Simulation in pediatric anesthesiology: current state and visions for the future.

PURPOSE OF REVIEW: Simulation is a well established practice in medicine. This review reflects upon the role of simulation in pediatric anesthesiology in three parts: training anesthesiologists to care for pediatric patients safely and effectively; evaluating and improving systems of care for children; and visions for the future. RECENT FINDINGS: Simulation continues to prove a useful modality to educate both novice and experienced clinicians in the perioperative care of infants and children. It is also a powerful tool to help analyze and improve upon how care is provided to infants and children. Advances in technology and computational power now allow for a greater than ever degree of innovation, accessibility, and focused reflection and debriefing, with an exciting outlook for promising advances in the near future. SUMMARY: Simulation plays a key role in developing and achieving peak performance in the perioperative care of infants and children. Although simulation already has a great impact, its full potential is yet to be harnessed.

Robust projection of East Asian summer monsoon rainfall based on dynamical modes of variability.

The Asian monsoon provides the freshwater that a large population in Asia depends on, but how anthropogenic climate warming may alter this key water source remains unclear. This is partly due to the prevailing point-wise assessment of climate projections, even though climate change patterns are inherently organized by dynamics intrinsic to the climate system. Here, we assess the future changes in the East Asian summer monsoon precipitation by projecting the precipitation from several large ensemble simulations and CMIP6 simulations onto the two leading dynamical modes of internal variability. The result shows a remarkable agreement among the ensembles on the increasing trends and the increasing daily variability in both dynamical modes, with the projection pattern emerging as early as the late 2030 s. The increase of the daily variability of the modes heralds more monsoon-related hydrological extremes over some identifiable East Asian regions in the coming decades.

Generative models of morphogenesis in developmental biology.

Understanding the mechanism by which cells coordinate their differentiation and migration is critical to our understanding of many fundamental processes such as wound healing, disease progression, and developmental biology. Mathematical models have been an essential tool for testing and developing our understanding, such as models of cells as soft spherical particles, reaction-diffusion systems that couple cell movement to environmental factors, and multi-scale multi-physics simulations that combine bottom-up rule-based models with continuum laws. However, mathematical models can often be loosely related to data or have so many parameters that model behaviour is weakly constrained. Recent methods in machine learning introduce new means by which models can be derived and deployed. In this review, we discuss examples of mathematical models of aspects of developmental biology, such as cell migration, and how these models can be combined with these recent machine learning methods.

Current and Future Applications of Virtual, Augmented, and Mixed Reality in Cardiothoracic Surgery.

BACKGROUND: This review aims to examine the existing literature to address currently used virtual, augmented, and mixed reality modalities in the areas of preoperative surgical planning, intraoperative guidance, and postoperative management in the field of cardiothoracic surgery. In addition this innovative technology provides future perspectives and potential benefits for cardiothoracic surgeons, trainees, and patients. METHODS: A targeted, nonsystematic literature assessment was performed within the Medline and Google Scholar databases to help identify current trends and to provide better understanding of the current state-of-the-art extended reality (XR) modalities in cardiothoracic surgery. Related articles published up to July 2020 were included in the review. RESULTS: XR is a novel technique gaining increasing application in cardiothoracic surgery. It provides a 3-dimensional and realistic view of structures and environments and offers the user the ability to interact with digital projections of surgical targets. Recent studies showed the validity and benefits of XR applications in cardiothoracic surgery. Examples include XR-guided preoperative planning, intraoperative guidance and navigation, postoperative pain and rehabilitation management, surgical simulation, and patient education. CONCLUSIONS: XR is gaining interest in the field of cardiothoracic surgery. In particular there are promising roles for XR applications in televirtuality, surgical planning, surgical simulation, and perioperative management. However future refinement and research are needed to further implement XR in the aforementioned settings within cardiothoracic surgery.

Adequate emergency department resource usage: Applying simulation-based workshop to improve teaching competence among elementary and junior high school teachers in Taiwan.

