AI algorithm for personalized resource allocation and treatment of hemorrhage casualties

A deep neural network-based artificial intelligence (AI) model was assessed for its utility in predicting vital signs of hemorrhage patients and optimizing the management of fluid resuscitation in mass casualties. With the use of a cardio-respiratory computational model to generate synthetic data of hemorrhage casualties, an application was created where a limited data stream (the initial 10 min of vital-sign monitoring) could be used to predict the outcomes of different fluid resuscitation allocations 60 min into the future. The predicted outcomes were then used to select the optimal resuscitation allocation for various simulated mass-casualty scenarios. This allowed the assessment of the potential benefits of using an allocation method based on personalized predictions of future vital signs versus a static population-based method that only uses currently available vital-sign information. The theoretical benefits of this approach included up to 46% additional casualties restored to healthy vital signs and a 119% increase in fluid-utilization efficiency. Although the study is not immune from limitations associated with synthetic data under specific assumptions, the work demonstrated the potential for incorporating neural network-based AI technologies in hemorrhage detection and treatment. The simulated injury and treatment scenarios used delineated possible benefits and opportunities available for using AI in pre-hospital trauma care. The greatest benefit of this technology lies in its ability to provide personalized interventions that optimize clinical outcomes under resource-limited conditions, such as in civilian or military mass-casualty events, involving moderate and severe hemorrhage

Predictive Approach Identifies Molecular Targets and Interventions to Restore Angiogenesis in Wounds With Delayed Healing

Impaired angiogenesis is a hallmark of wounds with delayed healing, and currently used therapies to restore angiogenesis have limited efficacy. Here, we employ a computational simulation-based approach to identify influential molecular and cellular processes, as well as protein targets, whose modulation may stimulate angiogenesis in wounds. We developed a mathematical model that captures the time courses for platelets, 9 cell types, 29 proteins, and oxygen, which are involved in inflammation, proliferation, and angiogenesis during wound healing. We validated our model using previously published experimental data. By performing global sensitivity analysis on thousands of simulated wound-healing scenarios, we identified six processes (among the 133 modeled in total) whose modulation may improve angiogenesis in wounds. By simulating knockouts of 25 modeled proteins and by simulating different wound-oxygenation levels, we identified four proteins [namely, transforming growth factor (TGF)-β, vascular endothelial growth factor (VEGF), fibroblast growth factor-2 (FGF-2), and angiopoietin-2 (ANG-2)], as well as oxygen, as therapeutic targets for stimulating angiogenesis in wounds. Our modeling results indicated that simultaneous inhibition of TGF-β and supplementation of either FGF-2 or ANG-2 could be more effective in stimulating wound angiogenesis than the modulation of either protein alone. Our findings suggest experimentally testable intervention strategies to restore angiogenesis in wounds with delayed healing

Theoretical investigations of intercellular communication in the microcirculation: Conducted responses, myoendothelial projections and endothelium derived hyperpolarizing factor

