues for innovation, including Smartphone-based platforms and integration with the Internet of Medical Things (IoMT). We discuss the design, demonstration, and testing of POC virus testing, including tests applicable to COVID-19, as Senior Design Projects for undergraduate mechanical, electrical, and manufacturing engineering majors. Copyright © 2021 by ASME
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The value of digital healthcare has been lauded in Canada at local, provincial, and national levels. Digital medicine is purported to enhance patient access to care while promising cost savings. Using institutional ethnography, we examined the potential for publicly funded digital testing for HIV and other sexually transmitted infections (STI) in Ontario, Canada. Our analyses draw from 23 stakeholder interviews with healthcare professionals conducted between 2019 and 2020, and textual analyses of government documents and private, for-profit digital healthcare websites. We uncovered a "two-tiered" system whereby private digital STI testing services enable people with economic resources to "pay to skip the line" queuing at public clinics and proceed directly to provide samples for diagnostics at local private medical labs. In Ontario, private lab corporations compete for fee-for-service contracts with government, which in turn organises opportunities for market growth when more patient samples are collected vis-à-vis digital testing. However, we also found that some infectious disease specimens (e.g., HIV) are re-routed for analysis at government public health laboratories, who may be unable to manage the increase in testing volume associated with digital STI testing due to state budget constraints. Our findings on public-private laboratory funding disparities thus discredit the claims that digital healthcare necessarily generates cost savings, or that it enhances patients' access to care. We conclude that divergent state funding relations together with the creeping privatisation of healthcare within this "universal" system coordinate the conditions through which private corporations capitalise from digital STI testing, compounding patient access inequities. We also stress that our findings bring forth large scale implications given the context of the global COVID-19 pandemic, the rapid diffusion of digital healthcare, together with significant novel coronavirus testing activities initiated by private industry.
Subject(s)
Digital Technology , HIV Infections/diagnosis , HIV Testing/economics , Mass Screening/economics , Politics , Sexually Transmitted Diseases/diagnosis , HIV Testing/methods , Humans , Mass Screening/methods , OntarioABSTRACT
Recent advances in electrochemical biosensors for pathogen detection are reviewed. Electrochemical biosensors for pathogen detection are broadly reviewed in terms of transduction elements, biorecognition elements, electrochemical techniques, and biosensor performance. Transduction elements are discussed in terms of electrode material and form factor. Biorecognition elements for pathogen detection, including antibodies, aptamers, and imprinted polymers, are discussed in terms of availability, production, and immobilization approach. Emerging areas of electrochemical biosensor design are reviewed, including electrode modification and transducer integration. Measurement formats for pathogen detection are classified in terms of sample preparation and secondary binding steps. Applications of electrochemical biosensors for the detection of pathogens in food and water safety, medical diagnostics, environmental monitoring, and bio-threat applications are highlighted. Future directions and challenges of electrochemical biosensors for pathogen detection are discussed, including wearable and conformal biosensors, detection of plant pathogens, multiplexed detection, reusable biosensors for process monitoring applications, and low-cost, disposable biosensors.