Trends in Biomathematics: Stability and Oscillations in Environmental, Social, and Biological Models: Selected Works from the BIOMAT Consortium Lectures, Rio de Janeiro, Brazil, 2021

This contributed volume convenes selected, peer-reviewed works presented at the BIOMAT 2021 International Symposium, which was virtually held on November 1-5, 2021, with its organization staff based in Rio de Janeiro, Brazil. In this volume the reader will find applications of mathematical modeling on health, ecology, and social interactions, addressing topics like probability distributions of mutations in different cancer cell types;oscillations in biological systems;modeling of marine ecosystems;mathematical modeling of organs and tissues at the cellular level;as well as studies on novel challenges related to COVID-19, including the mathematical analysis of a pandemic model targeting effective vaccination strategy and the modeling of the role of media coverage on mitigating the spread of infectious diseases. Held every year since 2001, the BIOMAT International Symposium gathers together, in a single conference, researchers from Mathematics, Physics, Biology, and affine fields to promote the interdisciplinary exchange of results, ideas and techniques, promoting truly international cooperation for problem discussion. BIOMAT volumes published from 2017 to 2020 are also available by Springer. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

From Asymptomatics to Zombies: Visualization-Based Education of Disease Modeling for Children

Throughout the COVID-19 pandemic, visualizations became commonplace in public communications to help people make sense of the world and the reasons behind government-imposed restrictions. Though the adult population were the main target of these messages, children were affected by restrictions through not being able to see friends and virtual schooling. However, through these daily models and visualizations, the pandemic response provided a way for children to understand what data scientists really do and provided new routes for engagement with STEM subjects. In this paper, we describe the development of an interactive and accessible visualization tool to be used in workshops for children to explain computational modeling of diseases, in particular COVID-19. We detail our design decisions based on approaches evidenced to be effective and engaging such as unplugged activities and interactivity. We share reflections and learnings from delivering these workshops to 140 children and assess their effectiveness. © 2023 Owner/Author.

Prospects of lassa fever candidate vaccines

Background: Lassa fever is an acute viral haemorrhagic disease caused by the Lassa virus (LASV). It is endemic in West Africa and infects about 300,000 people each year, leading to approximately 5000 deaths annually. The development of the LASV vaccine has been listed as a priority by the World Health Organization since 2018. Considering the accelerated development and availability of vaccines against COVID-19, we set out to assess the prospects of LASV vaccines and the progress made so far. Materials and Methods: We reviewed the progress made on twenty-six vaccine candidates listed by Salami et al. (2019) and searched for new vaccine candidates through Google Scholar, PubMed, and DOAJ from June to July 2021. We searched the articles published in English using keywords that included "vaccine" AND "Lassa fever" OR "Lassa virus" in the title/. Results: Thirty-four candidate vaccines were identified - 26 already listed in the review by Salami et al. and an additional 8, which were developed over the last seven years. 30 vaccines are still in the pre-clinical stage while 4 of them are currently undergoing clinical trials. The most promising candidates in 2019 were vesicular stomatitis virus-vectored vaccine and live-attenuated MV/LASV vaccine;both had progressed to clinical trials. Conclusions: Despite the focus on COVID-19 vaccines since 2020, LASV vaccine is under development and continues to make impressive progress, hence more emphasis should be put into exploring further clinical studies related to the most promising types of vaccines identified.

Evaluation of expression and titration of recombinant nucleocapsid protein as an immunogenic candidate against SARS-CoV2

Introduction: Covid-19 epidemic results from an infection caused by SARS-CoV2. Evolution-based analyses on the nucleotide sequences show that SARS-CoV2 is a member of the genus Beta-coronaviruses and its genome consists of a single-stranded RNA, encoding 16 proteins. Among the structural proteins, the nucleocapsid is the most abundant protein in virus structure, highly immunogenic, with sequence conservatory. Due to a large number of mutations in the spike protein, the aim of this study was to investigate bioinformatics, expression of nucleocapsid protein and evaluate its immunogenicity as an immunogenic candidate. Materials and Methods: B and T cell epitopes of nucleocapsid protein were examined in the IEDB database. The PET28a-N plasmid was transferred to E. coli BL21(DE3) expression host, and IPTG induced recombinant protein expression. The protein was purified using Ni-NTA column affinity chromatography, and the Western blotting method was utilized to confirm it. Finally, mice were immunized with three routes of purified protein. Statistical analysis of the control group injection and test results was carried out by t-test from SPSS software. Results: The optimized gene had a Codon adaptation index (CAI) of 0/97 Percentage of codons having high- frequency distribution was improved to 85%. Expression of recombinant protein in E. coli led to the production of BoNT/B-HCC with a molecular weight of 45 kDa. The total yield of purified protein was 43 mg/L. Immunization of mice induced serum antibody response. Statistical analysis showed that the antibody titer ratio was significantly different compared to the control sample and the antibody titer was acceptable up to a dilution of 1.256000. Conclusion: According to the present study results, the protein can be used as an immunogenic candidate for developing vaccines against SARS-CoV2 in future research.