INTRODUCTION: More than 80% of patients who visited Emergency Department (ED) was not urgent in Taiwan in 2019. It causes insufficient medical services and a latent fiscal threat to the Nation Health Insurance (NHI). This study adopted simulation-based educating modules to explore the effect in teaching competence among primary and middle school teachers for efficient AEDRU (adequate emergency department resource usage) education in the future. METHOD: The subjects were 414 elementary and junior high school teachers in Taiwan. 214 participants attended the simulation-based workshop as the simulation-based group, whereas 200 participants took an online self-learning module as the self-learning group. The workshop was created by an expert panel for decreasing the unnecessary usage amount of ED medial resources. The materials are lecture, board games, miniature ED modules, and simulation-based scenarios. A teaching competence questionnaire including ED knowledge, teaching attitude, teaching skills, and teaching self-efficacy was conducted among participants before and after the intervention. Data were analyzed via McNemar, paired t test and the generalized estimating equations (GEE). RESULTS: The study showed that teachers who participated in the simulation-based workshop had improved more in teaching competence than those who received the online self-learning module. In addition, there were significant differences between the pre-test and post-test among the two groups in teaching competence. CONCLUSION: The simulation-based workshop is effective and it should be spread out. When students know how to use ED medical resources properly, they could affect their families. It can help the ED service to be used properly and benefits the finance of the NHI. The health care cost will be managed while also improving health.

Modeling Early Warning Systems: Construction and Validation of a Discrete Event Simulation Model for Heart Failure.

OBJECTIVES: Developing and validating a discrete event simulation model that is able to model patients with heart failure managed with usual care or an early warning system (with or without a diagnostic algorithm) and to account for the impact of individual patient characteristics in their health outcomes. METHODS: The model was developed using patient-level data from the Trans-European Network - Home-Care Management System study. It was coded using RStudio Version 1.3.1093 (version 3.6.2.) and validated along the lines of the Assessment of the Validation Status of Health-Economic decision models tool. The model includes 20 patient and disease characteristics and generates 8 different outcomes. Model outcomes were generated for the base-case analysis and used in the model validation. RESULTS: Patients managed with the early warning system, compared with usual care, experienced an average increase of 2.99 outpatient visits and a decrease of 0.02 hospitalizations per year, with a gain of 0.81 life years (0.45 quality-adjusted life years) and increased average total costs of €11 249. Adding a diagnostic algorithm to the early warning system resulted in a 0.92 life year gain (0.57 quality-adjusted life years) and increased average costs of €9680. These patients experienced a decrease of 0.02 outpatient visits and 0.65 hospitalizations per year, while they avoided being hospitalized 0.93 times. The model showed robustness and validity of generated outcomes when comparing them with other models addressing the same problem and with external data. CONCLUSIONS: This study developed and validated a unique patient-level simulation model that can be used for simulating a wide range of outcomes for different patient subgroups and treatment scenarios. It provides useful information for guiding research and for developing new treatment options by showing the hypothetical impact of these interventions on a large number of important heart failure outcomes.

VANESA: An open-source hybrid functional Petri net modeling and simulation environment in systems biology.

Petri nets are a common method for modeling and simulation of systems biology application cases. Usually different Petri net concepts (e.g. discrete, hybrid, functional) are demanded depending on the purpose of the application cases. Modeling complex application cases requires a unification of those concepts, e.g. hybrid functional Petri nets (HFPN) and extended hybrid Petri nets (xHPN). Existing tools have certain limitations which motivated the extension of VANESA, an existing open-source editor for biological networks. The extension can be used to model, simulate, and visualize Petri nets based on the xHPN formalism. Moreover, it comprises additional functionality to support and help the user. Complex (kinetic) functions are syntactically analyzed and mathematically rendered. Based on syntax and given physical unit information, modeling errors are revealed. The numerical simulation is seamlessly integrated and executed in the background by the open-source simulation environment OpenModelica utilizing the Modelica library PNlib. Visualization of simulation results for places, transitions, and arcs are useful to investigate and understand the model and its dynamic behavior. The impact of single parameters can be revealed by comparing multiple simulation results. Simulation results, charts, and entire specification of the Petri net model as Latex file can be exported. All these features are shown in the demonstration case. The utilized Petri net formalism xHPN is fully specified and implemented in PNlib. This assures transparency, reliability, and comprehensible simulation results. Thus, the combination of VANESA and OpenModelica shape a unique open-source Petri net environment focusing on systems biology application cases. VANESA is available at: http://agbi.techfak.uni-bielefeld.de/vanesa.

