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1.
Chaos ; 32(10): 103102, 2022 Oct.
Article in English | MEDLINE | ID: covidwho-2077214

ABSTRACT

With the outbreak of COVID-19, great loss and damage were brought to human society, making the study of epidemic spreading become a significant topic nowadays. To analyze the spread of infectious diseases among different areas, e.g., communities, cities, or countries, we construct a network, based on the epidemic model and the network coupling, whose nodes denote areas, and edges represent population migrations between two areas. Each node follows its dynamic, which describes an epidemic spreading among individuals in an area, and the node also interacts with other nodes, which indicates the spreading among different areas. By giving mathematical proof, we deduce that our model has a stable solution despite the network structure. We propose the peak infected ratio (PIR) as a property of infectious diseases in a certain area, which is not independent of the network structure. We find that increasing the population mobility or the disease infectiousness both cause higher peak infected population all over different by simulation. Furthermore, we apply our model to real-world data on COVID-19 and after properly adjusting the parameters of our model, the distribution of the peak infection ratio in different areas can be well fitted.


Subject(s)
COVID-19 , Communicable Diseases , Epidemics , Humans , Computer Simulation , Disease Outbreaks , Communicable Diseases/epidemiology
2.
Epidemiol Infect ; 150: e171, 2022 Sep 27.
Article in English | MEDLINE | ID: covidwho-2076949

ABSTRACT

Coronavirus disease 2019 (COVID-19) asymptomatic cases are hard to identify, impeding transmissibility estimation. The value of COVID-19 transmissibility is worth further elucidation for key assumptions in further modelling studies. Through a population-based surveillance network, we collected data on 1342 confirmed cases with a 90-days follow-up for all asymptomatic cases. An age-stratified compartmental model containing contact information was built to estimate the transmissibility of symptomatic and asymptomatic COVID-19 cases. The difference in transmissibility of a symptomatic and asymptomatic case depended on age and was most distinct for the middle-age groups. The asymptomatic cases had a 66.7% lower transmissibility rate than symptomatic cases, and 74.1% (95% CI 65.9-80.7) of all asymptomatic cases were missed in detection. The average proportion of asymptomatic cases was 28.2% (95% CI 23.0-34.6). Simulation demonstrated that the burden of asymptomatic transmission increased as the epidemic continued and could potentially dominate total transmission. The transmissibility of asymptomatic COVID-19 cases is high and asymptomatic COVID-19 cases play a significant role in outbreaks.


Subject(s)
COVID-19 , Epidemics , Humans , Middle Aged , Computer Simulation , COVID-19/epidemiology , COVID-19/transmission , Disease Outbreaks , SARS-CoV-2 , Asymptomatic Infections
3.
Sensors (Basel) ; 22(19)2022 Oct 08.
Article in English | MEDLINE | ID: covidwho-2066355

ABSTRACT

Methods to prevent collisions between people to avoid traffic accidents are receiving significant attention. To measure the position in the non-line-of-sight (NLOS) area, which cannot be directly visually recognized, position-measuring methods use wireless-communication-type GPS and propagation characteristics of radio signals, such as received signal strength indication (RSSI). However, conventional position estimation methods using RSSI require multiple receivers, which decreases the position estimation accuracy, owing to the presence of surrounding buildings. This study proposes a system to solve this challenge using a receiver and position estimation method based on RSSI MAP simulation and particle filter. Moreover, this study utilizes BLE peripheral/central functions capable of advertising as the transmitter/receiver. By using the advertising radio waves, our method provides a framework for estimating the position of unspecified transmitters. The effectiveness of the proposed system is evaluated in this study through simulations and experiments in actual environments. We obtained an error average of the distance to be 1.6 m from the simulations, which shows the precision of the proposed method. In the actual environment, the proposed method showed an error average of the distance to be 3.3 m. Furthermore, we evaluated the accuracy of the proposed method when both the transmitter and receiver are in motion, which can be considered as a moving person in the outdoor NLOS area. The result shows an error of 4.5 m. Consequently, we concluded that the accuracy was comparable when the transmitter is stationary and when it is moving. Compared with conventional path loss, the model can measure distances of 3 m to 10 m, whereas the proposed method can estimate the "position" with the same accuracy in an outdoor environment. In addition, it can be expected to be used as a collision avoidance system that confirms the presence of strangers in the NLOS area.


