Inflammation and Disease: Modelling and Modulation of the Inflammatory Response to Alleviate Critical Illness

Bioengineered Extracellular Vesicle Hydrogel Modulating Inflammatory Microenvironment for Wound Management

Chronic wounds, frequently arising from conditions like diabetes, trauma, or chronic inflammation, represent a significant medical challenge due to persistent inflammation, heightened infection risk, and limited treatment solutions. This study presents a novel bioengineered approach to promote tissue repair and improve the healing environment. We developed a bioactive hydrogel patch, encapsulated zeolitic imidazolate framework-8 (ZIF-8) into extracellular vesicles (EVs) derived from anti-inflammatory M2 macrophages, and synthesized ZIF@EV, then embedded it in the sodium alginate matrix. This hydrogel structure enables the controlled release of therapeutic agents directly into the wound site, where it stimulates endothelial cell proliferation and promotes new blood vessel formation. These processes are key components of effective tissue regeneration. Crucially, the EV-infused patch influences the immune response by polarizing macrophages towards an M2 phenotype, shifting the wound environment from inflammation toward regenerative healing. When applied in a murine model of chronic wounds, the EV hydrogel patch demonstrated notable improvements in healing speed, quality, and tissue integration compared to traditional approaches such as growth factor therapies and foam dressings. These promising findings suggest that this bioactive hydrogel patch could serve as a versatile, practical solution for chronic wound management, providing an adaptable platform that addresses both the biological and logistical needs of wound care in clinical settings.

A multiscale inflammatory map: linking individual stress to societal dysfunction

As populations worldwide show increasing levels of stress, understanding emerging links among stress, inflammation, cognition, and behavior is vital to human and planetary health. We hypothesize that inflammation is a multiscale driver connecting stressors that affect individuals to large-scale societal dysfunction and, ultimately, to planetary-scale environmental impacts. We propose a 'central inflammation map' hypothesis to explain how the brain regulates inflammation and how inflammation impairs cognition, emotion, and action. According to our hypothesis, these interdependent inflammatory and neural processes, and the inter-individual transmission of environmental, infectious, and behavioral stressors - amplified via high-throughput digital global communications - can culminate in a multiscale, runaway, feed-forward process that could detrimentally affect human decision-making and behavior at scale, ultimately impairing the ability to address these same stressors. This perspective could provide non-intuitive explanations for behaviors and relationships among cells, organisms, and communities of organisms, potentially including population-level responses to stressors as diverse as global climate change, conflicts, and the COVID-19 pandemic. To illustrate our hypothesis and elucidate its mechanistic underpinnings, we present a mathematical model applicable to the individual and societal levels to test the links among stress, inflammation, control, and healing, including the implications of transmission, intervention (e.g., via lifestyle modification or medication), and resilience. Future research is needed to validate the model's assumptions, expand the factors/variables employed, and validate it against empirical benchmarks. Our model illustrates the need for multilayered, multiscale stress mitigation interventions, including lifestyle measures, precision therapeutics, and human ecosystem design. Our analysis shows the need for a coordinated, interdisciplinary, international research effort to understand the multiscale nature of stress. Doing so would inform the creation of interventions that improve individuals' lives and communities' resilience to stress and mitigate its adverse effects on the world.

Towards systems immunology of critical illness at scale: from single cell 'omics to digital twins

Single-cell 'omics methodology has yielded unprecedented insights based largely on data-centric informatics for reducing, and thus interpreting, massive datasets. In parallel, parsimonious mathematical modeling based on abstractions of pathobiology has also yielded major insights into inflammation and immunity, with these models being extended to describe multi-organ disease pathophysiology as the basis of 'digital twins' and in silico clinical trials. The integration of these distinct methods at scale can drive both basic and translational advances, especially in the context of critical illness, including diseases such as COVID-19. Here, I explore achievements and argue the challenges that are inherent to the integration of data-driven and mechanistic modeling approaches, highlighting the potential of modeling-based strategies for rational immune system reprogramming.