A Petri nets-based framework for whole-cell modeling.

Whole-cell modeling aims to incorporate all main genes and processes, and their interactions of a cell in one model. Whole-cell modeling has been regarded as the central aim of systems biology but also as a grand challenge, which plays essential roles in current and future systems biology. In this paper, we analyze whole-cell modeling challenges and requirements and classify them into three aspects (or dimensions): heterogeneous biochemical networks, uncertainties in components, and representation of cell structure. We then explore how to use different Petri net classes to address different aspects of whole-cell modeling requirements. Based on these analyses, we present a Petri nets-based framework for whole-cell modeling, which not only addresses many whole-cell modeling requirements, but also offers a graphical, modular, and hierarchical modeling tool. We think this framework can offer a feasible modeling approach for whole-cell model construction.

Model-Based Projection of Dementia Prevalence in China and Worldwide: 2020-2050.

BACKGROUND: Current and future incidence and prevalence estimates of dementia are essential for public health planning. OBJECTIVE: The objective was to establish prediction model of incidence and estimate the prevalence of dementia in the Chinese and worldwide population from 2020 to 2050. METHODS: A model-based method was used to project the dementia prevalence from 2020 to 2050 in China, which required incidence, the mortality rate for individual without dementia, and the relative risk of death. Furthermore, we detected the impact of intervention on the prevalence projection for dementia using a simulation method. We applied the same method to other projections worldwide. RESULTS: In 2020, the model predicted 16.25 million (95%confidence interval 11.55-21.18) persons with dementia in China. By 2050, this number would increase by approximately three-fold to 48.98 million (38.02-61.73). Through data simulation, if the incidence of dementia decreased by 10%every 10 years from 2020 after intervention and prevention, the number of dementia cases by 2050 was reduced by 11.96 million. This would reduce the economic burden by US $639.04 billion. In addition, using this model, dementia cases grew relatively slowly over the next few decades in the United States of America, the United Kingdom, and Japan, with percentage changes of 100.88%, 65.93%, and 16.20%, respectively. CONCLUSION: The number of people with dementia in China is large and will continue to increase rapidly. Effective interventions could reduce the number of patients drastically. Therefore, prevention and control strategies must be formulated urgently to reduce the occurrence of dementia.

Creative Approaches for Assessing Long-term Outcomes in Children.

Advances in new technologies, when incorporated into routine health screening, have tremendous promise to benefit children. The number of health screening tests, many of which have been developed with machine learning or genomics, has exploded. To assess efficacy of health screening, ideally, randomized trials of screening in youth would be conducted; however, these can take years to conduct and may not be feasible. Thus, innovative methods to evaluate the long-term outcomes of screening are needed to help clinicians and policymakers make informed decisions. These methods include using longitudinal and linked-data systems to evaluate screening in clinical and community settings, school data, simulation modeling approaches, and methods that take advantage of data available in the digital and genomic age. Future research is needed to evaluate how longitudinal and linked-data systems drawing on community and clinical settings can enable robust evaluations of the effects of screening on changes in health status. Additionally, future studies are needed to benchmark participating individuals and communities against similar counterparts and to link big data with natural experiments related to variation in screening policies. These novel approaches have great potential for identifying and addressing differences in access to screening and effectiveness of screening across population groups and communities.