Subject(s)
Algorithms , Radio Waves , Computer Simulation , Humans
4.
JMIR Public Health Surveill ; 8(9): e37887, 2022 Sep 09.
Article in English | MEDLINE | ID: covidwho-2054773

ABSTRACT

BACKGROUND: Surveillance data are essential public health resources for guiding policy and allocation of human and capital resources. These data often consist of large collections of information based on nonrandom sample designs. Population estimates based on such data may be impacted by the underlying sample distribution compared to the true population of interest. In this study, we simulate a population of interest and allow response rates to vary in nonrandom ways to illustrate and measure the effect this has on population-based estimates of an important public health policy outcome. OBJECTIVE: The aim of this study was to illustrate the effect of nonrandom missingness on population-based survey sample estimation. METHODS: We simulated a population of respondents answering a survey question about their satisfaction with their community's policy regarding vaccination mandates for government personnel. We allowed response rates to differ between the generally satisfied and dissatisfied and considered the effect of common efforts to control for potential bias such as sampling weights, sample size inflation, and hypothesis tests for determining missingness at random. We compared these conditions via mean squared errors and sampling variability to characterize the bias in estimation arising under these different approaches. RESULTS: Sample estimates present clear and quantifiable bias, even in the most favorable response profile. On a 5-point Likert scale, nonrandom missingness resulted in errors averaging to almost a full point away from the truth. Efforts to mitigate bias through sample size inflation and sampling weights have negligible effects on the overall results. Additionally, hypothesis testing for departures from random missingness rarely detect the nonrandom missingness across the widest range of response profiles considered. CONCLUSIONS: Our results suggest that assuming surveillance data are missing at random during analysis could provide estimates that are widely different from what we might see in the whole population. Policy decisions based on such potentially biased estimates could be devastating in terms of community disengagement and health disparities. Alternative approaches to analysis that move away from broad generalization of a mismeasured population at risk are necessary to identify the marginalized groups, where overall response may be very different from those observed in measured respondents.


Subject(s)
Research Design , Bias , Computer Simulation , Humans , Surveys and Questionnaires
6.
Sci Rep ; 12(1): 16105, 2022 Sep 27.
Article in English | MEDLINE | ID: covidwho-2050524

ABSTRACT

In this paper, we propose a mathematical model to describe the influence of the SARS-CoV-2 virus with correlated sources of randomness and with vaccination. The total human population is divided into three groups susceptible, infected, and recovered. Each population group of the model is assumed to be subject to various types of randomness. We develop the correlated stochastic model by considering correlated Brownian motions for the population groups. As the environmental reservoir plays a weighty role in the transmission of the SARS-CoV-2 virus, our model encompasses a fourth stochastic differential equation representing the reservoir. Moreover, the vaccination of susceptible is also considered. Once the correlated stochastic model, the existence and uniqueness of a positive solution are discussed to show the problem's feasibility. The SARS-CoV-2 extinction, as well as persistency, are also examined, and sufficient conditions resulted from our investigation. The theoretical results are supported through numerical/graphical findings.


Subject(s)
COVID-19 , SARS-CoV-2 , COVID-19/epidemiology , COVID-19/prevention & control , Computer Simulation , Disease Susceptibility/epidemiology , Humans , Stochastic Processes , Vaccination
7.
J Plast Reconstr Aesthet Surg ; 75(11): 4013-4022, 2022 Nov.
Article in English | MEDLINE | ID: covidwho-2048956

