Amyotrophic lateral sclerosis regional progression intervals change according to time of involvement of different body regions.

BACKGROUND AND PURPOSE: The prediction of disease course is one of the main targets of amyotrophic lateral sclerosis (ALS) research, particularly considering its wide phenotypic heterogeneity. Despite many attempts to classify patients into prognostic categories according to the different spreading patterns at diagnosis, a precise regional progression rate and the time of involvement of each region has yet to be clarified. The aim of our study was to evaluate the functional decline in different body regions according to their time of involvement during disease course. METHODS: In a population-based dataset of ALS patients, we analysed the functional decline in different body regions according to time and order of regional involvement. We calculated the regional progression intervals (RPIs) between initial involvement and severe functional impairment using the ALS Functional Rating Scale revised (ALSFRS-r) subscores for the bulbar, upper limb, lower limb and respiratory/thoracic regions. Time-to-event analyses, adjusted for age, sex, ALSFRS-r pre-slope (ΔALSFRS-R), cognitive status, and mutational status were performed. RESULTS: The duration of RPI differed significantly among ALS phenotypes, with the RPI of the first region involved being significantly longer than the RPIs of regions involved later. Cox proportional hazard models showed that in fact a longer time between disease onset and initial regional involvement was related to a reduced duration of the RPI duration in each different body region (bulbar region: hazard ratio [HR] 1.11, 95% confidence interval [CI] 1.06-1.16, p < 0.001; upper limb region: HR 1.16, 95% CI 1.06-1.28, p = 0.002; lower limb region: HR 1.11, 95% CI 1.03-1.19, p = 0.009; respiratory/thoracic region: HR 1.10, 95% CI 1.06-1.14, p = 0.005). CONCLUSIONS: We found that the progression of functional decline accelerates in regions involved later during disease course. Our findings can be useful in patient management and prognosis prediction.

Emergence of protective behaviour under different risk perceptions to disease spreading.

The behaviour of individuals is a main actor in the control of the spread of a communicable disease and, in turn, the spread of an infectious disease can trigger behavioural changes in a population. Here, we study the emergence of individuals' protective behaviours in response to the spread of a disease by considering two different social attitudes within the same population: concerned and risky. Generally speaking, concerned individuals have a larger risk aversion than risky individuals. To study the emergence of protective behaviours, we couple, to the epidemic evolution of a susceptible-infected-susceptible model, a decision game based on the perceived risk of infection. Using this framework, we find the effect of the protection strategy on the epidemic threshold for each of the two subpopulations (concerned and risky), and study under which conditions risky individuals are persuaded to protect themselves or, on the contrary, can take advantage of a herd immunity by remaining healthy without protecting themselves, thanks to the shield provided by concerned individuals. This article is part of the theme issue 'Emergent phenomena in complex physical and socio-technical systems: from cells to societies'.

Kinetic exchange models of societies and economies.

The statistical nature of collective human behaviour in a society is a topic of broad current interest. From formation of consensus through exchange of ideas, distributing wealth through exchanges of money, traffic flows, growth of cities to spread of infectious diseases, the application range of such collective responses cuts across multiple disciplines. Kinetic models have been an elegant and powerful tool to explain such collective phenomena in a myriad of human interaction-based problems, where an energy consideration for dynamics is generally inaccessible. Nonetheless, in this age of Big Data, seeking empirical regularities emerging out of collective responses is a prominent and essential approach, much like the empirical thermodynamic principles preceding quantitative foundations of statistical mechanics. In this introductory article of the theme issue, we will provide an overview of the field of applications of kinetic theories in different socio-economic contexts and its recent boosting topics. Moreover, we will put the contributions to the theme issue in an appropriate perspective. This article is part of the theme issue 'Kinetic exchange models of societies and economies'.

Effect of vaccine efficacy on disease transmission with age-structured.