Simulation for Foregut and Bariatric Surgery: Current Status and Future Directions.

Simulation offers the opportunity to practice in a safe, controlled, and standardized environment. Surgical simulation, in particular, is very attractive because it avoids learning and practicing surgical skills in the operating room. Many simulators are currently available such as box-lap trainers, virtual-reality platforms, cadavers, live animals, animal-based tissue blocks, and synthetic/artificial models. Endoscopic interventions can be practiced with high-fidelity virtual simulators. Box-lap trainers help practicing basic laparoscopic skills. Cadavers and live animals offer realism to train entire foregut and bariatric procedures. However, limited availability and high expenses often restrict their use. Ex vivo simulators with animal tissue blocks have been recently developed and appear to be a realistic and cost-effective alternative. Three-dimensional printing and real-time navigation systems have also emerged as promising training tools. Overall, further efforts are needed to develop a formal simulation curriculum with validated simulators for foregut and bariatric surgery.

In silico Nigellidine (N. sativa) bind to viral spike/active-sites of ACE1/2, AT1/2 to prevent COVID-19 induced vaso-tumult/vascular-damage/comorbidity.

COVID-19, a global-pandemic binds human-lung-ACE2. ACE2 causes vasodilatation. ACE2 works in balance with ACE1. The vaso-status maintains blood-pressure/vascular-health which is demolished in Covid-19 manifesting aldosterone/salt-deregulations/inflammations/endothelial-dysfunctions/hyper-hypo- tension, sepsis/hypovolemic-shock and vessel-thrombosis/coagulations. Here, nigellidine, an indazole-alkaloid was analyzed by molecular-docking for binding to different Angiotensin-binding-proteins (enzymes, ACE1(6en5)/ACE2(4aph)/receptors, AT1(6os1)/AT2(5xjm)) and COVID-19 spike-glycoprotein(6vsb). Nigellidine strongly binds to the spike-protein at the hinge-region/active-site-opening which may hamper proper-binding of nCoV2-ACE2 surface. Nigellidine effectively binds in the Angiotensin- II binding-site/entry-pocket (-7.54 kcal/mol, -211.76, Atomic-Contact-Energy; ACE-value) of ACE2 (Ki 8.68 and 8.3 µmol) in comparison to known-binder EGCG (-4.53) and Theaflavin-di-gallate (-2.85). Nigellidine showed strong-binding (Ki, 50.93 µmol/binding-energy -5.48 kcal/mol) to mono/multi-meric ACE1. Moreover, it binds Angiotensin-receptors, AT1/AT2 (Ki, 42.79/14.22 µmol, binding-energy, -5.96/-6.61 kcal/mol) at active-sites, respectively. This article reports the novel binding of nigellidine and subsequent blockage of angiotensin-binding proteins. The ACEs-blocking could restore Angiotensin-level, restrict vaso-turbulence in Covid patients and receptor-blocking might stop inflammatory/vascular impairment. Nigellidine may slowdown the vaso-fluctuations due to Angiotensin-deregulations in Covid patients. Angiotensin II-ACE2 binding (ACE-value -294.81) is more favorable than nigellidine-ACE2. Conversely, nigellidine-ACE1 binding-energy/Ki is lower than nigellidine-ACE2 values indicating a balanced-state between constriction-dilatation. Moreover, nigellidine binds to the viral-spike, closer-proximity to its ACE2 binding-domain. Taken together, Covid patients/elderly-patients, comorbid-patients (with hypertensive/diabetic/cardiac/renal-impairment, counting >80% of non-survivors) could be greatly benefited.

Simulation in Hernia Surgery: Where Do We Stand?

