Biosimulation of acute phonotrauma: an extended model

Pre- and Postoperative Voice Therapy for Benign Vocal Fold Lesions: An International Electronic Delphi Consensus Study

INTRODUCTION

Voice therapy management of benign vocal fold lesions (BVFLs) is variable and there are currently no clinical guidelines. Poor descriptions of voice therapy interventions lead to unwarranted variation in treatment. Triangulation of the current evidence identifies a number of potential best practice elements, but also a number of outstanding questions to be explored. The aim of this study was to refine and gain global consensus on "best practice" for a pre- and postoperative voice therapy intervention for adults with BVFLs.

METHODS

An international sample of expert voice therapists (n = 42) were recruited to take part in this three-round electronic modified Delphi study. Participants were presented with statements concerning a pre- and postoperative voice therapy intervention. Statements were developed from previous research and based on the TIDieR checklist (eg, why, when, what, how?) Participants rated the extent to which they agreed or disagreed with a statement and gave comments to support their response. Consensus was defined as >75% of participants agreeing or strongly agreeing with a given statement. If consensus was not reached, participant comments were used to generate new statements and were rated in the next round. Stability of consensus between rounds was assessed.

RESULTS

The 42 international experts achieved consensus on 33 statements relating to components of a best practice pre- and postoperative voice therapy intervention for patients with BVFLs. Consensus on statements ranged from 81% to 100%. These statements were explicitly mapped to the TIDieR checklist to ensure that all aspects of the intervention were considered and the questions of "why, what, how, when and individual tailoring" were addressed.

CONCLUSIONS

This study has significantly enhanced our understanding of what should be in a best practice pre- and postoperative voice therapy intervention. It is important to now test these findings for acceptability and feasibility, prior to considering effectiveness research.

H2O2 Concentration in Exhaled Breath Condensate Increases After Phonotrauma: A Promise of Noninvasive Monitoring?

PURPOSE

The present study was designed to observe the concentration of hydrogen peroxide (H2O2) in exhaled breath condensate (EBC) after induced phonotrauma.

METHODS

Thirty-five participants were randomly assigned to one of two conditions (1) Vocal demand and (2) Control. Participants in the experimental group (vocal demand) were asked to read aloud some texts during 1 hour, at 85-90 dB. Inflammation (H2O2 from exhaled breath condensate), acoustic, aerodynamic, and subjective measures were obtained at four time points: before vocal demand (baseline), immediately after baseline, 4-hour after baseline, and 24 hours after baseline. The same acquisition process was implemented for subjects in control group, except that they were not asked to engage in any vocal demand tasks at all.

RESULTS

As for biological samples, a significant effect for group was observed. Higher values were found for participants in experimental condition. Significant differences were observed for within contrasts in the experimental group, namely 4 hours against baseline, 4 hours against immediately post, and 24 hours against 4 hours. Instrumental outcomes did not show significant differences across the different conditions at any time points. Self-reported measures (vocal fatigue and sensation of muscle tension) showed a significant main effect for group and main effect for condition.

CONCLUSIONS

Intense vocal demand causes an increase in the concentration of H2O2 obtained from EBC at four hours after baseline, which is compatible with the generation of an inflammatory process in the vocal folds (phonotrauma). Moreover, the increase in the sensation of vocal fatigue and muscle tension after demand tasks seems to be an immediate reaction that did not match in time with the increment of H2O2 concentration.

Agent-based models of inflammation in translational systems biology: A decade later

Agent-based modeling is a rule-based, discrete-event, and spatially explicit computational modeling method that employs computational objects that instantiate the rules and interactions among the individual components ("agents") of system. Agent-based modeling is well suited to translating into a computational model the knowledge generated from basic science research, particularly with respect to translating across scales the mechanisms of cellular behavior into aggregated cell population dynamics manifesting at the tissue and organ level. This capacity has made agent-based modeling an integral method in translational systems biology (TSB), an approach that uses multiscale dynamic computational modeling to explicitly represent disease processes in a clinically relevant fashion. The initial work in the early 2000s using agent-based models (ABMs) in TSB focused on examining acute inflammation and its intersection with wound healing; the decade since has seen vast growth in both the application of agent-based modeling to a wide array of disease processes as well as methodological advancements in the use and analysis of ABM. This report presents an update on an earlier review of ABMs in TSB and presents examples of exciting progress in the modeling of various organs and diseases that involve inflammation. This review also describes developments that integrate the use of ABMs with cutting-edge technologies such as high-performance computing, machine learning, and artificial intelligence, with a view toward the future integration of these methodologies. This article is categorized under: Translational, Genomic, and Systems Medicine > Translational Medicine Models of Systems Properties and Processes > Mechanistic Models Models of Systems Properties and Processes > Organ, Tissue, and Physiological Models Models of Systems Properties and Processes > Organismal Models.

