Mathematical Modeling of Severe Acute Respiratory Syndrome Coronavirus 2 Infection Network with Cytokine Storm, Oxidative Stress, Thrombosis, Insulin Resistance, and Nitric Oxide Pathways

Optimal control of agent-based models via surrogate modeling

This paper describes and validates an algorithm to solve optimal control problems for agent-based models (ABMs). For a given ABM and a given optimal control problem, the algorithm derives a surrogate model, typically lower-dimensional, in the form of a system of ordinary differential equations (ODEs), solves the control problem for the surrogate model, and then transfers the solution back to the original ABM. It applies to quite general ABMs and offers several options for the ODE structure, depending on what information about the ABM is to be used. There is a broad range of applications for such an algorithm, since ABMs are used widely in the life sciences, such as ecology, epidemiology, and biomedicine and healthcare, areas where optimal control is an important purpose for modeling, such as for medical digital twin technology.

Modulatory role of Faecalibacterium on insulin resistance and coagulation in patients with post-viral long haulers depending on adiposity

Patients with Post-viral long hauler encompass lasting symptoms and comorbid complexities, often exacerbated in individuals with excessive body weight. The aim was to study gut microbiota in 130 patients with post-viral long hauler stratified by body mass index (BMI) and the relationship between inflammation and microbiota. Significant higher values were found for anthropometric variables and markers of glucose and dyslipidemia in individuals with higher BMI, as well as elevated levels of C-reactive protein, fibrinogen, IL-6, uric acid, and D-dimer. An interactive association showed an interplay between Faecalibacterium, D-dimer levels, and insulin resistance. This investigation showed that anthropometric, biochemical, and inflammatory variables were impaired in patients with post-viral long haulers with higher BMI. In addition, gut microbiota differences were found between groups and a modification effect on Faecalibacterium abundance regarding insulin resistance and D-dimer. These findings suggest that considering adiposity and gut microbiota structure and composition may improve personalized clinical interventions in patients with chronic inflammation.

Gestational diabetes mellitus in the era of COVID-19: Challenges and opportunities

BACKGROUND AND AIMS

The impact of the coronavirus disease 2019 (COVID-19) pandemic on pregnant women, especially those with gestational diabetes mellitus (GDM), has yet to be fully understood. This review aims to examine the interaction between GDM and COVID-19 and to elucidate the pathophysiological mechanisms underlying the comorbidity of these two conditions.

METHODS

We performed a systematic literature search using the databases of PubMed, Embase, and Web of Science with appropriate keywords and MeSH terms. Our analysis included studies published up to January 26, 2023.

RESULTS

Despite distinct clinical manifestations, GDM and COVID-19 share common pathophysiological characteristics, which involve complex interactions across multiple organs and systems. On the one hand, infection with severe acute respiratory syndrome coronavirus 2 may target the pancreas and placenta, resulting in β-cell dysfunction and insulin resistance in pregnant women. On the other hand, the hormonal and inflammatory changes that occur during pregnancy could also increase the risk of severe COVID-19 in mothers with GDM. Personalized management and close monitoring are crucial for treating pregnant women with both GDM and COVID-19.

CONCLUSIONS

A comprehensive understanding of the interactive mechanisms of GDM and COVID-19 would facilitate the initiation of more targeted preventive and therapeutic strategies. There is an urgent need to develop novel biomarkers and functional indicators for early identification and intervention of these conditions.

Hyperglycaemia and Its Prognostic Value in Patients with COVID-19 Admitted to the Hospital in Lithuania

BACKGROUND AND OBJECTIVES

Increased blood glucose levels atadmission are frequently observed in COVID-19 patients, even in those without pre-existing diabetes. Hyperglycaemia is associated with an increased incidence of severe COVID-19 infection. The aim of this study was to evaluate the association between hyperglycaemia at admission with the need for invasive mechanical ventilation (IMV) and in-hospital mortality in patients without diabetes who were hospitalized for COVID-19 infection.