Special Issue: Honor of Dr. Mike Bray on his retirement as the editor-in-chief of antiviral research. (Special Issue: Honor of Dr. Mike Bray on his retirement as the editor-in-chief of antiviral research.)

This special issue contains 23 articles that discuss various aspects of antiviral research, focusing on the contributions and legacy of Dr. Mike Bray, the retiring Editor-in-Chief of Antiviral Research. The articles cover a range of topics, including the underappreciated mouse model for Ebola virus disease, the history and impact of the mouse-adapted Ebola virus model, and the characterisation of CD-1 mice infected with different strains of Ebola virus. Other articles delve into transplacental vertical transmission of flaviviruses, the development of reverse genetic systems for SARS-CoV-2, and the mechanisms of action and drug resistance of nucleotide analogues against the virus. The special issue also explores therapeutics for flaviviral infections, alternative splicing in RNA virus infections, and targeted protein degradation as an antiviral approach.

Development of novel hybrid models for the prediction of COVID-19 in Kuwait. (Special issue on COVID.)

The first case of coronavirus 2019 (Covid-19) in Kuwait was reported on February 24, 2020, and then day by day cases of Covid-19 keep on increasing. The decision of the government about the standard citizens to repatriate them back to Kuwait from different epicenters of Covid-19 has created a big challenge. There is a need to estimate a prediction model for the estimation of this epidemic size. The main objective of the current study is to find an efficient way of prediction of this epidemic situation for coronavirus by using real-time data from 24th February to 12th June 2020. By considering the uncertainty in the current situation and non-stationary real-time data of corona cases, we consider a novel strategy for prediction purposes. By using a multilayer model with different decomposition techniques, we developed a novel hybrid model for decomposition and prediction of corona cases in Kuwait. A Hybrid methodology based on denoising, decomposition, prediction, and ensemble rules are applied to the confirmed corona cases in Kuwait. To evaluate the performance of the novel hybrid model in comparison with other existing models, we use mean relative error, mean absolute error, and mean square error. It is concluded that the proposed framework for the prediction of conformed corona cases indicated better performance as compared to other existing methods.

International Conference on "Biotechnological Interventions to Overcome Challenges of Covid/post-Covid Era-2022" (BIOCoPE-2022), Rajasthan, India, 24-25 March 2022

This journal issue includes 31 articles that discuss salt stress in plants and amelioration strategies;algal biorefinery;in-silico docking studies of selected phytochemicals against papain like protease of SARS-Cov-2;development of an effective strategy using green synthesized nanoparticles;effect of methyl jasmonate in enhanced growth, antioxidants and reduced Pb uptake in contrasting cluster bean cultivars;biosynthesis of silver nanoparticles using mushroom extract and its toxicity assessement in zebrafish embryos;phytogenic synthesis of metallic nanoparticles;abiotic stress responses and strategies of microbes mediated mitigation for sustainable agriculture.

Improved SEIR Model Based on Recovery Rate Optimization to Predict COVID-19

When using the traditional SEIR infectious disease model to predict the trend of novel coronavirus pneumonia epidemic, numerous initial parameters need to be tuned, and the parameters cannot change over time during the prediction process, which reduces the accuracy of the model. Firstly, thesis used a logistic model to preprocess the SEIR model parameters and proposed a SEIR model based on time series recovery rate optimization with a new parameter of effective immunity rate. Secondly, the model was trained with epidemic data from domestic and foreign provinces and cities, and the usability of the model was demonstrated experimentally, and the mean absolute percentage error (MAPE) and goodness of fit (R2) were used to compare with other models, which proved the superiority of the model prediction and indicated further research directions. © 2022 IEEE.