Towards a Physiological Scale of Vocal Fold Agent-Based Models of Surgical Injury and Repair: Sensitivity Analysis, Calibration and Verification

Agent based models (ABM) were developed to numerically simulate the biological response to surgical vocal fold injury and repair at the physiological level. This study aimed to improve the representation of existing ABM through a combination of empirical and computational experiments. Empirical data of vocal fold cell populations including neutrophils, macrophages and fibroblasts were obtained using flow cytometry up to four weeks following surgical injury. Random Forests were used as a sensitivity analysis method to identify model parameters that were most influential to ABM outputs. Statistical Parameter Optimization Tool for Python was used to calibrate those parameter values to match the ABM-simulation data with the corresponding empirical data from Day 1 to Day 5 following surgery. Model performance was evaluated by verifying if the empirical data fell within the 95% confidence intervals of ABM outputs of cell quantities at Day 7, Week 2 and Week 4. For Day 7, all empirical data were within the ABM output ranges. The trends of ABM-simulated cell populations were also qualitatively comparable to those of the empirical data beyond Day 7. Exact values, however, fell outside of the 95% statistical confidence intervals. Parameters related to fibroblast proliferation were indicative to the ABM-simulation of fibroblast dynamics in final stages of wound healing.

High-Performance Agent-Based Modeling Applied to Vocal Fold Inflammation and Repair

Fast and accurate computational biology models offer the prospect of accelerating the development of personalized medicine. A tool capable of estimating treatment success can help prevent unnecessary and costly treatments and potential harmful side effects. A novel high-performance Agent-Based Model (ABM) was adopted to simulate and visualize multi-scale complex biological processes arising in vocal fold inflammation and repair. The computational scheme was designed to organize the 3D ABM sub-tasks to fully utilize the resources available on current heterogeneous platforms consisting of multi-core CPUs and many-core GPUs. Subtasks are further parallelized and convolution-based diffusion is used to enhance the performance of the ABM simulation. The scheme was implemented using a client-server protocol allowing the results of each iteration to be analyzed and visualized on the server (i.e., in-situ) while the simulation is running on the same server. The resulting simulation and visualization software enables users to interact with and steer the course of the simulation in real-time as needed. This high-resolution 3D ABM framework was used for a case study of surgical vocal fold injury and repair. The new framework is capable of completing the simulation, visualization and remote result delivery in under 7 s per iteration, where each iteration of the simulation represents 30 min in the real world. The case study model was simulated at the physiological scale of a human vocal fold. This simulation tracks 17 million biological cells as well as a total of 1.7 billion signaling chemical and structural protein data points. The visualization component processes and renders all simulated biological cells and 154 million signaling chemical data points. The proposed high-performance 3D ABM was verified through comparisons with empirical vocal fold data. Representative trends of biomarker predictions in surgically injured vocal folds were observed.

In Situ Visualization for 3D Agent-Based Vocal Fold Inflammation and Repair Simulation

A fast and insightful visualization is essential in modeling biological system behaviors and understanding underlying inter-cellular mechanisms. High fidelity models produce billions of data points per time step, making in situ visualization techniques extremely desirable as they mitigate I/O bottlenecks and provide computational steering capability. In this work, we present a novel high-performance scheme to couple in situ visualization with the simulation of the vocal fold inflammation and repair using little to no extra cost in execution time or computing resources. The visualization component is first optimized with an adaptive sampling scheme to accelerate the rendering process while maintaining the precision of the displayed visual results. Our software employs VirtualGL to perform visualization in situ. The scheme overlaps visualization and simulation, resulting in the optimal utilization of computing resources. This results in an in situ system biology simulation suite capable of remote simulation of 17 million biological cells and 1.2 billion chemical data points, remote visualization of the results, and delivery of visualized frames with aggregated statistics to remote clients in real-time.

Real-Time Agent-Based Modeling Simulation with in-situ Visualization of Complex Biological Systems: A Case Study on Vocal Fold Inflammation and Healing

We present an efficient and scalable scheme for implementing agent-based modeling (ABM) simulation with In Situ visualization of large complex systems on heterogeneous computing platforms. The scheme is designed to make optimal use of the resources available on a heterogeneous platform consisting of a multicore CPU and a GPU, resulting in minimal to no resource idle time. Furthermore, the scheme was implemented under a client-server paradigm that enables remote users to visualize and analyze simulation data as it is being generated at each time step of the model. Performance of a simulation case study of vocal fold inflammation and wound healing with 3.8 million agents shows 35× and 7× speedup in execution time over single-core and multi-core CPU respectively. Each iteration of the model took less than 200 ms to simulate, visualize and send the results to the client. This enables users to monitor the simulation in real-time and modify its course as needed.

