Nano-biosensors for Diagnosing Infectious and Lifestyle-Related Disease of Human: An Update

At present time, a variety of infectious and lifestyle diseases are becoming lifethreatening day by day. Development in technology and immergence of nanoscience helped to provide a better health care system. Based on the working mechanism nano-biosensors are of majorly two types: electrochemical nanobiosensor and optical nano-biosensor. Nanomaterials used in the nano-biosensor increased their efficacy, sensitivity, and selectivity of the device. Different diseases have different biomarkers to get detected such as, absorption of cholesterol oxidase detect cholesterol, glaucoma in a diabetes patient is detected by cytokine Interleukin 12 in tear, C-reactive protein is detected for liver inflammation, the SARS virus is detected by N-protein and miRNA is a potential biomarker of cancers, especially colorectal cancer. Hitherto, identification of a biomarker for a specific disease is the major work. The accuracy of nanobiosensor in diagnosing diseases put them in demand in the biomedical field. But the major drawback comes with the cost-effectiveness and use of nanomaterial in health sectors focussing on any toxicological impact of the nano-biosensor on health in long run. In this chapter, we present an overview of the working mechanism of different nano-biosensors in diagnosing different infectious and lifestyle diseases. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

RAPID IDENTIFICATION OF ETIOLOGICAL AGENTS OF RESPIRATORY TRACT INFECTIONS DURING THE COVID-19 PANDEMIC USING THE MULTIPLEX-PCR METHOD.

Objective. Most respiratory infections have similar symptoms, so it is clinically difficult to determine their etiology. This study aimed to show the importance of molecular diagnostics in identifying the etiological agent of respiratory infections, especially during the coronavirus disease 2019 (COVID-19) pandemic. Methods. A total of 849 samples from patients hospitalized at the University Clinical Center Kragujevac (from January 1 to August 1, 2022) were examined using automated multiplex-polymerase chain reaction (PCR) tests. The BioFire-FilmArray-Respiratory Panel 2.1 test was used for 742 nasopharyngeal swabs [identification of 19 viruses (including SARS-CoV-2) and four bacteria], while the BioFire-FilmArray-Pneumonia Panel was used [identification of 18 bacteria and nine viruses] (BioMerieux, Marcy l'Etoile, France) for 107 tracheal aspirates. The tests were performed according to the manufacturer's instructions, and the results were available within an hour. Results. In 582 (78.4%) samples, the BioFire-FilmArray-Respiratory Panel 2.1 plus test identified at least one pathogen. The rhinovirus (20.6%), SARS-CoV-2 (17.7%), influenza A (17.5%), respiratory syncytial virus (12.4%), and parainfluenza 3 (10.1%) were the most common. Other viruses were found less frequently, and Bordetella parapertussis was detected in one sample. In 85 (79.4%) samples, the BioFire-FilmArray-Pneumonia Panel test identified at least one bacterium or virus. The most prevalent bacteria were Staphylococcus aureus (42.4%), Haemophilus influenzae (41.2%), Streptococcus pneumoniae (36.5%), Moraxella catarrhalis (22.3%), and Legionella pneumophila (2.4%). Among viruses, rhinovirus (36.5%), adenovirus (23.5%), influenza A (11.8%), and the genus Coronavirus (4.7%), were detected. Conclusion. Multiplex-PCR tests improved the implementation of therapeutic and epidemiological measures, preventing the spread of the COVID-19 infection and Legionnaires' disease.Copyright © 2022, Serbian Medical Society. All rights reserved.

Modeling and Analysis of Infectious Disease Transmission Considering Protection and Isolation

Based on the SEIR model, two compartments for self-protection and isolation are introduced, and a more general infectious disease transmission model is proposed.Through qualitative analysis of the model, the basic reproduction number of the model is calculated, and the local asymptotic stability of the disease-free equilibrium point and the endemic equilibrium point of the model is analyzed through eigenvalue theory and Routh-Hurwitz criterion.The numerical simulation and fitting results of COVID-19 virus show that the proposed SEIQRP model can effectively describe the dynamic transmission process of the infectious disease.In the model, the three parameters, i.e.protection rate, incubation period isolation rate, and infected person isolation rate play a very critical role in the spread of the disease.Raising people's awareness of self-protection, focusing on screening for patients in the incubation period, and isolating and treating infected people can effectively reduce the spread of infectious diseases. © 2023 Northeastern University.All rights reserved.

