ABSTRACT
The Covid-19 pandemic that hit us in 2020 changed our lifestyle in every way. There was tremendous damage to people's lives. It is now predicted that other variants of Coronavirus are affecting people's health throughout the world. We must remain vigilant against upcoming dangers. The Indian health ministry has also advised people to take the necessary precautions. In this paper, we will focus on automating temperature and oxygen monitoring using the Internet of Things. According to our proposed model, data generated by the temperature sensor (MLX90614) and oxygen saturation sensor (MAX30102) will be stored in a relational database. Using this data, future data analyses can be conducted. We are also going to visualize the data by building an interactive dashboard using Power BI. Overall, health monitoring will become much more convenient and speedier. © 2023 IEEE.
ABSTRACT
COVID-19 is highly infectious and has been extensively spread worldwide, with approximately 651 million definite cases crosswise the globe including Pakistan. At that era of pandemic where patients are not able to approach a doctor for even the routine checkups, in such curial situation even normal disease checkups are ignored by many families due to pandemic situations, those diseases may lead to be a perilous disease are results of it. Human disorders portray scenarios that even disturb or permanently cutoff the essential functions of a body parts. Consequently, the aim is to transform raw health data potential into actionable insights to applying the promising outcomes of Body Sensor Network (BSN) and State-of-Art Artificial Intelligence (AI) techniques to get proper medicine allocation to the particular health state of patient. In this paper the different techniques of Deep Learning and Machine Learning introduced to predict the actual medicine for the specific health state of patient according to data from the BSN. Experiments have been conducted on large dataset which shepherd it into 16 states of patient's health which will allotted to AI model to predict the medicine accordingly to the health state of patient. Experimental results show the 87.46% by Random Forest, 92.74% by K-Nearest Neighbors, 74.57% by Naive Bayes, 94.41% by Extreme Gradient Boost, 84.88% by Multi-Layer Perceptron in terms of precision of model training in event of classification. © 2023 IEEE.
ABSTRACT
While the exploration into biomolecules for diagnostic and prognostic devices continues to develop, many molecules continue to be examined for individual diseases or treatments. Consequently, it can be difficult to fully understand the scope of one individual molecule's current and potential clinical utilization. The scope of this study aimed to assess the potential of Interferon Gamma-induced Protein 10 (IP-10) as a biomarker in a wide variety of diseases, both as a main and supplemental indicator of disease infection and progression. IP-10 is a chemokine secreted in response to IFN-gamma playing a major role in the activation and regulation of inflammatory and immune responses within the body. Currently, IP-10 has displayed potential application in diseases such as COVID-19, tuberculosis, sepsis, Kawasaki disease, cancer, and many more. Molecular assays developed for the detection of IP-10 take longer testing time, sophisticated instrument utilization, and need more sample volumes. These cannot be utilized for bedside patient monitoring during the illness state of the patient. Biosensing tools are alternative methods used at clinical sites due to their rapid results. Though many types of sensing mechanisms established for the detection of disease biomarkers such as optical, piezoelectric sensors, and electrochemical biosensors are far beyond the other sensing methods due to their ease of mechanism, rapid results, and portable nature. IP-10 has been a promising biomarker in different diseases, evaluation of IP-10 levels at different time points of treatments is necessary. To achieve this, current conventional methods cannot be used and thus a portable device that provides rapid results is in demand. Such point-of-care (POC) device development for IP-10 analysis is very crucial in the current scenario. Beyond this, the clarification of its physiological role in healthy and infected individuals could allow for more proper utilization in clinical diagnoses, prognoses, treatment monitoring, and more. Overall, this study was developed to summarize the associations currently created between levels of IP-10 and other biomolecules and diseases.Copyright © 2023 The Author(s)
ABSTRACT
The dreaded coronavirus (COVID-19) disease traceable to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV2) has killed thousands of people worldwide, and the World Health Organization (WHO) has proclaimed the viral respiratory disease a human pandemic. The adverse flare of COVID-19 and its variants has triggered collaborative research interests across all disciplines, especially in medicine and healthcare delivery. Complex healthcare data collected from patients via sensors and devices are transmitted to the cloud for analysis and sharing. However, it is pretty difficult to achieve rapid and intelligent decisions on the processed information due to the heterogeneity and complexity of the data. Artificial intelligence (AI) has recently appeared as a promising paradigm to address this issue. The introduction of AI to the Internet of Medical Things (IoMT) births the era of AI of Medical Things (AIoMT). The AIoMT enables the autonomous operation of sensors and devices to provide a favourable and secure environmental landscape to healthcare personnel and patients. AIoMT finds successful applications in natural language processing (NLP), speech recognition, and computer vision. In the current emergency, medical-related records comprising blood pressure, heart rate, oxygen level, temperature, and more are collected to examine the medical conditions of patients. However, the power usage of the low-power sensor nodes employed for data transmission to the remote data centres poses significant limitations. Currently, sensitive medical information is transmitted over open wireless channels, which are highly susceptible to malicious attacks, posing a significant security risk. An insightful privacy-aware energy-efficient architecture using AIoMT for COVID-19 pandemic data handling is presented in this chapter. The goal is to secure sensitive medical records of patients and other stakeholders in the healthcare domain. Additionally, this chapter presents an elaborate discussion on improving energy efficiency and minimizing the communication cost to improve healthcare information security. Finally, the chapter highlights the open research issues and possible lines of future research in AIoMT.
