204 research outputs found
The Burn Agent-Based Model (BABM): Developing a Computational Agent-based Model of the Burn Wound Healing Process to Examine Fibrosis and Re-epithelialization After a Partial-thickness Burn Injury
No full textIntroduction: Partial-thickness burn injuries, depending on depth, are distinct from deeper burns in that they can preserve the hair follicle bulb as a source of epithelial stem cells and a modulator of mechanical forces and immune cells. This changes the dynamics of wound healing and is an important consideration for therapeutic decisions. Regardless, patients require appropriate interventions to facilitate proper restoration of the skin to prevent complications like wound infection or sepsis that can occur as sequelae of the disrupted epithelial barrier and systemic inflammatory cascades. Herein, we developed a computational agent-based model of the partial-thickness burn wound environment, the Burn Agent-based Model (BABM), in order to better characterize the complex systems of wound healing and facilitate investigation of novel therapeutic strategies. Methods: The model was developed using NetLogo and simulates a two-dimensional cross-section of epidermis and dermis containing multiple hair follicles. It includes multiple cell types present in those layers, such as keratinocytes, epithelial stem cells, neutrophils, macrophages, and fibroblasts, as well as key cytokines and chemokines. Burn injuries were implemented as a mechanical “shearing” of the entire epidermis and the superficial portion of the dermis with damage to the immediately deeper tissue. The model encompasses the first three weeks of the post-burn period, including hemostasis and coagulation, inflammation, proliferation, and re-epithelialization. Results: We successfully created an ABM of the partial-thickness burn wound healing process at the cellular and cytokinetic level. The model implements the mitotic function of epithelial stem cells particularly within hair follicle bulbs, paracrine influences of injured keratinocytes, phagocytic and inflammatory actions of neutrophils and macrophages, migration and extra-cellular matrix deposition by fibroblasts, and the vascular dynamics of dermal capillaries representing the zones of coagulation, stasis, and hyperemia. In addition, the model incorporates multiple cytokines, notably IL1, IL6, TNFa, TGFb, EGF, FGF, PDGF, and VEGF. The model was calibrated to closely follow the cytokine profiles obtained from cytokine multiplex assays of patients hospitalized for burn injuries. Conclusion: The BABM provides a powerful in-silico framework to study burn wound healing by simulating a small segment of injured skin that can be scaled near infinitely to represent a wound of any size but does not include the wound edge. As the model is refined and further validated, our goal is to allow for model calibration with individualized patient data in order to create a personalized “digital twin” that closely mimics the patient’s physiology and may augment diagnostic or therapeutic decision-making in the clinical setting
Model-based machine learning to identify clinical relevance in a high-resolution simulation of sepsis and trauma
No full textIntroduction: Sepsis is a devastating, costly, and complicated disease. It represents the summation of varied host immune responses in a clinical and physiological diagnosis. Despite extensive research, there is no current mediator-directed therapy, nor a biomarker panel able to categorize disease severity or reliably predict outcome. Although still distant from direct clinical translation, dynamic computational and mathematical models of acute systemic inflammation and sepsis are being developed. Although computationally intensive to run and calibrate, agent-based models (ABMs) are one type of model well suited for this. New analytical methods to efficiently extract knowledge from ABMs are needed. Specifically, machine-learning techniques are a promising option to augment the model development process such that parameterization and calibration are performed intelligently and efficiently. Methods: We used the Keras framework to train an Artificial Neural Network (ANN) for the purpose of identifying critical biological tipping points at which an in silico patient would heal naturally or require intervention in the Innate Immune Response Agent-Based Model (IIRABM). This ANN, determines simulated patient “survival” from cytokine state based on their overall resilience and the pathogenicity of any active infections experienced by the patient, defined by microbial invasiveness, toxigenesis, and environmental toxicity. These tipping points were gathered from previously generated datasets of simulated sweeps of the 4 IIRABM initializing parameters. Results: Using mean squared error as our loss function, we report an accuracy of greater than 85% with inclusion of 20% of the training set. This accuracy was independently validated on withheld runs. We note that there is some amount of error that is inherent to this process as the determination of the tipping points is a computation which converges monotonically to the true value as a function of the number of stochastic replicates used to determine the point. Conclusion: Our method of regression of these critical points represents an alternative to traditional parameter-sweeping or sensitivity analysis techniques. Essentially, the ANN computes the boundaries of the clinically relevant space as a function of the model’s parameterization, eliminating the need for a brute-force exploration of model parameter space. In doing so, we demonstrate the successful development of this ANN which will allows for an efficient exploration of model parameter space
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[Members of the Talons]