Augmenting Surgery via Multi-scale Modeling and Translational Systems Biology in the Era of Precision Medicine: A Multidisciplinary Perspective

In this era of tremendous technological capabilities and increased focus on improving clinical outcomes, decreasing costs, and increasing precision, there is a need for a more quantitative approach to the field of surgery. Multiscale computational modeling has the potential to bridge the gap to the emerging paradigms of Precision Medicine and Translational Systems Biology, in which quantitative metrics and data guide patient care through improved stratification, diagnosis, and therapy. Achievements by multiple groups have demonstrated the potential for (1) multiscale computational modeling, at a biological level, of diseases treated with surgery and the surgical procedure process at the level of the individual and the population; along with (2) patient-specific, computationally-enabled surgical planning, delivery, and guidance and robotically-augmented manipulation. In this perspective article, we discuss these concepts, and cite emerging examples from the fields of trauma, wound healing, and cardiac surgery.

Trauma in silico: Individual-specific mathematical models and virtual clinical populations

Trauma-induced critical illness is driven by acute inflammation, and elevated systemic interleukin-6 (IL-6) after trauma is a biomarker of adverse outcomes. We constructed a multicompartment, ordinary differential equation model that represents a virtual trauma patient. Individual-specific variants of this model reproduced both systemic inflammation and outcomes of 33 blunt trauma survivors, from which a cohort of 10,000 virtual trauma patients was generated. Model-predicted length of stay in the intensive care unit, degree of multiple organ dysfunction, and IL-6 area under the curve as a function of injury severity were in concordance with the results from a validation cohort of 147 blunt trauma patients. In a subcohort of 98 trauma patients, those with high-IL-6 single-nucleotide polymorphisms (SNPs) exhibited higher plasma IL-6 levels than those with low IL-6 SNPs, matching model predictions. Although IL-6 could drive mortality in individual virtual patients, simulated outcomes in the overall cohort were independent of the propensity to produce IL-6, a prediction verified in the 98-patient subcohort. In silico randomized clinical trials suggested a small survival benefit of IL-6 inhibition, little benefit of IL-1β inhibition, and worse survival after tumor necrosis factor-α inhibition. This study demonstrates the limitations of extrapolating from reductionist mechanisms to outcomes in individuals and populations and demonstrates the use of mechanistic simulation in complex diseases.

A comparative histopathological study of vocal fold polyps in smokers versus non-smokers

BACKGROUND

A large proportion of patients with vocal fold polyps are cigarette smokers. However, prior to this report no comparative study of polyp histopathology in smokers versus non-smokers had been performed.

METHODS

A prospective histopathological study of vocal fold polyps excised from 29 patients was undertaken. This comprised a comparative analysis of polyp histopathology in smokers versus non-smokers and a review of the pertinent literature.

RESULTS

Vocal fold polyps were larger in smokers than in non-smokers. Histopathological features significantly associated with the polyps of smokers versus those of non-smokers were increased keratinisation, dysplasia, a basement membrane thinning and hyaline degeneration.

CONCLUSION

Cigarette smoke has an injurious effect on vocal fold polyp epithelium and leads to increased hyaline degeneration in polyps.

Computational modelling of the inflammatory response in trauma, sepsis and wound healing: implications for modelling resilience

Resilience refers to the ability to recover from illness or adversity. At the cell, tissue, organ and whole-organism levels, the response to perturbations such as infections and injury involves the acute inflammatory response, which in turn is connected to and controlled by changes in physiology across all organ systems. When coordinated properly, inflammation can lead to the clearance of infection and healing of damaged tissues. However, when either overly or insufficiently robust, inflammation can drive further cell stress, tissue damage, organ dysfunction and death through a feed-forward process of inflammation → damage → inflammation. To address this complexity, we have obtained extensive datasets regarding the dynamics of inflammation in cells, animals and patients, and created data-driven and mechanistic computational simulations of inflammation and its recursive effects on tissue, organ and whole-organism (patho)physiology. Through this approach, we have discerned key regulatory mechanisms, recapitulated in silico key features of clinical trials for acute inflammation and captured diverse, patient-specific outcomes. These insights may allow for the determination of individual-specific tolerances to illness and adversity, thereby defining the role of inflammation in resilience.

Computational Modeling of Inflammation and Wound Healing

OBJECTIVE

Inflammation is both central to proper wound healing and a key driver of chronic tissue injury via a positive-feedback loop incited by incidental cell damage. We seek to derive actionable insights into the role of inflammation in wound healing in order to improve outcomes for individual patients.

APPROACH

To date, dynamic computational models have been used to study the time evolution of inflammation in wound healing. Emerging clinical data on histo-pathological and macroscopic images of evolving wounds, as well as noninvasive measures of blood flow, suggested the need for tissue-realistic, agent-based, and hybrid mechanistic computational simulations of inflammation and wound healing.