Recent outbreaks of novel infectious diseases (e.g., COVID-19, H2N3) have highlighted the threat of pathogen transmission, and vaccination offers a necessary tool to relieve illness. However, vaccine efficacy is one of the barriers to eradicating the epidemic. Intuitively, vaccine efficacy is closely related to age structures, and the distribution of vaccine efficacy usually obeys a Gaussian distribution, such as with H3N2 and influenza A and B. Based on this fact, in this paper, we study the effect of vaccine efficacy on disease spread by considering different age structures and extending the traditional susceptible-infected-recovery/vaccinator(SIR/V) model with two stages to three stages, which includes the decision-making stage, epidemic stage, and birth-death stage. Extensive numerical simulations show that our model generates a higher vaccination level compared with the case of complete vaccine efficacy because the vaccinated individuals in our model can form small and numerous clusters slower than that of complete vaccine efficacy. In addition, priority vaccination for the elderly is conducive to halting the epidemic when facing population ageing. Our work is expected to provide valuable information for decision-making and the design of more effective disease control strategies.

Does Social Distancing Matter for Infectious Disease Propagation? An SEIR Model and Gompertz Law Based Cellular Automaton.

In this paper, we present stochastic synchronous cellular automaton defined on a square lattice. The automaton rules are based on the SEIR (susceptible → exposed → infected → recovered) model with probabilistic parameters gathered from real-world data on human mortality and the characteristics of the SARS-CoV-2 disease. With computer simulations, we show the influence of the radius of the neighborhood on the number of infected and deceased agents in the artificial population. The increase in the radius of the neighborhood favors the spread of the pandemic. However, for a large range of interactions of exposed agents (who neither have symptoms of the disease nor have been diagnosed by appropriate tests), even isolation of infected agents cannot prevent successful disease propagation. This supports aggressive testing against disease as one of the useful strategies to prevent large peaks of infection in the spread of SARS-CoV-2-like diseases.

Extracellular Vesicles Physiological Role and the Particular Case of Disease-Spreading Mechanisms in Polyglutamine Diseases.

Recent research demonstrated pathological spreading of the disease-causing proteins from one focal point across other brain regions for some neurodegenerative diseases, such as Parkinson's and Alzheimer's disease. Spreading mediated by extracellular vesicles is one of the proposed disease-spreading mechanisms. Extracellular vesicles are cell membrane-derived vesicles, used by cells for cell-to-cell communication and excretion of toxic components. Importantly, extracellular vesicles carrying pathological molecules, when internalized by "healthy" cells, may trigger pathological pathways and, consequently, promote disease spreading to neighboring cells. Polyglutamine diseases are a group of genetic neurodegenerative disorders characterized by the accumulation of mutant misfolded proteins carrying an expanded tract of glutamines, including Huntington's and Machado-Joseph disease. The pathological spread of the misfolded proteins or the corresponding mutant mRNA has been explored. The understanding of the disease-spreading mechanism that plays a key role in the pathology progression of these diseases can result in the development of effective therapeutic approaches to stop disease progression, arresting the spread of the toxic components and disease aggravation. Therefore, the present review's main focus is the disease-spreading mechanisms with emphasis on polyglutamine diseases and the putative role played by extracellular vesicles in this process.

Myelinosome Organelles in the Retina of R6/1 Huntington Disease (HD) Mice: Ubiquitous Distribution and Possible Role in Disease Spreading.

Visual deficit is one of the complications of Huntington disease (HD), a fatal neurological disorder caused by CAG trinucleotide expansions in the Huntingtin gene, leading to the production of mutant Huntingtin (mHTT) protein. Transgenic HD R6/1 mice expressing human HTT exon1 with 115 CAG repeats recapitulate major features of the human pathology and exhibit a degeneration of the retina. Our aim was to gain insight into the ultrastructure of the pathological HD R6/1 retina by electron microscopy (EM). We show that the HD R6/1 retina is enriched with unusual organelles myelinosomes, produced by retinal neurons and glia. Myelinosomes are present in all nuclear and plexiform layers, in the synaptic terminals of photoreceptors, in the processes of retinal neurons and glial cells, and in the subretinal space. In vitro study shows that myelinosomes secreted by human retinal glial Müller MIO-M1 cells transfected with EGFP-mHTT-exon1 carry EGFP-mHTT-exon1 protein, as revealed by immuno-EM and Western-blotting. Myelinosomes loaded with mHTT-exon1 are incorporated by naive neuronal/neuroblastoma SH-SY5Y cells. This results in the emergence of mHTT-exon1 in recipient cells. This process is blocked by membrane fusion inhibitor MDL 28170. Conclusion: Incorporation of myelinosomes carrying mHTT-exon1 in recipient cells may contribute to HD spreading in the retina. Exploring ocular fluids for myelinosome presence could bring an additional biomarker for HD diagnostics.