ABSTRACT

BACKGROUND: Microsurgery is a technically demanding aspect of surgery that is integral to a variety of sub-specialties. Microsurgery is required in high-risk cases where time is limited and pressure is high, so there is increasing demand for skills acquisition beforehand. The aim of this review was to analyse the available literature on validated microsurgical assessment tools. METHODS: Covidence was used to screen papers for inclusion. Keywords included 'microsurgery', 'simulation', 'end-product assessment' and 'competence'. Inclusion criteria specified simulation models which demonstrate training and assessment of skill acquisition simultaneously. Tools which were used for training independently of technical assessment were excluded and so were tools which did not include a microvascular anastomosis. Each assessment tool was evaluated for validity, bias, complexity and fidelity and reliability using PRISMA and SWiM guidelines. RESULTS: Thirteen distinct tools were validated for use in microsurgical assessment. These can be divided into overall assessment and end-product assessment. Ten tools assessed the 'journey' of the operation, and three tools were specifically end-product assessments. All tools achieved construct validity. Criterion validity was only assessed for the UWOMSA1 and GRS.2 Interrater reliability was demonstrated for each tool except the ISSLA3 and SAMS.4 Four of the tools addressed demonstrate predictive validity.4-7 CONCLUSION: Thirteen assessment tools achieve variable validity for use in microsurgery. Interrater reliability is demonstrated for 11 of the 13 tools. The GRS and UWOMSA achieve intrarater reliability. The End Product Intimal Assessment tool and the Imperial College of Surgical Assessment device were valid tools for objective assessment of microsurgical skill.


Subject(s)
Clinical Competence , Microsurgery , Humans , Reproducibility of Results , Microsurgery/methods , Anastomosis, Surgical/education , Computer Simulation
8.
Theory Biosci ; 141(4): 365-374, 2022 Nov.
Article in English | MEDLINE | ID: covidwho-2048564

ABSTRACT

In this paper, a new mathematical model that describes the dynamics of the within-host COVID-19 epidemic is formulated. We show the stochastic dynamics of Target-Latent-Infected-Virus free within the human body with discrete delay and noise. Positivity and uniqueness of the solutions are established. Our study shows the extinction and persistence of the disease inside the human body through the stability analysis of the disease-free equilibrium [Formula: see text] and the endemic equilibrium [Formula: see text], respectively. Moreover, we show the impact of delay tactics and noise on the extinction of the disease. The most interesting result is even if the deterministic system is inevitably pandemic at a specific point, extinction will become possible in the stochastic version of our model.


Subject(s)
COVID-19 , Epidemics , Humans , Models, Biological , SARS-CoV-2 , Models, Theoretical , Stochastic Processes , Computer Simulation
9.
PLoS One ; 17(9): e0274781, 2022.
Article in English | MEDLINE | ID: covidwho-2039427

ABSTRACT

The beta distribution is routinely used to model variables that assume values in the standard unit interval, (0, 1). Several alternative laws have, nonetheless, been proposed in the literature, such as the Kumaraswamy and simplex distributions. A natural and empirically motivated question is: does the beta law provide an adequate representation for a given dataset? We test the null hypothesis that the beta model is correctly specified against the alternative hypothesis that it does not provide an adequate data fit. Our tests are based on the information matrix equality, which only holds when the model is correctly specified. They are thus sensitive to model misspecification. Simulation evidence shows that the tests perform well, especially when coupled with bootstrap resampling. We model state and county Covid-19 mortality rates in the United States. The misspecification tests indicate that the beta law successfully represents Covid-19 death rates when they are computed using either data from prior to the start of the vaccination campaign or data collected when such a campaign was under way. In the latter case, the beta law is only accepted when the negative impact of vaccination reach on death rates is moderate. The beta model is rejected under data heterogeneity, i.e., when mortality rates are computed using information gathered during both time periods.


Subject(s)
COVID-19 , COVID-19/epidemiology , Computer Simulation , Humans , Statistical Distributions , United States/epidemiology
10.
Bull Math Biol ; 84(11): 122, 2022 09 17.
Article in English | MEDLINE | ID: covidwho-2035260

ABSTRACT

A dynamic model called SqEAIIR for the COVID-19 epidemic is investigated with the effects of vaccination, quarantine and precaution promotion when the traveling and immigrating individuals are considered as unknown disturbances. By utilizing only daily sampling data of isolated symptomatic individuals collected by Mexican government agents, an equivalent model is established by an adaptive fuzzy-rules network with the proposed learning law to guarantee the convergence of the model's error. Thereafter, the optimal controller is developed to determine the adequate intervention policy. The main theorem is conducted to demonstrate the setting of all designed parameters regarding the closed-loop performance. The numerical systems validate the efficiency of the proposed scheme to control the epidemic and prevent the overflow of requiring healthcare facilities. Moreover, the sufficient performance of the proposed scheme is achieved with the effect of traveling and immigrating individuals.