MATERIALS AND METHODS

This retrospective observational study was conducted at Vilnius University Hospital Santaros Clinics, Lithuania with adult patients who tested positive for severe acute respiratory syndrome coronavirus 2 SARS-CoV-2 and were hospitalized between March 2020 and May 2021. Depersonalized data were retrieved from electronic medical records. Based on blood glucose levels on the day of admission, patients without diabetes were divided into 4 groups: patients with hypoglycaemia (blood glucose below 4.0 mmol/L), patients with normoglycaemia (blood glucose between ≥4.0 mmol/L and <6.1 mmol/L), patients with mild hyperglycaemia (blood glucose between ≥6.1 mmol/L and <7.8 mmol/L), and patients with intermittent hyperglycaemia (blood glucose levels ≥7.8 mmol/L and <11.1 mmol/L). A multivariable binary logistic regression model was created to determine the association between hyperglycaemia and the need for IMV. Survival analysis was performed to assess the effect of hyperglycaemia on outcome within 30 days of hospitalization.

RESULTS

Among 1945 patients without diabetes at admission, 1078 (55.4%) had normal glucose levels, 651 (33.5%) had mild hyperglycaemia, 196 (10.1%) had intermittent hyperglycaemia, and 20 (1.0%) had hypoglycaemia. The oddsratio (OR) for IMV in patients with intermittent hyperglycaemia was 4.82 (95% CI 2.70-8.61, p < 0.001), and the OR was 2.00 (95% CI 1.21-3.31, p = 0.007) in those with mild hyperglycaemia compared to patients presenting normal glucose levels. The hazardratio (HR) for 30-day in-hospital mortality in patients with mild hyperglycaemia was 1.62 (95% CI 1.10-2.39, p = 0.015), while the HR was 3.04 (95% CI 2.01-4.60, p < 0.001) in patients with intermittent hyperglycaemia compared to those with normoglycaemia at admission.

CONCLUSIONS

In COVID-19 patients without pre-existing diabetes, the presence of hyperglycaemia at admission is indicative of COVID-19-induced alterations in glucose metabolism and stress hyperglycaemia. Hyperglycaemia at admission in COVID-19 patients without diabetes is associated with an increased risk of invasive mechanical ventilation and in-hospital mortality. This finding highlights the importance for clinicians to carefully consider and select optimal support and treatment strategies for these patients. Further studies on the long-term consequences of hyperglycaemia in this specific population are warranted.

Modelling of the Innate and Adaptive Immune Response to SARS Viral Infection, Cytokine Storm and Vaccination

In this work, we develop mathematical models of the immune response to respiratory viral infection, taking into account some particular properties of the SARS-CoV infections, cytokine storm and vaccination. Each model consists of a system of ordinary differential equations that describe the interactions of the virus, epithelial cells, immune cells, cytokines, and antibodies. Conventional analysis of the existence and stability of stationary points is completed by numerical simulations in order to study the dynamics of solutions. The behavior of the solutions is characterized by large peaks of virus concentration specific to acute respiratory viral infections. At the first stage, we study the innate immune response based on the protective properties of interferon secreted by virus-infected cells. Viral infection down-regulates interferon production. This competition can lead to the bistability of the system with different regimes of infection progression with high or low intensity. After that, we introduce the adaptive immune response with antigen-specific T- and B-lymphocytes. The resulting model shows how the incubation period and the maximal viral load depend on the initial viral load and the parameters of the immune response. In particular, an increase in the initial viral load leads to a shorter incubation period and higher maximal viral load. The model shows that a deficient production of antibodies leads to an increase in the incubation period and even higher maximum viral loads. In order to study the emergence and dynamics of cytokine storm, we consider proinflammatory cytokines produced by cells of the innate immune response. Depending on the parameters of the model, the system can remain in the normal inflammatory state specific for viral infections or, due to positive feedback between inflammation and immune cells, pass to cytokine storm characterized by the excessive production of proinflammatory cytokines. Finally, we study the production of antibodies due to vaccination. We determine the dose-response dependence and the optimal interval of vaccine dose. Assumptions of the model and obtained results correspond to the experimental and clinical data.

