In silico agent-based modeling approach to characterize multiple in vitro tuberculosis infection models.

In vitro models of Mycobacterium tuberculosis (Mtb) infection are a valuable tool for examining host-pathogen interactions and screening drugs. With the development of more complex in vitro models, there is a need for tools to help analyze and integrate data from these models. To this end, we introduce an agent-based model (ABM) representation of the interactions between immune cells and bacteria in an in vitro setting. This in silico model was used to simulate both traditional and spheroid cell culture models by changing the movement rules and initial spatial layout of the cells in accordance with the respective in vitro models. The traditional and spheroid simulations were calibrated to published experimental data in a paired manner, by using the same parameters in both simulations. Within the calibrated simulations, heterogeneous outputs are seen for bacterial count and T cell infiltration into the macrophage core of the spheroid. The simulations also predict that equivalent numbers of activated macrophages do not necessarily result in similar bacterial reductions; that host immune responses can control bacterial growth in both spheroid structure dependent and independent manners; that STAT1 activation is the limiting step in macrophage activation in spheroids; and that drug screening and macrophage activation studies could have different outcomes depending on the in vitro culture used. Future model iterations will be guided by the limitations of the current model, specifically which parts of the output space were harder to reach. This ABM can be used to represent more in vitro Mtb infection models due to its flexible structure, thereby accelerating in vitro discoveries.

Does the Metabolic and Bariatric Surgery Accreditation and Quality Improvement Program bariatric risk/benefit calculator hold its weight? An assessment of its accuracy.

BACKGROUND: Bariatric clinical calculators have already been implemented in clinical practice to provide objective predictions of complications and outcomes. The Metabolic and Bariatric Surgery Accreditation and Quality Improvement Program (MBSAQIP) Surgical Risk/Benefit Calculator is the most comprehensive risk calculator in bariatric surgery. OBJECTIVES: Evaluate the accuracy of the calculator predictions regarding the 30-day complication risk, 1-year weight loss outcomes, and comorbidity resolution. SETTING: MBSAQIP-accredited center. METHODS: All adult patients who underwent primary laparoscopic Roux-en-Y gastric bypass (RYGB) or sleeve gastrectomy at our institution between 2012 and 2019 were included. Baseline characteristics were used to generate the individualized outcome predictions for each patient through the bariatric risk calculator and were compared to actual patient outcomes. Statistical analysis was performed using c-statistics, linear regression models, and McNemmar chi-square test. RESULTS: One thousand four hundred fifty-three patients with a median age of 45 (37, 55) and consisting of 80.1% females were included in the study. The c-statistics for the complications and comorbidity resolution ranged from .533 for obstructive sleep apnea remission to .675 for 30-day reoperation. The number of comorbidity resolutions predicted by the calculator was significantly higher than the actual remissions for diabetes, hyperlipidemia, hypertension and obstructive sleep apnea (P < .001). On average, the calculator body mass index (BMI) predictions deviated from the observed BMI measurement by 3.24 kg/m2. The RYGB procedure (Coef -.89; P = .005) and preoperative BMI (Coef -.4; P = .012) were risk factors associated with larger absolute difference between the predicted and observed BMI. CONCLUSIONS: The MBSAQIP Surgical Risk/Benefit Calculator prediction models for 1-year BMI, 30-day reoperation, and reintervention risks were fairly well calibrated with an acceptable level of discrimination except for obstructive sleep apnea remission. The 1-year BMI estimations were less accurate for RYGB patients and cases with very high or low preoperative BMI measurements. Therefore, the bariatric risk calculator constitutes a helpful tool that has a place in preoperative counseling.

Agent-based model demonstrates the impact of nonlinear, complex interactions between cytokines on muscle regeneration.

Muscle regeneration is a complex process due to dynamic and multiscale biochemical and cellular interactions, making it difficult to determine optimal treatments for muscle injury using experimental approaches alone. To understand the degree to which individual cellular behaviors impact endogenous mechanisms of muscle recovery, we developed an agent-based model (ABM) using the Cellular Potts framework to simulate the dynamic microenvironment of a cross-section of murine skeletal muscle tissue. We referenced more than 100 published studies to define over 100 parameters and rules that dictate the behavior of muscle fibers, satellite stem cells (SSC), fibroblasts, neutrophils, macrophages, microvessels, and lymphatic vessels, as well as their interactions with each other and the microenvironment. We utilized parameter density estimation to calibrate the model to temporal biological datasets describing cross-sectional area (CSA) recovery, SSC, and fibroblast cell counts at multiple time points following injury. The calibrated model was validated by comparison of other model outputs (macrophage, neutrophil, and capillaries counts) to experimental observations. Predictions for eight model perturbations that varied cell or cytokine input conditions were compared to published experimental studies to validate model predictive capabilities. We used Latin hypercube sampling and partial rank correlation coefficient to identify in silico perturbations of cytokine diffusion coefficients and decay rates to enhance CSA recovery. This analysis suggests that combined alterations of specific cytokine decay and diffusion parameters result in greater fibroblast and SSC proliferation compared to individual perturbations with a 13% increase in CSA recovery compared to unaltered regeneration at 28 days. These results enable guided development of therapeutic strategies that similarly alter muscle physiology (i.e. converting ECM-bound cytokines into freely diffusible forms as studied in cancer therapeutics or delivery of exogenous cytokines) during regeneration to enhance muscle recovery after injury.

Multi-season transmission model of Eastern Equine Encephalitis.

Eastern Equine Encephalitis (EEE) is an arbovirus that, while it has been known to exist since the 1930's, recently had a spike in cases. This increased prevalence is particularly concerning due to the severity of the disease with 1 in 3 symptomatic patients dying. The cause of this peak is currently unknown but could be due to changes in climate, the virus itself, or host behavior. In this paper we propose a novel multi-season deterministic model of EEE spread and its stochastic counterpart. Models were parameterized using a dataset from the Florida Department of Health with sixteen years of sentinel chicken seroconversion rates. The different roles of the enzootic and bridge mosquito vectors were explored. As expected, enzootic mosquitoes like Culiseta melanura were more important for EEE persistence, while bridge vectors were implicated in the disease burden in humans. These models were used to explore hypothetical viral mutations and host behavior changes, including increased infectivity, vertical transmission, and host feeding preferences. Results showed that changes in the enzootic vector transmission increased cases among birds more drastically than equivalent changes in the bridge vector. Additionally, a 5% difference in the bridge vector's bird feeding preference can increase cumulative dead-end host infections more than 20-fold. Taken together, this suggests changes in many parts of the transmission cycle can augment cases in birds, but the bridge vectors feeding preference acts as a valve limiting the enzootic circulation from its impact on dead-end hosts, such as humans. Our what-if scenario analysis reveals and measures possible threats regarding EEE and relevant environmental changes and hypothetically suggests how to prevent potential damage to public health and the equine economy.