Simulation seems to be the best method of improving medical attitude, technical skills, and operating times. A literature review of the available data in simulation for hernia surgery was performed. Surgical simulation has been included as a main requirement in residency programs and endorsed by several surgical societies. However, evaluating how simulation affects patient's outcomes is challenging. In addition, simulation training represents an institutional economic burden that could undermine its implementation and development. Published data support that simulation-based training is a highly efficient tool, thus, its implementation should be strongly encouraged.

Enhancing the future of simulation-based education in pediatrics.

Technology-enhanced simulation has emerged as a great educational tool for pediatric education. Indeed, it represents an effective method to instruct on technical and non-technical skills, employed by a large number of pediatric training programs. However, this unique pandemic era posed new challenges also on simulation-based education. Beyond the mere facing of the clinical and societal impacts, it is fundamental to take advantage from the current changes and investigate innovative approaches to improve the education of pediatric healthcare professionals. To this aim, we herein lay down the main pillars that should support the infrastructure of the future technology-enhanced simulation.

How to teach neural networks to mesh: Application on 2-D simplicial contours.

A machine learning meshing scheme for the generation of 2-D simplicial meshes is proposed based on the predictions of neural networks. The data extracted from meshed contours are utilized to train neural networks which are used to approximate the number of vertices to be inserted inside the contour cavity, their location, and connectivity. The accuracy of the scheme is evaluated by comparing the quality of the mesh generated by the neural networks with that generated by a reference mesher. Based on an element quality metric, after conducting tests on contours for a various number of edges, the results show a maximum average deviation of 15.2% on the mean quality and 27.3% on the minimum quality between the elements of the meshes generated by the scheme and the ones generated from the reference mesher; the scheme is able to produce good quality meshes that are suitable for meshing purposes. The meshing scheme is also applied to generate larger scale meshes with a recursive implementation. The findings encourage the adaption of the scheme for 3-D mesh generation.

Developing a Clinical Prediction Score: Comparing Prediction Accuracy of Integer Scores to Statistical Regression Models.

Researchers often convert prediction tools built on statistical regression models into integer scores and risk classification systems in the name of simplicity. However, this workflow discards useful information and reduces prediction accuracy. We, therefore, investigated the impact on prediction accuracy when researchers simplify a regression model into an integer score using a simulation study and an example clinical data set. Simulated independent training and test sets (n = 1000) were randomly generated such that a logistic regression model would perform at a specified target area under the receiver operating characteristic curve (AUC) of 0.7, 0.8, or 0.9. After fitting a logistic regression with continuous covariates to each data set, continuous variables were dichotomized using data-dependent cut points. A logistic regression was refit, and the coefficients were scaled and rounded to create an integer score. A risk classification system was built by stratifying integer scores into low-, intermediate-, and high-risk tertiles. Discrimination and calibration were assessed by calculating the AUC and index of prediction accuracy (IPA) for each model. The optimism in performance between the training set and test set was calculated for both AUC and IPA. The logistic regression model using the continuous form of covariates outperformed all other models. In the simulation study, converting the logistic regression model to an integer score and subsequent risk classification system incurred an average decrease of 0.057-0.094 in AUC, and an absolute 6.2%-17.5% in IPA. The largest decrease in both AUC and IPA occurred in the dichotomization step. The dichotomization and risk stratification steps also increased the optimism of the resulting models, such that they appeared to be able to predict better than they actually would on new data. In the clinical data set, converting the logistic regression with continuous covariates to an integer score incurred a decrease in externally validated AUC of 0.06 and a decrease in externally validated IPA of 13%. Converting a regression model to an integer score decreases model performance considerably. Therefore, we recommend developing a regression model that incorporates all available information to make the most accurate predictions possible, and using the unaltered regression model when making predictions for individual patients. In all cases, researchers should be mindful that they correctly validate the specific model that is intended for clinical use.

In silico T cell epitope identification for SARS-CoV-2: Progress and perspectives.