No full textPhotograph of members of North Texas State University's men's spirit group, the Talons, taken in front of the President's House with Boomer the Canon in 1973. From left: Barry Wortham, Stan Rhoads, John Walker, Steve Montgomery, Bill Pearson, Bruce Thompson, Don Hughes, Tom Mars, Ben Pearson, Wes Spiegel, Ted Chase, Robert Wells, Fred Arroyo, Phil Cockrell and Bruce Pfieffer
Ab initio nuclear structure calculations for light nuclei
No full textWe perform no-core full configuration calculations for the Lithium isotopes, 6Li, 7Li, and 8Li with the realistic nucleon-nucleon interaction JISP16. We obtain a set of observables, such as spectra, radii, multipole moments, transition probabilities, etc., and compare with experiment where available. We obtain underbinding by 0.5 MeV, 0.7 MeV, and 1.0 MeV for 6 Li, 7 Li, 8 Li respectively. Magnetic moments are well-converged and agree with experiment to within 20%. We then introduce the One-Body Density Matrix. We present a method to remove the spurious center-of-mass component from the space-fixed density distribution. We present space- fixed and translationally-invariant density distributions for various states of 6Li, 7Li, and 8Li. We also examine select translationally-invariant density distributions from the ground state and several excited states of 9Be. The resulting translationally-invariant densities can be used to examine convergence issues and better represent features of the nuclear shape. Convergence properties of these density distributions shed light on the convergence properties of experimental one-body observables. We then present a method to calculate the space-fixed and translationally-invariant Wigner Function using our One-Body Density Matrices. We present a novel visualization of these Wigner Functions
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[Members of the Talons]
No full textPhotograph of members of North Texas State University's men's spirit group, the Talons, taken in front of the President's House in 1973. Tentatively identified are: From left: Stan Rhoads, Barry Wortham, Bill Pearson, unknown, Bruce Thompson, Tom Mars, Phil Cockrell, unknown, Steve Montgomery, Don Hughes, Fred Arroyo, John Walker, Robert Wells, Bruce Pfieffer, Ben Pearson, Ted Chase, and Wes Spiegel
Recommended from our members
[Members of the Talons]
No full textPhotograph of members of North Texas State University's men's spirit group, the Talons, taken in front of the President's House with Boomer the Canon in 1973. From left: Barry Wortham, Stan Rhoads, John Walker, Steve Montgomery, Bill Pearson, Bruce Thompson, Don Hughes, Tom Mars, Ben Pearson, Wes Spiegel, Ted Chase, Robert Wells, Fred Arroyo, Phil Cockrell and Bruce Pfieffer
Recommended from our members
[Members of the Talons]
No full textPhotograph of members of North Texas State University's men's spirit group, the Talons, taken in front of the President's House in 1973. Tentatively identified are: From left: Stan Rhoads, Barry Wortham, Bill Pearson, unknown, Bruce Thompson, Tom Mars, Phil Cockrell, unknown, Steve Montgomery, Don Hughes, Fred Arroyo, John Walker, Robert Wells, Bruce Pfieffer, Ben Pearson, Ted Chase, and Wes Spiegel
Les Misérables
No full textThe vibe was decidedly French, and with the Eiffel Tower as a backdrop, guests were transported back in time to early 19th century Paris at the Broward Performing Arts Foundation\u27s 2025 Annual Reception & Brunch underwritten by J.P. Morgan Chase. Author, historian and master storyteller Dr. Robert Watson brought to life the history that inspired the show that revolutionized Broadway musicals, Les Misérables
Examining the controllability of sepsis using genetic algorithms on an agent-based model of systemic inflammation.
No full textSepsis, a manifestation of the body's inflammatory response to injury and infection, has a mortality rate of between 28%-50% and affects approximately 1 million patients annually in the United States. Currently, there are no therapies targeting the cellular/molecular processes driving sepsis that have demonstrated the ability to control this disease process in the clinical setting. We propose that this is in great part due to the considerable heterogeneity of the clinical trajectories that constitute clinical "sepsis," and that determining how this system can be controlled back into a state of health requires the application of concepts drawn from the field of dynamical systems. In this work, we consider the human immune system to be a random dynamical system, and investigate its potential controllability using an agent-based model of the innate immune response (the Innate Immune Response ABM or IIRABM) as a surrogate, proxy system. Simulation experiments with the IIRABM provide an explanation as to why single/limited cytokine perturbations at a single, or small number of, time points is unlikely to significantly improve the mortality rate of sepsis. We then use genetic algorithms (GA) to explore and characterize multi-targeted control strategies for the random dynamical immune system that guide it from a persistent, non-recovering inflammatory state (functionally equivalent to the clinical states of systemic inflammatory response syndrome (SIRS) or sepsis) to a state of health. We train the GA on a single parameter set with multiple stochastic replicates, and show that while the calculated results show good generalizability, more advanced strategies are needed to achieve the goal of adaptive personalized medicine. This work evaluating the extent of interventions needed to control a simplified surrogate model of sepsis provides insight into the scope of the clinical challenge, and can serve as a guide on the path towards true "precision control" of sepsis
Examining the controllability of sepsis using genetic algorithms on an agent-based model of systemic inflammation
No full text- …