The possibility of using perfluorocarbon compounds for virus-associated pneumonia treatment

The issues of practicality in using perfluorocarbon gas transport emulsions (or pure perfluorocarbons) in severe virus-associated pneumonia treatment were considered, including those caused by coronavirus infection. Perfluorocarbons are fully fluorinated carbon compounds, on the basis of which artificial blood substitutes have been developed - gas transport perfluorocarbon emulsions for medical purposes. Perfluorocarbon emulsions were widely used in the treatment of patients in critical conditions of various genesis at the end of the last-the beginning of this century, accompanied by hypoxia, disorders of rheological properties and microcirculation of blood, perfusion of organs and tissues, intoxication, and inflammation. Large-scale clinical trials have shown a domestic plasma substitute advantage based on perfluorocarbons (perfluoroan) over foreign analogues. It is quite obvious that the inclusion of perfluorocarbon emulsions in the treatment regimens of severe virus-associated pneumonia can significantly improve this category's treatment results after analyzing the accumulated experience. A potentially useful area of therapy for acute respiratory distress syndrome is partial fluid ventilation with the use of perfluorocarbons as respiratory fluids as shown in the result of many studies on animal models and existing clinical experience. There is no gas-liquid boundary in the alveoli, as a result of which, there is an improvement in gas exchange in the lungs and a decrease in pressure in the respiratory tract when using this technique, due to the unique physicochemical properties of liquid perfluorocarbons. A promising strategy for improving liquid ventilation effectiveness using perfluorocarbon compounds is a combination with other therapeutic methods, particularly with moderate hypothermia. Antibiotics, anesthetics, vasoactive substances, or exogenous surfactant can be delivered to the lungs during liquid ventilation with perfluorocarbons, including to the affected areas, which will enhance the drugs accumulation in the lung tissues and minimize their systemic effects. However, the indications and the optimal technique for conducting liquid ventilation of the lungs in patients with acute respiratory distress syndrome have not been determined currently. Further research is needed to clarify the indications, select devices, and determine the optimal dosage regimens for perfluorocarbons, as well as search for new technical solutions for this technique.

Susceptibility of different animal species to experimental infection with SARS-CoV-2 (review)

The susceptibility of animal species to SARS-CoV-2, under experimental conditions, is a subject of great interest for the international scientific community. Compared to observational studies of natural disease outbreaks in different animal species, experimental studies based on in silico, in vitro and in vivo research, are important alternatives to evaluate the prediction of potential hosts for SARS-CoV-2 infection. In order to determine the susceptibility of a host species and the risk of acting as a potential animal reservoir, a large number of different animal species, domestic and wild, were experimentally infected with SARS-CoV-2, which were classified as permissive or resistant. Experimental infections have proven to be crucial for clarifying aspects of the pathogenetic mechanism, viral persistence and elimination, immune response, antiviral sensitivity, vaccine production, immunotherapy and improving diagnostic methods. In this article, some experimental infections carried out in different animal species will be reviewed, according to the data from the literature.

Parameter Adaptive SEIRD model for Epidemic Prediction

The classical infectious disease diffusion model has a deficiency of static parameters, which will lead to server prediction error. Therefore, this article used three different parameter fitting methods to construct a dynamic update mechanism of outbreak spread parameters and reversed fitting through the actual data of the epidemic. The best epidemic transmission parameters can effectively predict the growth of the outbreak in the next cycle. Then, we take the second wave of the outbreak in India as an example, the dynamic update mechanism of the epidemic spread parameters can effectively improve the accuracy of the prediction of the evolution of the novel coronavirus epidemic. According to the test results,we believe it can help the government make correct decisions, implement effective control and realize the reasonable allocation of emergency resources. © 2022 IEEE.