Scaling law of COVID-19 cases and city size and its evolution over time

Urban scaling law quantifies the disproportional growth of urban indicators with urban population size, which is one of the simple rules behind the complex urban system. Infectious diseases are closely related to social interactions that intensify in large cities, resulting in a faster speed of transmission in large cities. However, how this scaling relationship varies in an evolving pandemic is rarely investigated and remains unclear. Here, taking the COVID- 19 epidemic in the United States as an example, we collected daily added cases and deaths from January 2020 to June 2022 in more than three thousand counties to explore the scaling law of COVID- 19 cases and city size and its evolution over time. Results show that COVID- 19 cases super- linearly scaled with population size, which means cases increased faster than population size from a small city to a large city, resulting in a higher morbidity rate of COVID- 19 in large cities. Temporally, the scaling exponent that reflects the scaling relationship stabilized at around 1.25 after a fast increase from less than one. The scaling exponent gradually decreased until it was close to one. In comparison, deaths caused by the epidemic did not show a super-linear scaling relationship with population size, which revealed that the fatality rate of COVID-19 in large cities was not higher than that in small or medium-sized cities. The scaling exponent of COVID- 19 deaths shared a similar trend with that of COVID- 19 cases but with a lag in time. We further estimated scaling exponents in each wave of the epidemic, respectively, which experienced the common evolution process of first rising, then stabilizing, and then decreasing. We also analyzed the evolution of scaling exponents over time from regional and provincial perspectives. The northeast, where New York State is located, had the highest scaling exponent, and the scaling exponent of COVID- 19 deaths was higher than that of COVID-19 cases, which indicates that large cities in this region were more prominently affected by the epidemic. This study reveals the size effect of infectious diseases based on the urban scaling law, and the evolution process of scaling exponents over time also promotes the understanding of the urban scaling law. The mechanism behind temporal variations of scaling exponents is worthy of further exploration. © 2023 Science Press. All rights reserved.

Study on Infection Probabilities of COVID-19 by SIR Network

Severe Acute Respiratory Syndrome Coronavirus 2 Related Diseases (COVID-19) is now one of the most challenging and concerning epidemics, which has been affecting the world so much. After that, countries around the world have been actively developing vaccines to deal with the sudden disease. How to carry out more efficient epidemic prevention has also become a problem of our concern. Unlike traditional SIR disease transmission models, network percolation has unique advantages in disease immune modelling, which makes it closer to reality in the simulation. This article introduces the study of SIR percolation network on infection probabilities of COVID-19, and proposes a method to preventing the spread of disease. © 2022 IEEE.

Infections and the Kidney

Global infections are very frequent cause of AKI. Often this is due to the non-specific systemic effects of sepsis and volume depletion and therefore can occur with many infectious agents perhaps most searingly brought to our attention with the SARS-CoV-2 pandemic. The kidney can also be damaged by infections directly involving the renal parenchyma, because of persistent infection elsewhere in the body, as a post-infectious response and secondary diseases causing obstruction. Identifying, first, that kidney injury is due to infection and the particular infection causing the patient's presentation is critical to management. Some infections discussed in this chapter are confined to specific areas of the world, but with increasing global travel and migration, patients may present to healthcare facilities anywhere;thus, a thorough travel history is invaluable. © Springer Nature Switzerland AG 2014, 2022.

COVID-19 Model Based on Conformable Fractional Derivativeand Its Numerical Solution

After the outbreak of COVID-19, it is of great significance to find an appropriate dynamic model of COVID-19 epidemic in order to master its transmission law, predict its development trend, and provide corresponding prevention and control basis. In this paper, the SEIRV chamber model is adopted, and the dynamics model of infectious disease is established by combining the fractional derivative of Conformable. The fractional derivative differential equation of Conformable is discretized by numerical method and its numerical solution is obtained. In addition, numerical simulation was carried out on the confirmed data of Wuhan city from January 23, 2020 to February 11, 2020. At the same time, consider that the Wuhan municipal government revised the epidemic data on February 12, 2020, adding nearly 14,000 people. The order α value of SEIRV model is modified, and then the revised data is simulated. The simulation results are in good agreement with the published data. The results show that compared with the traditional integer order model, the fractional order model can simulate the modified data. This reflects the advantages of fractional infectious disease dynamics model, and can provide certain reference value for the prediction of COVID-19 model. © 2022 Editorial Borad of Complex Systems and Complexity Science. All rights reserved.