Subject(s)
COVID-19 , Quarantine , Algorithms , COVID-19/epidemiology , COVID-19/prevention & control , Computer Simulation , Feedback , Humans , Mathematical Concepts , Models, Biological , Neural Networks, Computer , Nonlinear Dynamics , Policy
11.
Bull Math Biol ; 84(11): 127, 2022 09 22.
Article in English | MEDLINE | ID: covidwho-2035259

ABSTRACT

Mathematical modeling is a tool used for understanding diseases dynamics. The discrete-time model is an especial case in modeling that satisfactorily describes the epidemiological dynamics because of the discrete nature of the real data. However, discrete models reduce their descriptive and fitting potential because of assuming a homogeneous population. Thus, in this paper, we proposed contagion probability functions according to two infection paradigms that consider factors associated with transmission dynamics. For example, we introduced probabilities of establishing an infectious interaction, the number of contacts with infectious and the level of connectivity or social distance within populations. Through the probabilities design, we overcame the homogeneity assumption. Also, we evaluated the proposed probabilities through their introduction into discrete-time models for two diseases and different study zones with real data, COVID-19 for Germany and South Korea, and dengue for Colombia. Also, we described the oscillatory dynamics for the last one using the contagion probabilities alongside parameters with a biological sense. Finally, we highlight the implementation of the proposed probabilities would improve the simulation of the public policy effect of control strategies over an infectious disease outbreak.


Subject(s)
COVID-19 , Models, Biological , COVID-19/epidemiology , Computer Simulation , Humans , Likelihood Functions , Mathematical Concepts , Probability
12.
Sci Rep ; 12(1): 11314, 2022 07 04.
Article in English | MEDLINE | ID: covidwho-2028713

ABSTRACT

In the article, the authors present a multi-agent model that simulates the development of the COVID-19 pandemic at the regional level. The developed what-if system is a multi-agent generalization of the SEIR epidemiological model, which enables predicting the pandemic's course in various regions of Poland, taking into account Poland's spatial and demographic diversity, the residents' level of mobility, and, primarily, the level of restrictions imposed and the associated compliance. The developed simulation system considers detailed topographic data and the residents' professional and private lifestyles specific to the community. A numerical agent represents each resident in the system, thus providing a highly detailed model of social interactions and the pandemic's development. The developed model, made publicly available as free software, was tested in three representative regions of Poland. As the obtained results indicate, implementing social distancing and limiting mobility is crucial for impeding a pandemic before the development of an effective vaccine. It is also essential to consider a given community's social, demographic, and topographic specificity and apply measures appropriate for a given region.


Subject(s)
COVID-19 , Influenza, Human , COVID-19/epidemiology , Computer Simulation , Humans , Influenza, Human/epidemiology , Pandemics/prevention & control , Poland/epidemiology
13.
Sensors (Basel) ; 22(17)2022 Aug 26.
Article in English | MEDLINE | ID: covidwho-2024047

ABSTRACT

Automatic modulation classification (AMC) plays a fundamental role in common communication systems. Existing clustering models typically handle fewer modulation types with lower classification accuracies and more computational resources. This paper proposes a hierarchical self-organizing map (SOM) based on a feature space composed of high-order cumulants (HOC) and amplitude moment features. This SOM with two stacked layers can identify intrinsic differences among samples in the feature space without the need to set thresholds. This model can roughly cluster the multiple amplitude-shift keying (MASK), multiple phase-shift keying (MPSK), and multiple quadrature amplitude keying (MQAM) samples in the root layer and then finely distinguish the samples with different orders in the leaf layers. We creatively implement a discrete transformation method based on modified activation functions. This method causes MQAM samples to cluster in the leaf layer with more distinct boundaries between clusters and higher classification accuracies. The simulation results demonstrate the superior performance of the proposed hierarchical SOM on AMC problems when compared with other clustering models. Our proposed method can manage more categories of modulation signals and obtain higher classification accuracies while using fewer computational resources.