Growing evidence suggests that T cells may play a critical role in combating severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Hence, COVID-19 vaccines that can elicit a robust T cell response may be particularly important. The design, development and experimental evaluation of such vaccines is aided by an understanding of the landscape of T cell epitopes of SARS-CoV-2, which is largely unknown. Due to the challenges of identifying epitopes experimentally, many studies have proposed the use of in silico methods. Here, we present a review of the in silico methods that have been used for the prediction of SARS-CoV-2 T cell epitopes. These methods employ a diverse set of technical approaches, often rooted in machine learning. A performance comparison is provided based on the ability to identify a specific set of immunogenic epitopes that have been determined experimentally to be targeted by T cells in convalescent COVID-19 patients, shedding light on the relative performance merits of the different approaches adopted by the in silico studies. The review also puts forward perspectives for future research directions.

Physiological-based pharmacokinetic modeling trends in pharmaceutical drug development over the last 20-years; in-depth analysis of applications, organizations, and platforms.

We assess the advancement of physiologically based pharmacokinetic (PBPK) modeling and simulation (M&S) over the last 20 years (start of 2000 to end of 2019) focusing on the trends in each decade with the relative contributions from different organizations, areas of applications, and software tools used. Unlike many of the previous publications which focused on regulatory applications, our analysis is based on PBPK publications in peer-reviewed journals based on a large sample (>700 original articles). We estimated a rate of growth for PBPK (>40 fold/20 years) that was much steeper than the general pharmacokinetic modeling (<3 fold/20 years) or overall scientific publications (∼3 fold/20 years). The analyses demonstrated that contrary to commonly held belief, commercial specialized PBPK platforms with graphical-user interface were a much more popular choice than open-source alternatives even within academic organizations. These platforms constituted 81% of the whole set of the sample we assessed. The major PBPK applications (top 3) were associated with the study design, predicting formulation effects, and metabolic drug-drug interactions, while studying the fate of drugs in special populations, predicting kinetics in early drug development, and investigating transporter drug interactions have increased proportionally over the last decade. The proportions of application areas based on published research were distinctively different from those shown previously for the regulatory submissions and impact on labels. This may demonstrate the lag time between the research applications versus verified usage within the regulatory framework. The report showed the trend of overall PBPK publications in pharmacology drug development from the past 2 decades stratified by the organizations involved, software used, and area of applications. The analysis showed a more rapid increase in PBPK than that of the pharmacokinetic space itself with an equal contribution from academia and industry. By establishing and recording the journey of PBPK modeling in the past and looking at its current status, the analysis can be used for devising plans based on the anticipated trajectory of future regulatory applications.

The impact of surgical simulation on patient outcomes: a systematic review and meta-analysis.

The use of simulation in surgical training is ever growing. Evidence suggests such training may have beneficial clinically relevant effects. The objective of this research is to investigate the effects of surgical simulation training on clinically relevant patient outcomes by evaluating randomized controlled trials (RCT). PubMed was searched using PRISMA guidelines: "surgery" [All Fields] AND "simulation" [All Fields] AND "patient outcome" [All Fields]. Of 119 papers identified, 100 were excluded for various reasons. Meta-analyses were conducted using the inverse-variance random-effects method. Nineteen papers were reviewed using the CASP RCT Checklist. Sixteen studies looked at surgical training, two studies assessed patient-specific simulator practice, and one paper focused on warming-up on a simulator before performing surgery. Median study population size was 22 (range 3-73). Most articles reported outcome measures such as post-intervention Global Rating Scale (GRS) score and/or operative time. On average, the intervention group scored 0.42 (95% confidence interval 0.12 to 0.71, P = 0.005) points higher on a standardized GRS scale of 1-10. On average, the intervention group was 44% (1% to 87%, P = 0.04) faster than the control group. Four papers assessed the impact of simulation training on patient outcomes, with only one finding a significant effect. We found a significant effect of simulation training on operative performance as assessed by GRS, albeit a small one, as well as a significant reduction to operative time. However, there is to date scant evidence from RCTs to suggest a significant effect of surgical simulation training on patient outcomes.