Evaluation of the potential incidence of COVID-19 and effectiveness of containment measures in Spain: a data-driven approach.

BACKGROUND: We are currently experiencing an unprecedented challenge, managing and containing an outbreak of a new coronavirus disease known as COVID-19. While China-where the outbreak started-seems to have been able to contain the growth of the epidemic, different outbreaks are nowadays present in multiple countries. Nonetheless, authorities have taken action and implemented containment measures, even if not everything is known. METHODS: To facilitate this task, we have studied the effect of different containment strategies that can be put into effect. Our work referred initially to the situation in Spain as of February 28, 2020, where a few dozens of cases had been detected, but has been updated to match the current situation as of 13 April. We implemented an SEIR metapopulation model that allows tracing explicitly the spatial spread of the disease through data-driven stochastic simulations. RESULTS: Our results are in line with the most recent recommendations from the World Health Organization, namely, that the best strategy is the early detection and isolation of individuals with symptoms, followed by interventions and public recommendations aimed at reducing the transmissibility of the disease, which, although might not be sufficient for disease eradication, would produce as a second order effect a delay of several days in the raise of the number of infected cases. CONCLUSIONS: Many quantitative aspects of the natural history of the disease are still unknown, such as the amount of possible asymptomatic spreading or the role of age in both the susceptibility and mortality of the disease. However, preparedness plans and mitigation interventions should be ready for quick and efficacious deployment globally. The scenarios evaluated here through data-driven simulations indicate that measures aimed at reducing individuals' flow are much less effective than others intended for early case identification and isolation. Therefore, resources should be directed towards detecting as many and as fast as possible the new cases and isolate them.

Hand-Hygiene Mitigation Strategies Against Global Disease Spreading through the Air Transportation Network.

The risk for a global transmission of flu-type viruses is strengthened by the physical contact between humans and accelerated through individual mobility patterns. The Air Transportation System plays a critical role in such transmissions because it is responsible for fast and long-range human travel, while its building components-the airports-are crowded, confined areas with usually poor hygiene. Centers for Disease Control and Prevention (CDC) and World Health Organization (WHO) consider hand hygiene as the most efficient and cost-effective way to limit disease propagation. Results from clinical studies reveal the effect of hand washing on individual transmissibility of infectious diseases. However, its potential as a mitigation strategy against the global risk for a pandemic has not been fully explored. Here, we use epidemiological modeling and data-driven simulations to elucidate the role of individual engagement with hand hygiene inside airports in conjunction with human travel on the global spread of epidemics. We find that, by increasing travelers engagement with hand hygiene at all airports, a potential pandemic can be inhibited by 24% to 69%. In addition, we identify 10 airports at the core of a cost-optimal deployment of the hand-washing mitigation strategy. Increasing hand-washing rate at only those 10 influential locations, the risk of a pandemic could potentially drop by up to 37%. Our results provide evidence for the effectiveness of hand hygiene in airports on the global spread of infections that could shape the way public-health policy is implemented with respect to the overall objective of mitigating potential population health crises.

A non-homogeneous Markov early epidemic growth dynamics model. Application to the SARS-CoV-2 pandemic.

This work introduces a new markovian stochastic model that can be described as a non-homogeneous Pure Birth process. We propose a functional form of birth rate that depends on the number of individuals in the population and on the elapsed time, allowing us to model a contagion effect. Thus, we model the early stages of an epidemic. The number of individuals then becomes the infectious cases and the birth rate becomes the incidence rate. We obtain this way a process that depends on two competitive phenomena, infection and immunization. Variations in those rates allow us to monitor how effective the actions taken by government and health organizations are. From our model, three useful indicators for the epidemic evolution over time are obtained: the immunization rate, the infection/immunization ratio and the mean time between infections (MTBI). The proposed model allows either positive or negative concavities for the mean value curve, provided the infection/immunization ratio is either greater or less than one. We apply this model to the present SARS-CoV-2 pandemic still in its early growth stage in Latin American countries. As it is shown, the model accomplishes a good fit for the real number of both positive cases and deaths. We analyze the evolution of the three indicators for several countries and perform a comparative study between them. Important conclusions are obtained from this analysis.

