Dynamic heterogeneity in COVID-19: Insights from a mathematical model.

Critical illness, such as severe COVID-19, is heterogenous in presentation and treatment response. However, it remains possible that clinical course may be influenced by dynamic and/or random events such that similar patients subject to similar injuries may yet follow different trajectories. We deployed a mechanistic mathematical model of COVID-19 to determine the range of possible clinical courses after SARS-CoV-2 infection, which may follow from specific changes in viral properties, immune properties, treatment modality and random external factors such as initial viral load.

In silico clinical studies for optimal COVID-19 vaccination schedules in patients with cancer.

This study introduces a tailored COVID-19 model for patients with cancer, incorporating viral variants and immune-response dynamics. The model aims to optimize vaccination strategies, contributing to personalized healthcare for vulnerable groups.

Transport Barriers Influence the Activation of Anti-Tumor Immunity: A Systems Biology Analysis.

Effective anti-cancer immune responses require activation of one or more naïve T cells. If the correct naïve T cell encounters its cognate antigen presented by an antigen presenting cell, then the T cell can activate and proliferate. Here, mathematical modeling is used to explore the possibility that immune activation in lymph nodes is a rate-limiting step in anti-cancer immunity and can affect response rates to immune checkpoint therapy.

Cancer immunotherapy response persists after lymph node resection.

Lymphatic transport facilitates the presentation of cancer antigens in tumor-draining lymph nodes (tdLNs), leading to T cell activation and the generation of systemic antitumor immune surveillance. Surgical removal of LNs to control cancer progression is routine in clinical practice. However, whether removing tdLNs impairs immune checkpoint blockade (ICB) is still controversial.

Computational simulations of tumor growth and treatment response: Benefits of high-frequency, low-dose drug regimens and concurrent vascular normalization.

Implementation of effective cancer treatment strategies requires consideration of how the spatiotemporal heterogeneities within the tumor microenvironment (TME) influence tumor progression and treatment response. Here, we developed a multi-scale three-dimensional mathematical model of the TME to simulate tumor growth and angiogenesis and then employed the model to evaluate an array of single and combination therapy approaches. Treatments included maximum tolerated dose or metronomic (i.

Mechanistic model for booster doses effectiveness in healthy, cancer, and immunosuppressed patients infected with SARS-CoV-2.

SARS-CoV-2 vaccines are effective at limiting disease severity, but effectiveness is lower among patients with cancer or immunosuppression. Effectiveness wanes with time and varies by vaccine type. Moreover, previously prescribed vaccines were based on the ancestral SARS-CoV-2 spike-protein that emerging variants may evade.

Investigation of cancer response to chemotherapy: a hybrid multi-scale mathematical and computational model of the tumor microenvironment.

Tumor microenvironment (TME) is a multi-scale biological environment that can control tumor dynamics with many biomechanical and biochemical factors. Investigating the physiology of TME with a heterogeneous structure and abnormal functions not only can achieve a deeper understanding of tumor behavior but also can help develop more efficient anti-cancer strategies. In this work, we develop a hybrid multi-scale mathematical model of TME to simulate the progression of a three-dimensional tumor and elucidate its response to different chemotherapy approaches.

Strategies to minimize heterogeneity and optimize clinical trials in Acute Respiratory Distress Syndrome (ARDS): Insights from mathematical modelling.

Background: Mathematical modelling may aid in understanding the complex interactions between injury and immune response in critical illness.

Methods: We utilize a system biology model of COVID-19 to analyze the effect of altering baseline patient characteristics on the outcome of immunomodulatory therapies. We create example parameter sets meant to mimic diverse patient types.

Chemo-mechanistic multi-scale model of a three-dimensional tumor microenvironment to quantify the chemotherapy response of cancer.

Exploring efficient chemotherapy would benefit from a deeper understanding of the tumor microenvironment (TME) and its role in tumor progression. As in vivo experimental methods are unable to isolate or control individual factors of the TME, and in vitro models often cannot include all the contributing factors, some questions are best addressed with mathematical models of systems biology. In this study, we establish a multi-scale mathematical model of the TME to simulate three-dimensional tumor growth and angiogenesis and then implement the model for an array of chemotherapy approaches to elucidate the effect of TME conditions and drug scheduling on controlling tumor progression.

Placental growth factor promotes tumour desmoplasia and treatment resistance in intrahepatic cholangiocarcinoma.