The contractile state of microcirculatory vessels is a major determinant of the blood pressure of the whole systemic circulation. Continuous bi-directional communication exists between the endothelial cells (ECs) and smooth muscle cells (SMCs) that regulates calcium (Ca2+) dynamics in these cells. This study presents theoretical approaches to understand some of the important and currently unresolved microcirculatory phenomena. Agonist induced events at local sites have been shown to spread long distances in the microcirculation. We have developed a multicellular computational model by integrating detailed single EC and SMC models with gap junction and nitric oxide (NO) coupling to understand the mechanisms behind this effect. Simulations suggest that spreading vasodilation mainly occurs through Ca 2+ independent passive conduction of hyperpolarization in RMAs. Model predicts a superior role for intercellular diffusion of inositol (1,4,5)-trisphosphate (IP3) than Ca2+ in modulating the spreading response. Endothelial derived signals are initiated even during vasoconstriction of stimulated SMCs by the movement of Ca2+ and/or IP3 into the EC which provide hyperpolarizing feedback to SMCs to counter the ongoing constriction. Myoendothelial projections (MPs) present in the ECs have been recently proposed to play a role in myoendothelial feedback. We have developed two models using compartmental and 2D finite element methods to examine the role of these MPs by adding a sub compartment in the EC to simulate MP with localization of intermediate conductance calcium activated potassium channels (IKCa) and IP3 receptors (IP 3R). Both models predicted IP3 mediated high Ca2+ gradients in the MP after SMC stimulation with limited global spread. This Ca 2+ transient generated a hyperpolarizing feedback of ∼ 2–3mV. Endothelium derived hyperpolarizing factor (EDHF) is the dominant form of endothelial control of SMC constriction in the microcirculation. A number of factors have been proposed for the role of EDHF but no single pathway is agreed upon. We have examined the potential of myoendothelial gap junctions (MEGJs) and potassium (K+) accumulation as EDHF using two models (compartmental and 2D finite element). An extra compartment is added in SMC to simulate micro domains (MD) which have NaKα2 isoform sodium potassium pumps. Simulations predict that MEGJ coupling is much stronger in producing EDHF than alone K+ accumulation. On the contrary, K+ accumulation can alter other important parameters (EC V m, IKCa current) and inhibit its own release as well as EDHF conduction via MEGJs. The models developed in this study are essential building blocks for future models and provide important insights to the current understanding of myoendothelial feedback and EDHF

Computational Identification of Mechanistic Factors That Determine the Timing and Intensity of the Inflammatory Response

<div><p>Timely resolution of inflammation is critical for the restoration of homeostasis in injured or infected tissue. Chronic inflammation is often characterized by a persistent increase in the concentrations of inflammatory cells and molecular mediators, whose distinct amount and timing characteristics offer an opportunity to identify effective therapeutic regulatory targets. Here, we used our recently developed computational model of local inflammation to identify potential targets for molecular interventions and to investigate the effects of individual and combined inhibition of such targets. This was accomplished via the development and application of computational strategies involving the simulation and analysis of thousands of inflammatory scenarios. We found that modulation of macrophage influx and efflux is an effective potential strategy to regulate the amount of inflammatory cells and molecular mediators in both normal and chronic inflammatory scenarios. We identified three molecular mediators − tumor necrosis factor-α (TNF-α), transforming growth factor-β (TGF-β), and the chemokine CXCL8 − as potential molecular targets whose individual or combined inhibition may robustly regulate both the amount and timing properties of the kinetic trajectories for neutrophils and macrophages in chronic inflammation. Modulation of macrophage flux, as well as of the abundance of TNF-α, TGF-β, and CXCL8, may improve the resolution of chronic inflammation.</p></div

Correlation analysis for the model parameters (P#) and the timing indices of the model output variables.

<p>The shades of grey represent the absolute values of the CCs. Subplot <b>a</b> shows the raw absolute CC values for T_act calculated using the full set of 10,000 simulations. Subplots <b>b</b>, <b>c</b>, and <b>d</b> show only the strong (i.e., CC ≥ 0.5) index-parameter associations for T_act in the full set of 10,000 simulations, T_act in the “chronic” (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004460#sec008" target="_blank">Materials and Methods</a>) subset of simulations, and R_i in the “chronic” subset of simulations, respectively. See <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004460#pcbi.1004460.t001" target="_blank">Table 1</a> for a list of all model variables and parameters.</p

Results of the model sensitivity analysis.

<p>Results of the model sensitivity analysis.</p

List of model output variables (abbreviations) and model parameters with their assigned numbers (P#) and descriptions of their function [11].

<p>List of model output variables (abbreviations) and model parameters with their assigned numbers (P#) and descriptions of their function [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004460#pcbi.1004460.ref011" target="_blank">11</a>].</p

Quantitative indices of a typical inflammatory response curve.

<p>The timing indices (T_act and R_i) and the amount indices (Ψ_max and R_p) are defined as follows. Ψ_max: peak height for a model output variable (molecular species or cell type); T_max: time at which a model output variable reaches the peak height value; T_act: time to reach peak height after inflammation initiation; T₅₀: time for a model output variable to decay from its peak height value to 50% of the peak height; R_i: difference between T₅₀ and T_act; R_p: the value of a model output variable at the right-most time point of the simulation, as a percentage of the peak height value.</p