Social network analysis for better understanding of influenza.

INTRODUCTION: The objective of this study is to improve the understanding of spatial spreading of complicated cases of influenza that required hospitalizations, by creating heatmaps and social networks. They will allow to identify critical hubs and routes of spreading of Influenza, in specific geographic locations, in order to contain infections and prevent complications, that require hospitalizations. MATERIAL AND METHODS: Data were downloaded from the Healthcare Cost and Utilization Project (HCUP) - SID, New York State database. Patients hospitalized with flu complications, between 2003 and 2012 were included in the research (30,380 cases). A novel approach was designed, by constructing heatmaps for specific geographic regions in New York state and power law networks, in order to analyze distribution of hospitalized flu cases. RESULTS: Heatmaps revealed that distributions of patients follow urban areas and big roads, indicating that flu spreads along routes, that people use to travel. A scale-free network, created from correlations among zip codes, discovered that, the highest populated zip codes didn't have the largest number of patients with flu complications. Among the top five most affected zip codes, four were in Bronx. Demographics of top affected zip codes were presented in results. Normalized numbers of cases per population revealed that, none of zip codes from Bronx were in the top 20. All zip codes with the highest node degrees were in New York City area. DISCUSSION: Heatmaps identified geographic distribution of hospitalized flu patients and network analysis identified hubs of the infection. Our results will enable better estimation of resources for prevention and treatment of hospitalized patients with complications of Influenza. CONCLUSION: Analyses of geographic distribution of hospitalized patients with Influenza and demographic characteristics of populations, help us to make better planning and management of resources for Influenza patients, that require hospitalization. Obtained results could potentially help to save many lives and improve the health of the population.

Prion-like properties of disease-relevant proteins in amyotrophic lateral sclerosis.

The hallmark of age-related neurodegenerative diseases is the appearance of cellular protein deposits and spreading of this pathology throughout the central nervous system. Growing evidence has shown the involvement and critical role of proteins with prion-like properties in the formation of these characteristic cellular aggregates. Prion-like domains of such proteins with their proposed function in the organization of membraneless organelles are prone for misfolding and promoting further aggregation. Spreading of these toxic aggregates between cells and across tissues can explain the progression of clinical phenotypes and pathology in a stereotypical manner, characteristic for almost every neurodegenerative disease. Here, we want to review the current evidence for the role of prion-like mechanisms in classical neurodegenerative diseases and ALS in particular. We will also discuss an intriguingly central role of the protein TDP-43 in the majority of cases of this devastating disease.

Stepwise acquirement of hallmark neuropathology in FUS-ALS iPSC models depends on mutation type and neuronal aging.

Autosomal-dominant mutations within the gene FUS (fused in sarcoma) are responsible for 5% of familial cases of amyotrophic lateral sclerosis (ALS). The FUS protein is physiologically mainly located in the nucleus, while cytoplasmic FUS aggregates are pathological hallmarks of FUS-ALS. Data from non-neuronal cell models and/or models using heterologous expression of FUS mutants suggest cytoplasmic FUS translocation as a pivotal initial event which leads to neurodegeneration depending on a second hit. Here we present the first human model of FUS-ALS using patient-derived neurons carrying endogenous FUS mutations leading to a benign (R521C) or a more severe clinical phenotype (frameshift mutation R495QfsX527). We thereby showed that the severity of the underlying FUS mutation determines the amount of cytoplasmic FUS accumulation and cellular vulnerability to exogenous stress. Cytoplasmic FUS inclusions formed spontaneously depending on both, severity of FUS mutation and neuronal aging. These aggregates showed typical characteristics of FUS-ALS including methylated FUS. Finally, neurodegeneration was not specific to layer V cortical neurons perfectly in line with the current model of disease spreading in ALS. Our study highlights the value and usefulness of patient-derived cell models in FUS-ALS.

Mutant copper-zinc superoxide dismutase (SOD1) induces protein secretion pathway alterations and exosome release in astrocytes: implications for disease spreading and motor neuron pathology in amyotrophic lateral sclerosis.