INNOVATION

We developed a computational modeling system, Simple Platform for Agent-based Representation of Knowledge, to facilitate the construction of tissue-realistic models.

RESULTS

A hybrid equation-agent-based model (ABM) of pressure ulcer formation in both spinal cord-injured and -uninjured patients was used to identify control points that reduce stress caused by tissue ischemia/reperfusion. An ABM of arterial restenosis revealed new dynamics of cell migration during neointimal hyperplasia that match histological features, but contradict the currently prevailing mechanistic hypothesis. ABMs of vocal fold inflammation were used to predict inflammatory trajectories in individuals, possibly allowing for personalized treatment.

CONCLUSIONS

The intertwined inflammatory and wound healing responses can be modeled computationally to make predictions in individuals, simulate therapies, and gain mechanistic insights.

Vocal exercise may attenuate acute vocal fold inflammation

OBJECTIVES/HYPOTHESES

The objective was to assess the utility of selected "resonant voice" (RV) exercises for the reduction of acute vocal fold inflammation. The hypothesis was that relatively large-amplitude, low-impact vocal fold exercises associated with RV would reduce inflammation more than spontaneous speech (SS) and possibly more than voice rest.

STUDY DESIGN

The study design was prospective, randomized, and double blind.

METHODS

Nine vocally healthy adults underwent a 1-hour vocal loading procedure, followed by randomization to a SS condition, vocal rest condition, or RV exercise condition. Treatments were monitored in clinic for 4 hours and continued extraclinically until the next morning. At baseline (BL), immediately after loading, after the 4-hour in-clinic treatment, and 24 hours post-BL, secretions were suctioned from the vocal folds bilaterally and submitted to enzyme-linked immunosorbent assay to estimate concentrations of key markers of tissue injury and inflammation: interleukin (IL)-1β, IL-6, IL-8, tumor necrosis factor α, matrix metalloproteinase (MMP)-8, and IL-10.

RESULTS

Complete data sets were obtained for three markers--IL-1β, IL-6, and MMP-8--for one subject in each treatment condition. For these markers, results were poorest at 24-hour follow-up in the SS condition, sharply improved in the voice rest condition, and was the best in the RV condition. Average results for all markers and responsive subjects with normal BL mediator concentrations revealed an almost identical pattern.

CONCLUSIONS

Some forms of tissue mobilization may be useful to attenuate acute vocal fold inflammation.

Current Understanding and Future Directions for Vocal Fold Mechanobiology

The vocal folds, which are located in the larynx, are the main organ of voice production for human communication. The vocal folds are under continuous biomechanical stress similar to other mechanically active organs, such as the heart, lungs, tendons and muscles. During speech and singing, the vocal folds oscillate at frequencies ranging from 20 Hz to 3 kHz with amplitudes of a few millimeters. The biomechanical stress associated with accumulated phonation is believed to alter vocal fold cell activity and tissue structure in many ways. Excessive phonatory stress can damage tissue structure and induce a cell-mediated inflammatory response, resulting in a pathological vocal fold lesion. On the other hand, phonatory stress is one major factor in the maturation of the vocal folds into a specialized tri-layer structure. One specific form of vocal fold oscillation, which involves low impact and large amplitude excursion, is prescribed therapeutically for patients with mild vocal fold injuries. Although biomechanical forces affect vocal fold physiology and pathology, there is little understanding of how mechanical forces regulate these processes at the cellular and molecular level. Research into vocal fold mechanobiology has burgeoned over the past several years. Vocal fold bioreactors are being developed in several laboratories to provide a biomimic environment that allows the systematic manipulation of physical and biological factors on the cells of interest in vitro. Computer models have been used to simulate the integrated response of cells and proteins as a function of phonation stress. The purpose of this paper is to review current research on the mechanobiology of the vocal folds as it relates to growth, pathogenesis and treatment as well as to propose specific research directions that will advance our understanding of this subject.

Translational applications of evaluating physiologic variability in human endotoxemia

Dysregulation of the inflammatory response is a critical component of many clinically challenging disorders such as sepsis. Inflammation is a biological process designed to lead to healing and recovery, ultimately restoring homeostasis; however, the failure to fully achieve those beneficial results can leave a patient in a dangerous persistent inflammatory state. One of the primary challenges in developing novel therapies in this area is that inflammation is comprised of a complex network of interacting pathways. Here, we discuss our approaches towards addressing this problem through computational systems biology, with a particular focus on how the presence of biological rhythms and the disruption of these rhythms in inflammation may be applied in a translational context. By leveraging the information content embedded in physiologic variability, ranging in scale from oscillations in autonomic activity driving short-term heart rate variability to circadian rhythms in immunomodulatory hormones, there is significant potential to gain insight into the underlying physiology.