Subject(s)
Algorithms , Cluster Analysis , Computer Simulation
14.
Sensors (Basel) ; 22(16)2022 Aug 17.
Article in English | MEDLINE | ID: covidwho-2024043

ABSTRACT

In recent years, there has been a renewed interest in using virtual reality (VR) to (re)create different scenarios and environments with interactive and immersive experiences. Although VR has been popular in the tourism sector to reconfigure tourists' relationships with places and overcome mobility restrictions, its usage in senior cyclotourism has been understudied. VR is suggested to positively impact tourism promotion, cycling simulation, and active and healthy ageing due to physical and mental rehabilitation. The purpose of this study is to assess the senior citizens' perceived experience and attitudes toward a designed 360° VR cyclotouristic experiment, using a head-mounted display (HMD) setting within a laboratory context. A total of 76 participants aged between 50 and 97 years old were involved in convergent parallel mixed-method research, and data were collected using a questionnaire based on the technology acceptance model, as well as the researchers' field notes. Findings suggest that 360° VR with HMD can be an effective assistive technology to foster senior cyclotourism by promoting tourism sites, simulating the cycling pedaling effect, and improving senior citizens' general wellbeing and independence with physical and mental rehabilitation.


Subject(s)
Self-Help Devices , Smart Glasses , Virtual Reality , Aged , Aged, 80 and over , Computer Simulation , Humans , Middle Aged , Tourism
15.
Proc Natl Acad Sci U S A ; 119(37): e2203019119, 2022 09 13.
Article in English | MEDLINE | ID: covidwho-2017027

ABSTRACT

The global spread of coronavirus disease 2019 (COVID-19) has emphasized the need for evidence-based strategies for the safe operation of schools during pandemics that balance infection risk with the society's responsibility of allowing children to attend school. Due to limited empirical data, existing analyses assessing school-based interventions in pandemic situations often impose strong assumptions, for example, on the relationship between class size and transmission risk, which could bias the estimated effect of interventions, such as split classes and staggered attendance. To fill this gap in school outbreak studies, we parameterized an individual-based model that accounts for heterogeneous contact rates within and between classes and grades to a multischool outbreak data of influenza. We then simulated school outbreaks of respiratory infectious diseases of ongoing threat (i.e., COVID-19) and potential threat (i.e., pandemic influenza) under a variety of interventions (changing class structures, symptom screening, regular testing, cohorting, and responsive class closures). Our results suggest that interventions changing class structures (e.g., reduced class sizes) may not be effective in reducing the risk of major school outbreaks upon introduction of a case and that other precautionary measures (e.g., screening and isolation) need to be employed. Class-level closures in response to detection of a case were also suggested to be effective in reducing the size of an outbreak.


Subject(s)
Disease Outbreaks , Pandemics , Respiratory Tract Infections , Schools , COVID-19/prevention & control , COVID-19/transmission , Child , Computer Simulation , Disease Outbreaks/prevention & control , Humans , Influenza, Human/prevention & control , Influenza, Human/transmission , Pandemics/prevention & control , Respiratory Tract Infections/prevention & control , Respiratory Tract Infections/transmission
16.
Sci Rep ; 12(1): 13237, 2022 08 02.
Article in English | MEDLINE | ID: covidwho-2016819

