COMMBINI:an experimentally-informed COmputational Model of Macrophage dynamics in the Bone INjury Immunoresponse

Bone fracture healing is a well-orchestrated but complex process that involves numerous regulations at different scales. This complexity becomes particularly evident during the inflammatory stage, as immune cells invade the healing region and trigger a cascade of signals to promote a favorable regenerative environment. Thus, the emergence of criticalities during this stage might hinder the rest of the process. Therefore, the investigation of the many interactions that regulate the inflammation has a primary importance on the exploration of the overall healing progression. In this context, an in silico model named COMMBINI (COmputational Model of Macrophage dynamics in the Bone INjury Immunoresponse) has been developed to investigate the mechano-biological interactions during the early inflammatory stage at the tissue, cellular and molecular levels. An agent-based model is employed to simulate the behavior of immune cells, inflammatory cytokines and fracture debris as well as their reciprocal multiscale biological interactions during the development of the early inflammation (up to 5 days post-injury). The strength of the computational approach is the capacity of the in silico model to simulate the overall healing process by taking into account the numerous hidden events that contribute to its success. To calibrate the model, we present an in silico immunofluorescence method that enables a direct comparison at the cellular level between the model output and experimental immunofluorescent images. The combination of sensitivity analysis and a Genetic Algorithm allows dynamic cooperation between these techniques, enabling faster identification of the most accurate parameter values, reducing the disparity between computer simulation and histological data. The sensitivity analysis showed a higher sensibility of the computer model to the macrophage recruitment ratio during the early inflammation and to proliferation in the late stage. Furthermore, the Genetic Algorithm highlighted an underestimation of macrophage proliferation by in vitro experiments. Further experiments were conducted using another externally fixated murine model, providing an independent validation dataset. The validated COMMBINI platform serves as a novel tool to deepen the understanding of the intricacies of the early bone regeneration phases. COMMBINI aims to contribute to designing novel treatment strategies in both the biological and mechanical domains.</p

COMMBINI:an experimentally-informed COmputational Model of Macrophage dynamics in the Bone INjury Immunoresponse

Bone fracture healing is a well-orchestrated but complex process that involves numerous regulations at different scales. This complexity becomes particularly evident during the inflammatory stage, as immune cells invade the healing region and trigger a cascade of signals to promote a favorable regenerative environment. Thus, the emergence of criticalities during this stage might hinder the rest of the process. Therefore, the investigation of the many interactions that regulate the inflammation has a primary importance on the exploration of the overall healing progression. In this context, an in silico model named COMMBINI (COmputational Model of Macrophage dynamics in the Bone INjury Immunoresponse) has been developed to investigate the mechano-biological interactions during the early inflammatory stage at the tissue, cellular and molecular levels. An agent-based model is employed to simulate the behavior of immune cells, inflammatory cytokines and fracture debris as well as their reciprocal multiscale biological interactions during the development of the early inflammation (up to 5 days post-injury). The strength of the computational approach is the capacity of the in silico model to simulate the overall healing process by taking into account the numerous hidden events that contribute to its success. To calibrate the model, we present an in silico immunofluorescence method that enables a direct comparison at the cellular level between the model output and experimental immunofluorescent images. The combination of sensitivity analysis and a Genetic Algorithm allows dynamic cooperation between these techniques, enabling faster identification of the most accurate parameter values, reducing the disparity between computer simulation and histological data. The sensitivity analysis showed a higher sensibility of the computer model to the macrophage recruitment ratio during the early inflammation and to proliferation in the late stage. Furthermore, the Genetic Algorithm highlighted an underestimation of macrophage proliferation by in vitro experiments. Further experiments were conducted using another externally fixated murine model, providing an independent validation dataset. The validated COMMBINI platform serves as a novel tool to deepen the understanding of the intricacies of the early bone regeneration phases. COMMBINI aims to contribute to designing novel treatment strategies in both the biological and mechanical domains.</p

Impact of implementation of an individualised thromboprophylaxis protocol in critically ill ICU patients with COVID-19: A longitudinal controlled before-after study

INTRODUCTION: An individualised thromboprophylaxis was implemented in critically ill patients suffering from coronavirus disease 2019 (COVID-19) pneumonia to reduce mortality and improve clinical outcome. The aim of this study was to evaluate the effect of this intervention on clinical outcome. METHODS: In this mono-centric, controlled, before-after study, all consecutive adult patients with confirmed COVID-19 pneumonia admitted to ICU from March 13th to April 20th 2020 were included. A thromboprophylaxis protocol, including augmented LMWH dosing, individually tailored with anti-Xa measurements and twice-weekly ultrasonography screening for DVT, was implemented on March 31th 2020. Primary endpoint is one-month mortality. Secondary outcomes include two-week and three-week mortality, the incidence of VTE, acute kidney injury and continuous renal replacement therapy (CRRT). Multiple regression modelling was used to correct for differences between the two groups. RESULTS: 46 patients were included in the before group, 26 patients in the after group. One month mortality decreased from 39.13% to 3.85% (p < 0.001). After correction for confounding variables, one-month mortality was significantly higher in the before group (p = 0.02, OR 8.86 (1.46, 53.75)). The cumulative incidence of VTE and CRRT was respectively 41% and 30.4% in the before group and dropped to 15% (p = 0.03) and 3.8% (p = 0.01), respectively. After correction for confounding variables, risk of VTE (p = 0.03, 6.01 (1.13, 32.12)) and CRRT (p = 0.02, OR 19.21 (1.44, 255.86)) remained significantly higher in the before group. CONCLUSION: Mortality, cumulative risk of VTE and need for CRRT may be significantly reduced in COVID-19 patients by implementation of a more aggressive thromboprophylaxis protocol. Future research should focus on confirmation of these results in a randomized design and on uncovering the mechanisms underlying these observations. REGISTRATION NUMBER: NCT04394000.status: publishe