Optimal selection of cruise ship waste disposal ports

As cruise ships call at many ports and passengers come from all over the world, it is very easy to carry viruses on cruise ships. Under the control of the epidemic situation on board, the solid waste generated by them should be scientifically treated to prevent the spread of infectious diseases such as COVID-19 pneumonia. Therefore, Reasonable selection of waste disposal ports and formulation of unloading plans are directly related to the resumption of cruise operations. This study considers the cost and risk of waste disposal, uses robust optimization to deal with waste volume, increases the scenarios of port service interruption due to epidemics and other reasons, and proposes a variety of emergency strategies. Finally, the relevant strategies are selected according to the decision-maker's preference for cost and risk;By solving the relevant examples, the optimal choice of the cruise ship waste disposal port under the epidemic situation is given, which verifies the validity and feasibility of the model. The research helps to improve the management of cruise waste during the post-epidemic period, and has practical value and guiding significance for the normal operation and development of the global cruise market. © 2023 SPIE.

Commentary: Educational Policies in Pandemic Times. A View from Latin America

Most countries in the world closed their educational centers and maintained classes online to prevent the spread of the virus SARSV-Cov-2. Latin America is not an exception. Estimates of the transmission dynamics of the pandemic indicate the application of actions that will affect educational contexts for years. This piece reflects on necessary changes in educational policies to take account of the current setting of COVID-19. We focus this reflection from a Latino American perspective, but it is not exclusive. The discussion can be useful to other countries with similar characteristics. [ FROM AUTHOR] Copyright of Journal of Latinos & Education is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Food Insecurity and COVID-19 Diagnosis: Findings from a National United States Sample

This study explores the association between experiencing food insecurity and COVID-19 diagnosis in the United States, and what sociodemographic characteristics moderate this relationship. We analyzed a national sample of adults in the United States (n = 6,475). Multiple logistic regression results revealed respondents experiencing food insecurity had an approximately 3.0 times significantly higher odds of a positive COVID-19 diagnosis (odds ratio [OR] = 2.95, 95% confidence interval [CI] = 1.38-6.32, p < 0.01), which remained significant after adjusting for sociodemographics and COVID-19 mitigation behaviors (OR = 2.59, 95% CI = 1.09-6.18, p < 0.05). Age group had a significant moderating effect (OR = 42.55, 95% CI = 3.13-579.15, p < 0.01). Results indicate experiencing food insecurity is associated with contracting COVID-19.Copyright © 2022 Taylor & Francis Group, LLC.

Planning cities for pandemics: A review of urban and transport planning lessons from COVID-19

For the past years, the world has been facing one of the worst pandemics of modern times. The COVID-19 outbreak joined a long list of infectious diseases that turned pandemic, and it will most likely leave scars and change how we live, plan, and manage the urban space and its infrastructures. Many fields of science were called into action to mitigate the impacts of this pandemic, including spatial and transport planning. Given the large number of articles recently published in these research areas, it is time to carry out an overview of the knowledge produced, synthesising, systematising, and critically analysing it. This article aims to review how the urban layout, accessibility and mobility influence the spread of a virus in an urban environment and what solutions exist or have been proposed to create a more effective and less intrusive response to pandemics. This review is split into two avenues of research: spatial planning and transport planning, including the direct and indirect impact on the environment and sustainability. © 2023 ICE Publishing: All rights reserved.

The significance of educational level in acquiring good literacy of COVID-19: a case study in Indonesia

Background. Surveys on Public Knowledge, Attitude, and Practice (PKAP) have been conducted in various countries with respondents from the public as well as health workers. Measuring the knowledge of the public about COVID-19 is very important to determine the knowledge gap among the public and also as an evaluation of the preventive efforts for COVID-19. Objective. The purpose of this research was to determine whether education level is a factor that affects one's literacy about COVID-19. Materials and Methods. This is cross-sectional research with online-based data collection using the Kobo toolbox application. The data collection was carried out from the 19th of April until the 2nd of May 2020. The number of people under study is 792. The level of knowledge was measured using 12 research questions with true or false question types. the multivariable logistic regression was carried out. Results. Most of the respondents (52.5%) were in the young age group (15-35 years old), were male (57.3%), and had a bache-lor or diploma education level (62.1%). Furthermore, most of the respondents had good knowledge (65.4%). The higher the respon-dents' educational level means, the better knowledge they had concerning COVID-19 (P=0.013). Conclusions. Public knowledge about COVID-19 is affected by their level of education. A good level of knowledge about COVID-19 was found among respondents with master's and doctoral degrees. This finding can contribute to the prevention of COVID-19, in which the priority of educating communities about COVID-19 should be given to those having an educational level below a master's degree.Copyright © the Author(s), 2023.

