Mathematical Model of Oxygen Transport in Tuberculosis Granulomas

Datta, Meenal; Via, Laura E.; Chen, Wei; Baish, James W.; Xu, Lei; Barry, Clifton E.; Jain, Rakesh K.

doi:10.1007/s10439-015-1415-3

Cited by 35 publications

(41 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We also did not include neutrophils whose role as “double edge sword” needs further experimental evaluation [ 52 ]. Finally we did not include in our model the oxygen transport within a granuloma [ 53 ]. The present paper can be considered as a first step towards a more comprehensive and predictive model of the dynamics of granulomas in response to infection.…”

Section: Discussionmentioning

confidence: 99%

Modeling Granulomas in Response to Infection in the Lung

2016

View full text Add to dashboard Cite

Alveolar macrophages play a large role in the innate immune response of the lung. However, when these highly immune-regulatory cells are unable to eradicate pathogens, the adaptive immune system, which includes activated macrophages and lymphocytes, particularly T cells, is called upon to control the pathogens. This collection of immune cells surrounds, isolates and quarantines the pathogen, forming a small tissue structure called a granuloma for intracellular pathogens like Mycobacterium tuberculosis (Mtb). In the present work we develop a mathematical model of the dynamics of a granuloma by a system of partial differential equations. The ‘strength’ of the adaptive immune response to infection in the lung is represented by a parameter α, the flux rate by which T cells and M1 macrophages that immigrated from the lymph nodes enter into the granuloma through its boundary. The parameter α is negatively correlated with the ‘switching time’, namely, the time it takes for the number of M1 type macrophages to surpass the number of infected, M2 type alveolar macrophages. Simulations of the model show that as α increases the radius of the granuloma and bacterial load in the granuloma both decrease. The model is used to determine the efficacy of potential host-directed therapies in terms of the parameter α, suggesting that, with fixed dosing level, an infected individual with a stronger immune response will receive greater benefits in terms of reducing the bacterial load.

show abstract

Section: Discussionmentioning

confidence: 99%

Modeling Granulomas in Response to Infection in the Lung

2016

View full text Add to dashboard Cite

show abstract

“…Recently Datta et al ( 2015 ) developed an empirical-based model of an idealized granuloma that captures oxygen transport and consumption using Michaelis–Menten based kinetic approximations. Their model was used to predict the size and shape of granulomas, and outcomes were comparable to in vivo rabbit models of disease.…”

Section: Introductionmentioning

confidence: 99%

Oxygen Modulates the Effectiveness of Granuloma Mediated Host Response to Mycobacterium tuberculosis: A Multiscale Computational Biology Approach

Sershen

Plimpton

May

2016

Front. Cell. Infect. Microbiol.

View full text Add to dashboard Cite

Mycobacterium tuberculosis associated granuloma formation can be viewed as a structural immune response that can contain and halt the spread of the pathogen. In several mammalian hosts, including non-human primates, Mtb granulomas are often hypoxic, although this has not been observed in wild type murine infection models. While a presumed consequence, the structural contribution of the granuloma to oxygen limitation and the concomitant impact on Mtb metabolic viability and persistence remains to be fully explored. We develop a multiscale computational model to test to what extent in vivo Mtb granulomas become hypoxic, and investigate the effects of hypoxia on host immune response efficacy and mycobacterial persistence. Our study integrates a physiological model of oxygen dynamics in the extracellular space of alveolar tissue, an agent-based model of cellular immune response, and a systems biology-based model of Mtb metabolic dynamics. Our theoretical studies suggest that the dynamics of granuloma organization mediates oxygen availability and illustrates the immunological contribution of this structural host response to infection outcome. Furthermore, our integrated model demonstrates the link between structural immune response and mechanistic drivers influencing Mtbs adaptation to its changing microenvironment and the qualitative infection outcome scenarios of clearance, containment, dissemination, and a newly observed theoretical outcome of transient containment. We observed hypoxic regions in the containment granuloma similar in size to granulomas found in mammalian in vivo models of Mtb infection. In the case of the containment outcome, our model uniquely demonstrates that immune response mediated hypoxic conditions help foster the shift down of bacteria through two stages of adaptation similar to thein vitro non-replicating persistence (NRP) observed in the Wayne model of Mtb dormancy. The adaptation in part contributes to the ability of Mtb to remain dormant for years after initial infection.

show abstract

“…As known, the oxygen transport is diffusion-limited from the alveolus to the pulmonary capillary in patients with pulmonary diseases (i.e., pulmonary fibrosis). 29 Several reports [26][27][28][29] have shown that the PFR responds to an increase of diversion at a constant oxygen saturation by indicating poorer pulmonary function. Thus, it is reasonable to assume that the PFR may be used in clinical settings as an indicator of predicting pulmonary function status of patients with H7N9 VI.…”

Section: Discussionmentioning

confidence: 99%

<p>Nomogram for the Individualized Prediction of Survival Among Patients with H7N9 Infection</p>

Cheng

Zhou

Zhao

et al. 2020

RMHP

View full text Add to dashboard Cite

These authors contributed equally to this work Background: Until recently, almost all of these studies have identified multiple risk factors but did not offer practical instruments for routine use in predicting individualized survival in human H7N9 infection cases. The objective of this study is to create a practical instrument for use in predicting an individualized survival probability of H7N9 patients. Methods: A matched case-control study (1:2 ratios) was performed in Zhejiang Province between 2013 and 2019. We reviewed specific factors and outcomes regarding patients with H7N9 virus infection (VI) to determine relationships and developed a nomogram to calculate individualized survival probability. This tool was used to predict each individual patient's probability of survival based on results obtained from the multivariable Cox proportional hazard regression analysis. Results: We examined 227 patients with H7N9 VI enrolled in our study. Stepwise selection was applied to the data, which resulted in a final model with 8 independent predictors [including initial PaO 2 /FiO 2 ratio ≤300 mmHg, age ≥60 years, chronic diseases, poor hand hygiene, time from illness onset to the first medical visit, incubation period ≤5 days, peak C-reactive protein ≥120 mg/L], and initial bilateral lung infection. The concordance index of this nomogram was 0.802 [95% confidence interval (CI): 0.694-0.901] and 0.793 (95% CI: 0.611-0.952) for the training and validation sets, respectively, which indicates adequate discriminatory power. The calibration curves for the survival showed optimal agreement between nomogram prediction and actual observation in the training and validation sets, respectively. Conclusion: We established and validated a novel nomogram that can accurately predict the survival probability of patients with H7N9 VI. This nomogram can serve an important role in counseling patients with H7N9 VI and guide treatment decisions.

show abstract

Mathematical Model of Oxygen Transport in Tuberculosis Granulomas

Cited by 35 publications

References 49 publications

Modeling Granulomas in Response to Infection in the Lung

Modeling Granulomas in Response to Infection in the Lung

Oxygen Modulates the Effectiveness of Granuloma Mediated Host Response to Mycobacterium tuberculosis: A Multiscale Computational Biology Approach

<p>Nomogram for the Individualized Prediction of Survival Among Patients with H7N9 Infection</p>

Contact Info

Product

Resources

About