Amyotrophic lateral sclerosis is the most common motor neuron disease and is still incurable. The mechanisms leading to the selective motor neuron vulnerability are still not known. The interplay between motor neurons and astrocytes is crucial in the outcome of the disease. We show that mutant copper-zinc superoxide dismutase (SOD1) overexpression in primary astrocyte cultures is associated with decreased levels of proteins involved in secretory pathways. This is linked to a general reduction of total secreted proteins, except for specific enrichment in a number of proteins in the media, such as mutant SOD1 and valosin-containing protein (VCP)/p97. Because there was also an increase in exosome release, we can deduce that astrocytes expressing mutant SOD1 activate unconventional secretory pathways, possibly as a protective mechanism. This may help limit the formation of intracellular aggregates and overcome mutant SOD1 toxicity. We also found that astrocyte-derived exosomes efficiently transfer mutant SOD1 to spinal neurons and induce selective motor neuron death. We conclude that the expression of mutant SOD1 has a substantial impact on astrocyte protein secretion pathways, contributing to motor neuron pathology and disease spread.

Effect of SARS-CoV-2 shedding rate distribution of individuals during their disease days on the estimation of the number of infected people. Application of wastewater-based epidemiology to the city of Thessaloniki, Greece.

During the COVID-19 pandemic, wastewater-based epidemiology has proved to be an important tool for monitoring the spread of a disease in a population. Indeed, wastewater surveillance was successfully used as a complementary approach to support public health monitoring schemes and decision-making policies. An essential feature for the estimation of a disease transmission using wastewater data is the distribution of viral shedding rate of individuals in their personal human wastes as a function of the days of their infection. Several candidate shapes for this function have been proposed in literature for SARS-CoV-2. The purpose of the present work is to explore the proposed function shapes and examine their significance on analyzing wastewater SARS-CoV-2 shedding rate data. For this purpose, a simple model is employed applying to medical surveillance and wastewater data of the city of Thessaloniki during a period of Omicron variant domination in 2022. The distribution shapes are normalized with respect to the total virus shedding and then their basic features are investigated. Detailed analysis reveals that the main parameter determining the results of the model is the difference between the day of maximum shedding rate and the day of infection reporting. Since the latter is not part of the distribution shape, the major feature of the distribution affecting the estimation of the number of infected people is the day of maximum shedding rate with respect to the initial infection day. On the contrary, the duration of shedding (total number of disease days) as well as the exact shape of the distribution are by far less important. The incorporation of such wastewater surveillance models in conventional epidemiological models - based on recorded disease transmission data- may improve predictions for disease spread during outbreaks.

Effects of delayed recovery and nonuniform transmission on the spreading of diseases in complex networks.

We investigate the effects of delaying the time to recovery (delayed recovery) and of nonuniform transmission on the propagation of diseases on structured populations. Through a mean-field approximation and large-scale numerical simulations, we find that postponing the transition from the infectious to the recovered states can largely reduce the epidemic threshold, therefore promoting the outbreak of epidemics. On the other hand, if we consider nonuniform transmission among individuals, the epidemic threshold increases, thus inhibiting the spreading process. When both mechanisms are at work, the latter might prevail, hence resulting in an increase of the epidemic threshold with respect to the standard case, in which both ingredients are absent. Our findings are of interest for a better understanding of how diseases propagate on structured populations and to a further design of efficient immunization strategies.

Antibiotic action and resistance: updated review of mechanisms, spread, influencing factors, and alternative approaches for combating resistance.

Antibiotics represent a frequently employed therapeutic modality for the management of bacterial infections across diverse domains, including human health, agriculture, livestock breeding, and fish farming. The efficacy of antibiotics relies on four distinct mechanisms of action, which are discussed in detail in this review, along with accompanying diagrammatic illustrations. Despite their effectiveness, antibiotic resistance has emerged as a significant challenge to treating bacterial infections. Bacteria have developed defense mechanisms against antibiotics, rendering them ineffective. This review delves into the specific mechanisms that bacteria have developed to resist antibiotics, with the help of diagrammatic illustrations. Antibiotic resistance can spread among bacteria through various routes, resulting in previously susceptible bacteria becoming antibiotic-resistant. Multiple factors contribute to the worsening crisis of antibiotic resistance, including human misuse of antibiotics. This review also emphasizes alternative solutions proposed to mitigate the exacerbation of antibiotic resistance.