Artificial Intelligence in Covid-19

This book deals with the advantages of using artificial intelligence (AI) in the fight against the COVID-19 and against future pandemics that could threat humanity and our environment. This book is a practical, scientific and clinically relevant example of how medicine and mathematics will fuse in the 2020s, out of external pandemic pressure and out of scientific evolutionary necessity.This book contains a unique blend of the world's leading researchers, both in medicine, mathematics, computer science, clinical and preclinical medicine, and presents the research front of the usage of AI against pandemics.Equipped with this book the reader will learn about the latest AI advances against COVID-19, and how mathematics and algorithms can aid in preventing its spreading course, treatments, diagnostics, vaccines, clinical management and future evolution. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

24th Collegium Ramazzini Statement: Prevention of Work-Related Infection in the COVID-19 Pandemic

Background: Workers whose occupations put them in contact with infected persons and the public are at increased risk of coronavirus disease (COVID-19) infection. Recommendations: The Collegium Ramazzini calls on governments at all levels to protect worker health by strengthening public health systems;maintaining comprehensive social insurance systems;establishing policies that presume all COVID-19 infections in high-risk workers are work-related;enforcing all occupational health standards;and developing pandemic preparedness plans. The Collegium Ramazzini calls on all employers-large and small, public and private-to protect the health of all workers by developing disease preparedness plans;implementing basic infection control measures;establishing disease identification and isolation policies;reducing hazardous exposures;supporting personal protective equipment (PPE) programs;and restricting unnecessary travel. Conclusion(s): Governments and employers have legal obligations to protect worker health. They are not relieved of these duties during pandemics.Copyright © 2020 American College of Occupational and Environmental Medicine.

Monitoring, Tracking and Fighting Pandemics using Drone-based Artificial Intelligence in IoT

The world has altered since the World Health Organization (WHO) designated (COVID-19) a worldwide epidemic. Everything in society, from professions to routines, has shifted to accommodate the new reality. The World Health Organization warns that future pandemics of infectious diseases are likely and that people should be ready for the worst. Therefore, this study presents a framework for tracking and monitoring COVID-19 using a Deep Learning (DL) perfect. The suggested framework utilises UAVs (such as a quadcopter or drone) equipped with artificial intelligence (AI) and the Internet of Things (IoT) to keep an eye on and combat the spread of COVID-19. AI/IoT for COVID-19 nursing and a drone-based IoT scheme for sterilisation make up the bulk of the infrastructure. The proposed solution is based on the use of a current camera installed in a face-shield or helmet for use in emergency situations like pandemics. The developed AI algorithm processes the thermal images that have been detected using multi-scale similar convolution blocks (MPCs) and Res blocks that are trained using residual learning. When infected cases are detected, the helmet's embedded Internet of Things system can trigger the drone system to intervene. The infected population is eradicated with the help of the drone's sterilisation process. The developed system undergoes experimental evaluation, and the findings are presented. The developed outline delivers a novel and well-organized arrangement for monitoring and combating COVID-19 and additional future epidemics, as evidenced by the results. © 2023 IEEE.

Research on the spread of infectious diseases based on computer simulation

The virus has a big impact on the whole world. The new Coronavirus has a great impact on everyone's life and will even lead to changes in the world pattern. Because of the virus, society is not functioning properly, the recession, people's expectations of economic development are falling. Trains and planes were suspended in some areas. In this paper, computer is used to simulate SIR model, based on system dynamics, to study the spread of infectious diseases. The SIR model passes reality and limit tests. On the basis of the original model, supplementing the original model, isolation and vaccination can effectively stop the spread of the virus. It can slow the outbreak of the virus and reduce the number of infected people. Panic comes from the unknown, and our confidence in defeating the 2019-nCoV virus comes from our scientific base. © 2022 IEEE.

A Hybridized Stochastic SIR-Vasicek Model in Evaluating a Pandemic Emergency Financing Facility