A methodological approach for modeling the spread of disease using geographical discrete-event spatial models

The study of infectious disease models has become increasingly important during the COVID-19 pandemic. The forecasting of disease spread using mathematical models has become a common practice by public health authorities, assisting in creating policies to combat the spread of the virus. Common approaches to the modeling of infectious diseases include compartmental differential equations and cellular automata, both of which do not describe the spatial dynamics of disease spread over unique geographical regions. We introduce a new methodology for modeling disease spread within a pandemic using geographical models. We demonstrate how geography-based Cell-Discrete-Event Systems Specification (DEVS) and the Cadmium JavaScript Object Notation (JSON) library can be used to develop geographical cellular models. We exemplify the use of these methodologies by developing different versions of a compartmental model that considers geographical-level transmission dynamics (e.g. movement restriction or population disobedience to public health guidelines), the effect of asymptomatic population, and vaccination stages with a varying immunity rate. Our approach provides an easily adaptable framework that allows rapid prototyping and modifications. In addition, it offers deterministic predictions for any number of regions simulated simultaneously and can be easily adapted to unique geographical areas. While the baseline model has been calibrated using real data from Ontario, we can update and/or add different infection profiles as soon as new information about the spread of the disease become available. © The Author(s) 2023.

Natural killer (NK) cells: an innate defense shield against respiratory viral infections

In general, B and T lymphocytes, which are involved in adaptive immunity, are in charge of cell-mediated response and antibody-mediated immunity, respectively. Another subset of lymphocytes, known as natural killer (NK) cells, are innate effector cells. They serve as the body's initial line of defence against viral infections. They perform the task of eliminating stressed cells and are crucial for tumour immunity. These cells are capable of performing their killing function without clonal expansion and differentiation following activation. The NK cells will immediately eliminate infected host cells but other lymphocytes need lymphocyte proliferative response which takes several days and further differentiate into effector cells, so that they eliminate host cells infected by the viral pathogen. The NK cells also form a bridge between the adaptive and innate immunity and play significant roles during respiratory infection. Number and the role of NK cells correlate with the severity of severe acute respiratory syndrome (SARS);the number and the percentage of CD158b+ NK cells in severe SARS infection were significantly less in number than those with mild cases. Innate defence mechanisms, particularly NK cells, are able to control SARS infection even in the absence of T cells and antibodies, according to cellular immunological responses to SARS infection in mice. As a result, NK cells are crucial in the fight against viral infections of the respiratory system. As an innate immune system, they serve as the initial line of virus protection. It is possible to do additional research to take advantage of this NK cell trait and develop a cutting-edge therapeutic approach to fight developing respiratory viral diseases.

A dynamic design strategy of epidemic spreading parameters based on data driven

When modeling and fitting various kinds of epidemic outbreaks, the value of parameters has always been an important practical problem for many scholars. In the existing studies, most of the authors select a fixed parameter by referring to the relevant literature or combined with medical experiments. With the help of Euler difference transformation and the characteristics of the solution of linear equations, we innovatively propose a dynamic update strategy of epidemic diffusion parameters based on data-driven in this study in order to overcome the above limitation. The method can help decision-makers to calculate the optimal parameters of epidemic spread by combining the real-time update data. A case study is conducted with the COVID-19 data of Wuhan. The results show that the dynamic parameter update strategy designed in this paper can effectively improve the accuracy of the evolution prediction of epidemic outbreaks, which provides an important decision support for the accurate allocation of government emergency resources. © 2023 Northeast University. All rights reserved.

Human Mobility Driven Modeling of an Infectious Disease

In conventional disease models, disease properties are dominant parameters (e.g., infection rate, incubation pe-riod). As seen in the recent literature on infectious diseases, human behavior - particularly mobility - plays a crucial role in spreading diseases. This paper proposes an epidemiological model named SEIRD+m that considers human mobility instead of modeling disease properties alone. SEIRD+m relies on the core deterministic epidemic model SEIR (Susceptible, Exposed, Infected, and Recovered), adds a new compartment D - Dead, and enhances each SEIRD component by human mobility information (such as time, location, and movements) retrieved from cell-phone data collected by SafeGraph. We demonstrate a way to reduce the number of infections and deaths due to COVID-19 by restricting mobility on specific Census Block Groups (CBGs) detected as COVID-19 hotspots. A case study in this paper depicts that a reduction of mobility by 50 % could help reduce the number of infections and deaths in significant percentages in different population groups based on race, income, and age. © 2022 IEEE.

Special Issue: Mutagenic factors in the environment impacting human and animal health. (Special Issue: Mutagenic factors in the environment impacting human and animal health.)

This special issue includes 15 articles that discuss the mutagenic effect of tobacco smoke on male fertility;environmental and occupational exposure of metals and female reproductive health;free radical biology in neurological manifestations;paternal factors in recurrent pregnancy loss;mechanical dependency of the SARS-CoV-2 virus and the renin-angiotensin-aldosterone (RAAS) axis;a perspective review on medicinal plant resources for their antimutagenic potentials;asystematic review and meta-analysis of the impacts of glyphosate on the reproductive hormones;impact of ginseng on neurotoxicity induced by cisplatin in rats.