Preparing for the next pandemic: Simulation-based deep reinforcement learning to discover and test multimodal control of systemic inflammation using repurposed immunomodulatory agents

Background

Preparation to address the critical gap in a future pandemic between non-pharmacological measures and the deployment of new drugs/vaccines requires addressing two factors: 1) finding virus/pathogen-agnostic pathophysiological targets to mitigate disease severity and 2) finding a more rational approach to repurposing existing drugs. It is increasingly recognized that acute viral disease severity is heavily driven by the immune response to the infection ("cytokine storm" or "cytokine release syndrome"). There exist numerous clinically available biologics that suppress various pro-inflammatory cytokines/mediators, but it is extremely difficult to identify clinically effective treatment regimens with these agents. We propose that this is a complex control problem that resists standard methods of developing treatment regimens and accomplishing this goal requires the application of simulation-based, model-free deep reinforcement learning (DRL) in a fashion akin to training successful game-playing artificial intelligences (AIs). This proof-of-concept study determines if simulated sepsis (e.g. infection-driven cytokine storm) can be controlled in the absence of effective antimicrobial agents by targeting cytokines for which FDA-approved biologics currently exist.

Methods

We use a previously validated agent-based model, the Innate Immune Response Agent-based Model (IIRABM), for control discovery using DRL. DRL training used a Deep Deterministic Policy Gradient (DDPG) approach with a clinically plausible control interval of 6 hours with manipulation of six cytokines for which there are existing drugs: Tumor Necrosis Factor (TNF), Interleukin-1 (IL-1), Interleukin-4 (IL-4), Interleukin-8 (IL-8), Interleukin-12 (IL-12) and Interferon-γ(IFNg).

Results

DRL trained an AI policy that could improve outcomes from a baseline Recovered Rate of 61% to one with a Recovered Rate of 90% over ~21 days simulated time. This DRL policy was then tested on four different parameterizations not seen in training representing a range of host and microbe characteristics, demonstrating a range of improvement in Recovered Rate by +33% to +56.

Discussion

The current proof-of-concept study demonstrates that significant disease severity mitigation can potentially be accomplished with existing anti-mediator drugs, but only through a multi-modal, adaptive treatment policy requiring implementation with an AI. While the actual clinical implementation of this approach is a projection for the future, the current goal of this work is to inspire the development of a research ecosystem that marries what is needed to improve the simulation models with the development of the sensing/assay technologies to collect the data needed to iteratively refine those models.

An update on the interaction between COVID-19, vaccines, and diabetic kidney disease

Up to now, coronavirus disease 2019 (COVID-19) is still affecting worldwide due to its highly infectious nature anrapid spread. Diabetic kidney disease (DKD) is an independent risk factor for severe COVID-19 outcomes, and they have a certain correlation in some aspects. Particularly, the activated renin–angiotensin–aldosterone system, chronic inflammation, endothelial dysfunction, and hypercoagulation state play an important role in the underlying mechanism linking COVID-19 to DKD. The dipeptidyl peptidase-4 inhibitor is considered a potential therapy for COVID-19 and has similarly shown organ protection in DKD. In addition, neuropilin-1 as an alternative pathway for angiotensin-converting enzyme 2 also contributes to severe acute respiratory syndrome coronavirus 2 entering the host cells, and its decreased expression can affect podocyte migration and adhesion. Here, we review the pathogenesis and current evidence of the interaction of DKD and COVID-19, as well as focus on elevated blood glucose following vaccination and its possible mechanism. Grasping the pathophysiology of DKD patients with COVID-19 is of great clinical significance for the formulation of therapeutic strategies.