In the context of the present global health crisis, we examine the design and valuation of a pandemic emergency financing facility (PEFF) akin to a catastrophe (CAT) bond. While a CAT bond typically enables fund generation to the insurers and re-insurers after a disaster happens, a PEFF or pandemic bond's payout is linked to random thresholds that keep evolving as the pandemic continues to unfold. The subtle difference in the timing and structure of the funding payout between the usual CAT bond and PEFF complicates the valuation of the latter. We address this complication, and our analysis identifies certain aspects in the PEFF's design that must be simplified and strengthened so that this financial instrument is able to serve the intent of its original creation. An extension of the compartmentalized deterministic epidemic model-which describes the random number of people in three classes: susceptible (S), infected (I), and removed (R) or SIR for short-to its stochastic analog is put forward. At time t, S(t), I(t), and R (t) satisfy a system of interacting stochastic differential equations in our extended framework. The payout is triggered when the number of infected people exceeds a predetermined threshold. A CAT-bond pricing setup is developed with the Vasicek-based financial risk factor correlated with the SIR dynamics for the PEFF valuation. The probability of a pandemic occurrence during the bond's term to maturity is calculated via a Poisson process. Our sensitivity analyses reveal that the SIR's disease transmission and recovery rates, as well as the interest rate's mean-reverting level, have a substantial effect on the bond price. Our proposed synthesized model was tested and validated using a Canadian COVID-19 dataset during the early development of the pandemic. We illustrate that the PEFF's payout could occur as early as seven weeks after the official declaration of the pandemic, and the deficiencies of the most recent PEFF sold by an international financial institution could be readily rectified.

Performance Analysis of VGG-16 Deep Learning Model for COVID-19 Detection using Chest X-Ray Images

COVID-19 is an infectious disease caused by newly discovered coronavirus. Currently, RT-PCR and Rapid Testing are used to test a person against COVID-19. These methods do not produce immediate results. Hence, we propose a solution to detect COVID-19 from chest X-ray images for immediate results. The solution is developed using a convolutional neural network architecture (VGG-16) model to extract features by transfer learning and a classification model to classify an input chest X-ray image as COVID-19 positive or negative. We introduced various parameters and computed the impact on the performance of the model to identify the parameters with high impact on the model's performance. The proposed solution is observed to provide best results compared to the existing ones. © 2023 Bharati Vidyapeeth, New Delhi.

The nursing students' experience of COVID-19 confirmation

This study was conducted to prepare the basic data for the development of practical nursing intervention programs for nursing college students who have been confirmed with COVID-19 since its outbreak in Korea. The subjects of this study were 70 nursing students at the University of Nursing located in Seoul, Gangwon-do, and Gyeonggi-do, Chungcheongnam-do. The data were collected from April 1st through April 30th, 2022, and analyzed it using the content analysis method. The experience of the nursing students infected with COVID-19 was classified and analyzed to draw a total of 187 significant statements and 36 categories. When establishing an infectious disease prevention program for nursing students and developing a practical nursing intervention program, it is necessary to focus on the preventive activities that emphasize personal aspects such as infection control, health management, and self-management, and to strengthen social support systems and improve quality of life. © 2023 The Authors. Published by IASE.

Electrostatic Precipitator Design Optimization for the Removal of Aerosol and Airborne Viruses

In the midst of the COVID-19 pandemic, new requirements for clean air supply are introduced for heating, ventilation, and air conditioning (HVAC) systems. One way for HVAC systems to efficiently remove airborne viruses is by filtering them. Unlike disposable filters that require repeated purchases of consumables, the electrostatic precipitator (ESP) is an alternative option without the drawback of reduced dust collection efficiency in high-efficiency particulate air (HEPA) filters due to dust buildup. The majority of viruses have a diameter ranging from 0.1 μm to 5 μm. This study proposed a two-stage ESP, which charged airborne viruses and particles via positive electrode ionization wire and collected them on a collecting plate with high voltage. Numerical simulations were conducted and revealed a continuous decrease in collection efficiencies between 0.1 μm and 0.5 μm, followed by a consistent increase from 0.5 μm to 1 μm. For particles larger than 1 μm, collection efficiencies exceeding 90% were easily achieved with the equipment used in this study. Previous studies have demonstrated that the collection efficiency of suspended particles is influenced by both the ESP voltage and turbulent flow at this stage. To improve the collection efficiency of aerosols ranging from 0.1 μm to 1 μm, this study used a multi-objective genetic algorithm (MOGA) in combination with numerical simulations to obtain the optimal parameter combination of ionization voltage and flow speed. The particle collection performance of the ESP was examined under the Japan Electrical Manufacturers' Association (JEMA) standards and showed consistent collection performance throughout the experiment. Moreover, after its design was optimized, the precipitator collected aerosols ranging from 0.1 μm to 3 μm, demonstrating an efficiency of over 95%. With such high collection efficiency, the proposed ESP can effectively filter airborne particles as efficiently as an N95 respirator, eliminating the need to wear a mask in a building and preventing the spread of droplet infectious diseases such as COVID-19 (0.08 μm–0.16 μm). © 2023 by the authors.