Pathology of animal models of COVID-19

This paper describes the pathogenesis and immunology of Macaca mulatta, Macaca fascicularis, ferrets, Syrian golden hamsters (Mesocricetus auratus), mice, cats, mink, pigs and rabbits used as models for SARS-CoV-2 infection.

Extracting Self-Reported COVID-19 Symptom Tweets and Twitter Movement Mobility Origin/Destination Matrices to Inform Disease Models

The emergence of the novel coronavirus (COVID-19) generated a need to quickly and accurately assemble up-to-date information related to its spread. In this research article, we propose two methods in which Twitter is useful when modelling the spread of COVID-19: (1) machine learning algorithms trained in English, Spanish, German, Portuguese and Italian are used to identify symptomatic individuals derived from Twitter. Using the geo-location attached to each tweet, we map users to a geographic location to produce a time-series of potential symptomatic individuals. We calibrate an extended SEIRD epidemiological model with combinations of low-latency data feeds, including the symptomatic tweets, with death data and infer the parameters of the model. We then evaluate the usefulness of the data feeds when making predictions of daily deaths in 50 US States, 16 Latin American countries, 2 European countries and 7 NHS (National Health Service) regions in the UK. We show that using symptomatic tweets can result in a 6% and 17% increase in mean squared error accuracy, on average, when predicting COVID-19 deaths in US States and the rest of the world, respectively, compared to using solely death data. (2) Origin/destination (O/D) matrices, for movements between seven NHS regions, are constructed by determining when a user has tweeted twice in a 24 h period in two different locations. We show that increasing and decreasing a social connectivity parameter within an SIR model affects the rate of spread of a disease. © 2023 by the authors.

Ocular manifestations of COVID-19 infection

The eyes, which were not previously the subject of investigation, have emerged as one of the more intriguing aspect of COVID-19 infection. The probable pathways for SARS-CoV-2 infection through the eyes are either through the tear film and draining tear ducts leading to the upper respiratory tract and gastrointestinal tract, or through the conjunctiva into limbal superficial cells into the inner eye, where distribution via the blood or nervous system appears to be possible. Virus pathogenesis through tear and conjunctiva has been established in animal models of several coronavirus eye diseases, and preliminary research have shown that SARS-CoV-2 may do the same in humans. Given that COVID-19 cases have reached pandemic proportions and are on the rise, it is vital to recognize the illness's ocular manifestations and avert potentially vision-threatening effects. Coronaviruses may be transmitted between animals and humans through evolution, therefore investigating them in animal models could be crucial in the future to discover further elements of ocular involvement. It is recommended that health care and individual personnel take steps to avoid infection and limit viral transmission. More study is needed to understand the transmission pathways and the alternatives for COVID-19 prevention and therapy disseminated through the ocular surfaces for prompt recovery of the patients.

Towards a Better Understanding of the Complexities of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome and Long COVID.

Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a complex condition arising in susceptible people, predominantly following viral infection, but also other stressful events. The susceptibility factors discussed here are both genetic and environmental although not well understood. While the dysfunctional physiology in ME/CFS is becoming clearer, understanding has been hampered by different combinations of symptoms in each affected person. A common core set of mainly neurological symptoms forms the modern clinical case definition, in the absence of an accessible molecular diagnostic test. This landscape has prompted interest in whether ME/CFS patients can be classified into a particular phenotype/subtype that might assist better management of their illness and suggest preferred therapeutic options. Currently, the same promising drugs, nutraceuticals, or behavioral therapies available can be beneficial, have no effect, or be detrimental to each individual patient. We have shown that individuals with the same disease profile exhibit unique molecular changes and physiological responses to stress, exercise and even vaccination. Key features of ME/CFS discussed here are the possible mechanisms determining the shift of an immune/inflammatory response from transient to chronic in ME/CFS, and how the brain and CNS manifests the neurological symptoms, likely with activation of its specific immune system and resulting neuroinflammation. The many cases of the post viral ME/CFS-like condition, Long COVID, following SARS-CoV-2 infection, and the intense research interest and investment in understanding this condition, provide exciting opportunities for the development of new therapeutics that will benefit ME/CFS patients.