Objective: Intrahepatic cholangiocarcinoma (ICC)-a rare liver malignancy with limited therapeutic options-is characterised by aggressive progression, desmoplasia and vascular abnormalities. The aim of this study was to determine the role of placental growth factor (PlGF) in ICC progression.

Design: We evaluated the expression of PlGF in specimens from ICC patients and assessed the therapeutic effect of genetic or pharmacologic inhibition of PlGF in orthotopically grafted ICC mouse models.

In silico dynamics of COVID-19 phenotypes for optimizing clinical management.

Understanding the underlying mechanisms of COVID-19 progression and the impact of various pharmaceutical interventions is crucial for the clinical management of the disease. We developed a comprehensive mathematical framework based on the known mechanisms of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, incorporating the renin-angiotensin system and ACE2, which the virus exploits for cellular entry, key elements of the innate and adaptive immune responses, the role of inflammatory cytokines, and the coagulation cascade for thrombus formation. The model predicts the evolution of viral load, immune cells, cytokines, thrombosis, and oxygen saturation based on patient baseline condition and the presence of comorbidities.

In silico dynamics of COVID-19 phenotypes for optimizing clinical management.

Understanding the underlying mechanisms of COVID-19 progression and the impact of various pharmaceutical interventions is crucial for the clinical management of the disease. We developed a comprehensive mathematical framework based on the known mechanisms of the SARS-CoV-2 virus infection, incorporating the renin-angiotensin system and ACE2, which the virus exploits for cellular entry, key elements of the innate and adaptive immune responses, the role of inflammatory cytokines and the coagulation cascade for thrombus formation. The model predicts the evolution of viral load, immune cells, cytokines, thrombosis, and oxygen saturation based on patient baseline condition and the presence of co-morbidities.

High precision invasive FFR, low-cost invasive iFR, or non-invasive CFR?: optimum assessment of coronary artery stenosis based on the patient-specific computational models.

The objective of this paper is to apply computational fluid dynamic (CFD) as a complementary tool for clinical tests to not only predict the present and future status of left coronary artery stenosis but also to evaluate some clinical hypotheses. In order to assess the present status of the coronary artery stenosis severity, and thereby selecting the most appropriate type of treatment for each patient, fractional flow reserve (FFR), instantaneous wave free-ratio (iFR), and coronary flow reserve (CFR) are calculated. To examine FFR, iFR, and CFR results, the effect of geometric features of stenoses, including diameter reduction (%), lesion length (LL), and minimum lumen diameter (MLD), is studied on them.

A mechanobiological mathematical model of liver metabolism.

The liver plays a complex role in metabolism and detoxification, and better tools are needed to understand its function and to develop liver-targeted therapies. In this study, we establish a mechanobiological model of liver transport and hepatocyte biology to elucidate the metabolism of urea and albumin, the production/detoxification of ammonia, and consumption of oxygen and nutrients. Since hepatocellular shear stress (SS) can influence the enzymatic activities of liver, the effect of SS on the urea and albumin synthesis are empirically modeled through the mechanotransduction mechanisms.

A multi-scale model for determining the effects of pathophysiology and metabolic disorders on tumor growth.

The search for efficient chemotherapy drugs and other anti-cancer treatments would benefit from a deeper understanding of the tumor microenvironment (TME) and its role in tumor progression. Because in vivo experimental methods are unable to isolate or control individual factors of the TME and in vitro models often do not include all the contributing factors, some questions are best addressed with systems biology mathematical models. In this work, we present a new fully-coupled, agent-based, multi-scale mathematical model of tumor growth, angiogenesis and metabolism that includes important aspects of the TME spanning subcellular-, cellular- and tissue-level scales.

Simulation of Low Density Lipoprotein (LDL) permeation into multilayer coronary arterial wall: Interactive effects of wall shear stress and fluid-structure interaction in hypertension.

Due to increased atherosclerosis-caused mortality, identification of its genesis and development is of great importance. Although, key factors of the origin of the disease is still unknown, it is widely believed that cholesterol particle penetration and accumulation in arterial wall is mainly responsible for further wall thickening and decreased rate of blood flow during a gradual progression. To date, various effective components are recognized whose simultaneous consideration would lead to a more accurate approximation of Low Density Lipoprotein (LDL) distribution within the wall.

Organ-Tumor-on-a-Chip for Chemosensitivity Assay: A Critical Review.

With a mortality rate over 580,000 per year, cancer is still one of the leading causes of death worldwide. However, the emerging field of microfluidics can potentially shed light on this puzzling disease. Unique characteristics of microfluidic chips (also known as micro-total analysis system) make them excellent candidates for biological applications.