ABSTRACT

The identification of novel drug-target interactions (DTI) is critical to drug discovery and drug repurposing to address contemporary medical and public health challenges presented by emergent diseases. Historically, computational methods have framed DTI prediction as a binary classification problem (indicating whether or not a drug physically interacts with a given protein target); however, framing the problem instead as a regression-based prediction of the physiochemical binding affinity is more meaningful. With growing databases of experimentally derived drug-target interactions (e.g. Davis, Binding-DB, and Kiba), deep learning-based DTI predictors can be effectively leveraged to achieve state-of-the-art (SOTA) performance. In this work, we formulated a DTI competition as part of the coursework for a senior undergraduate machine learning course and challenged students to generate component DTI models that might surpass SOTA models and effectively combine these component models as part of a meta-model using the Reciprocal Perspective (RP) multi-view learning framework. Following 6 weeks of concerted effort, 28 student-produced component deep-learning DTI models were leveraged in this work to produce a new SOTA RP-DTI model, denoted the Meta Undergraduate Student DTI (MUSDTI) model. Through a series of experiments we demonstrate that (1) RP can considerably improve SOTA DTI prediction, (2) our new double-cold experimental design is more appropriate for emergent DTI challenges, (3) that our novel MUSDTI meta-model outperforms SOTA models, (4) that RP can improve upon individual models as an ensembling method, and finally, (5) RP can be utilized for low computation transfer learning. This work introduces a number of important revelations for the field of DTI prediction and sequence-based, pairwise prediction in general.


Subject(s)
Drug Development , Drug Discovery , Computer Simulation , Drug Discovery/methods , Drug Interactions , Humans , Machine Learning
17.
PLoS Comput Biol ; 18(9): e1010463, 2022 09.
Article in English | MEDLINE | ID: covidwho-2009674

ABSTRACT

BACKGROUND: Based on the principles of equity and effectiveness, the World Health Organization and COVAX formulate vaccine allocation as a mathematical optimization problem. This study aims to solve the optimization problem using agent-based simulations. METHODS: We built open-sourced agent-based models to simulate virus transition among a demographically representative sample of 198 million people in 148 countries using advanced computational services. All countries continuing their current vaccine progress is defined as the baseline scenario. Comparison scenarios include achieving minimum vaccination rates and allocating vaccines based on pandemic levels. FINDINGS: The simulations are fitted using the pandemic data from 148 countries from January 2020 to June 2021. Under the baseline scenario, the world will add 24.36 million cases and 468,945 deaths during the projection period of three months. Inoculating at least 10%, 20%, and 26% of populations in all countries requires 1.12, 3.31, and 5.00 million additional vaccine doses every day, respectively. Achieving these benchmarks reduces new cases by 0.56, 2.74, and 3.32 million, respectively. If allocated by the current global distribution, 5.00 million additional vaccine doses will only avert 1.45 million new cases. If those 5.00 million vaccines are allocated based on projected cases in each country, the averted cases will increase more than six-fold to 9.20 million. Similar differences between allocation methods are observed in averted deaths. CONCLUSION: The global distribution of COVID-19 vaccines can be optimized to achieve better outcomes in terms of both equity and effectiveness. Alternative vaccine allocation methods may avert several times more cases and deaths than the current global distribution. With reasonable requirements on additional vaccines, COVAX could adopt alternative allocation strategies that reduce cross-country inequity and save more lives.


Subject(s)
COVID-19 Vaccines , COVID-19 , COVID-19/epidemiology , COVID-19/prevention & control , Computer Simulation , Global Health , Humans , Vaccination , World Health Organization
18.
Proc Natl Acad Sci U S A ; 119(37): e2205598119, 2022 09 13.
Article in English | MEDLINE | ID: covidwho-2008361

ABSTRACT

The humoral immune response, a key arm of adaptive immunity, consists of B cells and their products. Upon infection or vaccination, B cells undergo a Darwinian evolutionary process in germinal centers (GCs), resulting in the production of antibodies and memory B cells. We developed a computational model to study how humoral memory is recalled upon reinfection or booster vaccination. We find that upon reexposure to the same antigen, affinity-dependent selective expansion of available memory B cells outside GCs (extragerminal center compartments [EGCs]) results in a rapid response made up of the best available antibodies. Memory B cells that enter secondary GCs can undergo mutation and selection to generate even more potent responses over time, enabling greater protection upon subsequent exposure to the same antigen. GCs also generate a diverse pool of B cells, some with low antigen affinity. These results are consistent with our analyses of data from humans vaccinated with two doses of a COVID-19 vaccine. Our results further show that the diversity of memory B cells generated in GCs is critically important upon exposure to a variant antigen. Clones drawn from this diverse pool that cross-react with the variant are rapidly expanded in EGCs to provide the best protection possible while new secondary GCs generate a tailored response for the new variant. Based on a simple evolutionary model, we suggest that the complementary roles of EGC and GC processes we describe may have evolved in response to complex organisms being exposed to evolving pathogen families for millennia.