ABSTRACT
Previously, there were physically operated hand dispensers that needed to be contacted every time we needed to apply the sanitizer. Due to the outbreak of the COVID-19, the importance of alcohol-based sanitizers has come up. As there are chances of spreading the viruses through contact surfaces, foot-operated dispensers have been developed to avoid contact between hands and hand dispensers. For the virus-infected people, their body temperatures may be high. Therefore, many organizations have been using infrared guns to record their body temperatures before they start working. To record their temperature, a manual presence is required. To replace these manual operations, an automated hand dispenser can be used. An IoT-based automated machine is designed to sanitize the hands with alcohol-based liquid without any contact with the prefilled sanitizer bottle. In connection to that, a temperature sensor is also employed within the dispenser machine to indicate the temperature of the person before they want to sanitize their hands. During the COVID-19 pandemic, most organizations were allowing their clients to work only after checking their body temperature and sanitizing themselves physically. Therefore, to avoid physical contact between the dispenser and people, a device that provides contactless operation has been developed, and further, it does not require any manual presence. While this device uses an ultrasonic sensor along with a temperature sensor to identify the existence of a hand and note the temperature of that hand, a microcontroller is also employed to control the operations. Subsequently, an LCD display is used to indicate the recorded temperature and to pump the alcohol-based sanitizer. A submersible DC motor pump is used, which is operated using a relay from the microcontroller (Granted Patent Ref.: AU 2020102940). © 2023 Elsevier Ltd. All rights reserved.
ABSTRACT
Most electronics such as sensors, actuators and energy harvesters need piezoceramic films to interconvert mechanical and electrical energy. Transferring the ceramic films from their growth substrates for assembling electronic devices commonly requires chemical or physical etching, which comes at the sacrifice of the substrate materials, film cracks, and environmental contamination. Here, we introduce a van der Waals stripping method to fabricate large-area and freestanding piezoceramic thin films in a simple, green, and cost-effective manner. The introduction of the quasi van der Waals epitaxial platinum layer enables the capillary force of water to drive the separation process of the film and substrate interface. The fabricated lead-free film, [Formula: see text] (BCZT), shows a high piezoelectric coefficient d33 = 209 ± 10 pm V-1 and outstanding flexibility of maximum strain 2%. The freestanding feature enables a wide application scenario, including micro energy harvesting, and covid-19 spike protein detection. We further conduct a life cycle analysis and quantify the low energy consumption and low pollution of the water-based stripping film method.
ABSTRACT
In the midst of the COVID-19 pandemic, adaptive solutions are needed to allow us to make fast decisions and take effective sanitation measures, e.g., the fast screening of large groups (employees, passengers, pupils, etc.). Although being reliable, most of the existing SARS-CoV-2 detection methods cannot be integrated into garments to be used on demand. Here, we report an organic field-effect transistor (OFET)-based biosensing device detecting of both SARS-CoV-2 antigens and anti-SARS-CoV-2 antibodies in less than 20 min. The biosensor was produced by functionalizing an intrinsically stretchable and semiconducting triblock copolymer (TBC) film either with the anti-S1 protein antibodies (S1 Abs) or receptor-binding domain (RBD) of the S1 protein, targeting CoV-2-specific RBDs and anti-S1 Abs, respectively. The obtained sensing platform is easy to realize due to the straightforward fabrication of the TBC film and the utilization of the reliable physical adsorption technique for the molecular immobilization. The device demonstrates a high sensitivity of about 19%/dec and a limit of detection (LOD) of 0.36 fg/mL for anti-SARS-Cov-2 antibodies and, at the same time, a sensitivity of 32%/dec and a LOD of 76.61 pg/mL for the virus antigen detection. The TBC used as active layer is soft, has a low modulus of 24 MPa, and can be stretched up to 90% with no crack formation of the film. The TBC is compatible with roll-to-roll printing, potentially enabling the fabrication of low-cost wearable or on-skin diagnostic platforms aiming at point-of-care concepts.
ABSTRACT
Rapid and reliable techniques for virus identification are required in light of recurring epidemics and pandemics throughout the world. Several techniques have been distributed for testing the flow of patients. Polymerase chain reaction with reverse transcription is a reliable and sensitive, though not rapid, tool. The antibody-based strip is a rapid, though not reliable, and sensitive tool. A set of alternative tools is being developed to meet all the needs of the customer. Surface-enhanced Raman spectroscopy (SERS) provides the possibility of single molecule detection taking several minutes. Here, a multiplex lithographic SERS aptasensor was developed aiming at the detection of several respiratory viruses in one pot within 17 min. The four labeled aptamers were anchored onto the metal surface of four SERS zones; the caught viruses affect the SERS signals of the labels, providing changes in the analytical signals. The sensor was able to decode mixes of SARS-CoV-2 (severe acute respiratory syndrome coronavirus two), influenza A virus, respiratory syncytial virus, and adenovirus within a single experiment through a one-stage recognition process.
Subject(s)
Biosensing Techniques , COVID-19 , Humans , SARS-CoV-2 , Spectrum Analysis, Raman/methods , Oligonucleotides/chemistry , Respiratory Syncytial Viruses , Biosensing Techniques/methodsABSTRACT
This study describes the use of copper nanoparticles (CuNPs) and reduced graphene oxide (rGO) as an electrode modifier for the determination of chloroquine phosphate (CQP). The synthetized rGO-CuNPs composite was morphologically characterized using scanning electron microscopy and electrochemically characterized using cyclic voltammetry. The parameters were optimized and the developed electrochemical sensor was applied in the determination of CQP using square-wave voltammetry (SWV). The analytical range for the determination of CQP was 0.5 to 110 µmol L-1 (one of the highest linear ranges for CQP considering electrochemical sensors), with limits of detection and quantification of 0.23 and 0.78 µmol L-1, respectively. Finally, the glassy carbon (GC) electrode modified with rGO-CuNPs was used for quantification of CQP in tap water; a study was carried out with interferents using SWV and obtained great results. The use of rGO-CuNP material as an electrode modifier was thus shown to be a good alternative for the development of low-cost devices for CQP analysis.