Influencing factors of COVID-19 spreading: a case study of Thailand.

Aim: A novel corona virus disease 2019 (COVID-19) was declared as pandemic by WHO as global level and local levels in many countries. The movement of people might be one influencing factor, this paper aims to report the situation COVID-19 and spreading in Thailand, including influencing factors of spreading and control. Subject and method: Infected, confirmed COVID-19 data were obtained from the official website of the Department of Disease Control, Ministry of Public Health. Tourist data was downloaded from Ministry of Tourism and Sports. Researchers analyzed the situation from the first found case in Thailand until 15 April 2020 with the timeline of important influencing factors. Correlation coefficients of tourist data and infected case was calculated by person correlation coefficient. Results: The number of infected cases was significant associated (correlation coefficient > 0.7) with economic factor, namely; number of visitors, generated income from both Thai and foreigner tourist (p value <0.01). The influencing factors of slow increased rate were the enforcement and implementation of both central and local government regulation, the strength of the Thai health care system, the culture and social relation, the partnership among various governmental and private sectors. Conclusion: We found that the number of tourist and their activities were significant associated with number of infected, confirmed COVID-19 cases. The public education and social supporting were the key roles for regulation enforcement and implementation.

Spatial effects in parasite-induced marine diseases of immobile hosts.

Emerging marine infectious diseases pose a substantial threat to marine ecosystems and the conservation of their biodiversity. Compartmental models of epidemic transmission in marine sessile organisms, available only recently, are based on non-spatial descriptions in which space is homogenized and parasite mobility is not explicitly accounted for. However, in realistic scenarios epidemic transmission is conditioned by the spatial distribution of hosts and the parasites' mobility patterns, calling for an explicit description of space. In this work, we develop a spatially explicit individual-based model to study disease transmission by waterborne parasites in sessile marine populations. We investigate the impact of spatial disease transmission through extensive numerical simulations and theoretical analysis. Specifically, the effects of parasite mobility into the epidemic threshold and the temporal progression of the epidemic are assessed. We show that larger values of pathogen mobility imply more severe epidemics, as the number of infections increases, and shorter timescales to extinction. An analytical expression for the basic reproduction number of the spatial model, R ~ 0 , is derived as a function of the non-spatial counterpart, R 0, which characterizes a transition between a disease-free and a propagation phase, in which the disease propagates over a large fraction of the system.

The basic reproductive ratio of the 2022 outbreak of the monkey pox virus disease for the United Kingdom, Canada, Brazil, the United Arab Emirates, and Nigeria.

Background: A recent outbreak of the monkey pox virus disease (MPVD) started to spread over the world before the second half of the 2022 year. This outbreak of the monkey pox virus disease is known as the 2022 outbreak of the monkey pox virus disease. The monkey pox virus disease is a type of the pox disease similar to the human one. This disease is an endemic in some African countries; however, a new spreading of this disease started to appear in other countries, such as the Spain, brazil, Greece, the United Kingdom, and Portugal, Australia, and the USA. As of the end of September 2022, the MPVD spread over than 107 countries over the world. Results: This study focuses on the employing of the simplest model of the diseases forecasting which is SIRD model for the finding of the basic reproductive ratio of the monkey pox virus disease in multiple countries over the world where the disease spreads. The model takes into accounts the number of the susceptible people, the number of the infectious people, the number of the recovered people, and the number of the deceased people. Based on the results of the SIRD model coefficients, we find that the basic reproductive ratio values of the recent spreading of the monkey pox virus disease are 1.3274 for the United Kingdom where the first case of the disease was recorded, 1.0714 for the United Arab Emirates, 1.0866 for Nigeria, 1.5589 for Brazil, and 1.3610 for Canada. Conclusions: We find that the average value of the basic reproductive ratio of the 2022 outbreak of the monkey pox virus disease is about 1.2809. This important result of our calculations predicts that the 2022 outbreak of the monkey pox virus disease is turned into pandemic over the world. The things which confirm this result, based on our calculations, are the values of the basic reproductive ratio of the 2022 outbreak of the disease in the considered countries from multiple continents where all the values of the basic reproductive ratio are bigger than one. From this point, the counties over the world must apply multiple procedures for limiting the spreading of the monkey pox virus disease.