Subject(s)
Antigens , B-Lymphocytes , Immunity, Humoral , Immunologic Memory , Antigens/immunology , B-Lymphocytes/immunology , COVID-19/prevention & control , COVID-19 Vaccines/immunology , Computer Simulation , Germinal Center/immunology , Humans , Models, Biological
19.
Comput Methods Programs Biomed ; 225: 107094, 2022 Oct.
Article in English | MEDLINE | ID: covidwho-2007619

ABSTRACT

BACKGROUND AND OBJECTIVE: Pulmonary fibrosis (PF) is a chronic progressive disease with an extremely high mortality rate and is a complication of COVID-19. Inhalable microspheres have been increasingly used in the treatment of lung diseases such as PF in recent years. Compared to the direct inhalation of drugs, a larger particle size is required to ensure the sustained release of microspheres. However, the clinical symptoms of PF may lead to the easier deposition of microspheres in the upper respiratory tract. Therefore, it is necessary to understand the effects of PF on the deposition of microspheres in the respiratory tract. METHODS: In this study, airway models with different degrees of PF in humans and mice were established, and the transport and deposition of microspheres in the airway were simulated using computational fluid dynamics. RESULTS: The simulation results showed that PF increases microsphere deposition in the upper respiratory tract and decreases bronchial deposition in both humans and mice. Porous microspheres with low density can ensure deposition in the lower respiratory tract and larger particle size. In healthy and PF humans, porous microspheres of 10 µm with densities of 700 and 400 kg/m³ were deposited most in the bronchi. Unlike in humans, microspheres larger than 4 µm are completely deposited in the upper respiratory tract of mice owing to their high inhalation velocity. For healthy and PF mice, microspheres of 6 µm with densities of and 100 kg/m³ are recommended. CONCLUSIONS: The results showed that with the exacerbation of PF, it is more difficult for microsphere particles to deposit in the subsequent airway. In addition, there were significant differences in the deposition patterns among the different species. Therefore, it is necessary to process specific microspheres from different individuals. Our study can guide the processing of microspheres and achieve differentiated drug delivery in different subjects to maximize therapeutic effects.


Subject(s)
COVID-19 , Pulmonary Fibrosis , Animals , Computer Simulation , Delayed-Action Preparations , Humans , Lung , Mice , Microspheres , Models, Biological , Particle Size , Porosity , Pulmonary Fibrosis/drug therapy , Respiratory Aerosols and Droplets , Trachea
20.
Semin Respir Crit Care Med ; 43(3): 335-345, 2022 06.
Article in English | MEDLINE | ID: covidwho-2004821

ABSTRACT

Computer simulation offers a fresh approach to traditional medical research that is particularly well suited to investigating issues related to mechanical ventilation. Patients receiving mechanical ventilation are routinely monitored in great detail, providing extensive high-quality data-streams for model design and configuration. Models based on such data can incorporate very complex system dynamics that can be validated against patient responses for use as investigational surrogates. Crucially, simulation offers the potential to "look inside" the patient, allowing unimpeded access to all variables of interest. In contrast to trials on both animal models and human patients, in silico models are completely configurable and reproducible; for example, different ventilator settings can be applied to an identical virtual patient, or the same settings applied to different patients, to understand their mode of action and quantitatively compare their effectiveness. Here, we review progress on the mathematical modeling and computer simulation of human anatomy, physiology, and pathophysiology in the context of mechanical ventilation, with an emphasis on the clinical applications of this approach in various disease states. We present new results highlighting the link between model complexity and predictive capability, using data on the responses of individual patients with acute respiratory distress syndrome to changes in multiple ventilator settings. The current limitations and potential of in silico modeling are discussed from a clinical perspective, and future challenges and research directions highlighted.


Subject(s)
Respiration, Artificial , Respiratory Distress Syndrome , Computer Simulation , Humans , Respiration, Artificial/methods , Respiratory Distress Syndrome/therapy , Ventilators, Mechanical
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