Embracing complexity in sepsis

Sepsis involves the dynamic interplay between a pathogen, the host response, the failure of organ systems, medical interventions and a myriad of other factors. This together results in a complex, dynamic and dysregulated state that has remained ungovernable thus far. While it is generally accepted that sepsis is very complex indeed, the concepts, approaches and methods that are necessary to understand this complexity remain underappreciated. In this perspective we view sepsis through the lens of complexity theory. We describe the concepts that support viewing sepsis as a state of a highly complex, non-linear and spatio-dynamic system. We argue that methods from the field of complex systems are pivotal for a fuller understanding of sepsis, and we highlight the progress that has been made over the last decades in this respect. Still, despite these considerable advancements, methods like computational modelling and network-based analyses continue to fly under the general scientific radar. We discuss what barriers contribute to this disconnect, and what we can do to embrace complexity with regards to measurements, research approaches and clinical applications. Specifically, we advocate a focus on longitudinal, more continuous biological data collection in sepsis. Understanding the complexity of sepsis will require a huge multidisciplinary effort, in which computational approaches derived from complex systems science must be supported by, and integrated with, biological data. Such integration could finetune computational models, guide validation experiments, and identify key pathways that could be targeted to modulate the system to the benefit of the host. We offer an example for immunological predictive modelling, which may inform agile trials that could be adjusted throughout the trajectory of disease. Overall, we argue that we should expand our current mental frameworks of sepsis, and embrace nonlinear, system-based thinking in order to move the field forward.

Echocardiographic 60-day mortality markers in patients hospitalized in intensive care for COVID-19

BACKGROUND

Coronavirus disease COVID-19 produces a predominantly pulmonary affection, being cardiac involvement an important component of the multiorganic dysfunction. At the moment there are few reports about the behavior of echocardiographic images in the patients who have the severe forms of the disease.

OBJECTIVE

Identify the echocardiographic prognostic markers for death within 60 days in patients hospitalized in intensive care.

METHODS

A single-center prospective cohort was made with patients hospitalized in intensive care for COVID-19 confirmed via polymerase chain reaction who got an echocardiogram between May and October 2020. A Cox multivariate model was plotted reporting the HR and confidence intervals with their respective p values for clinical and echocardiographic variables.

RESULTS

Out of the 326 patients included, 153 patients got an echocardiogram performed on average 6.8 days after admission. The average age was 60.7, 47 patients (30.7%) were females and 67 (44.7%) registered positive troponin. 91 patients (59.5%) died. The univariate analysis identified TAPSE, LVEF, pulmonary artery systolic pressure, acute cor pulmonale, right ventricle diastolic dysfunction, and right ventricular dilatation as variables associated with mortality. The multivariate model identified that the acute cor pulmonale with HR= 4.05 (CI 95% 1.09 - 15.02, p 0.037), the right ventricular dilatation with HR= 3.33 (CI 95% 1.29 - 8.61, p 0.013), and LVEF with HR= 0.94 (CI 95% 0.89 - 0.99, p 0.020) were associated with mortality within 60 days.

CONCLUSIONS

In patients hospitalized in the intensive care unit for COVID-19, the LVEF, acute cor pulmonale and right ventricular dilatation are prognostic echocardiographic markers associated with death within 60 days.

Divergent COVID-19 Disease Trajectories Predicted by a DAMP-Centered Immune Network Model

COVID-19 presentations range from mild to moderate through severe disease but also manifest with persistent illness or viral recrudescence. We hypothesized that the spectrum of COVID-19 disease manifestations was a consequence of SARS-CoV-2-mediated delay in the pathogen-associated molecular pattern (PAMP) response, including dampened type I interferon signaling, thereby shifting the balance of the immune response to be dominated by damage-associated molecular pattern (DAMP) signaling. To test the hypothesis, we constructed a parsimonious mechanistic mathematical model. After calibration of the model for initial viral load and then by varying a few key parameters, we show that the core model generates four distinct viral load, immune response and associated disease trajectories termed “patient archetypes”, whose temporal dynamics are reflected in clinical data from hospitalized COVID-19 patients. The model also accounts for responses to corticosteroid therapy and predicts that vaccine-induced neutralizing antibodies and cellular memory will be protective, including from severe COVID-19 disease. This generalizable modeling framework could be used to analyze protective and pathogenic immune responses to diverse viral infections.

Quality Control Measures and Validation in Gene Association Studies: Lessons for Acute Illness

Acute illness is a complex constellation of responses involving dysregulated inflammatory and immune responses, which are ultimately associated with multiple organ dysfunction. Gene association studies have associated single-nucleotide polymorphisms (SNPs) with clinical and pharmacological outcomes in a variety of disease states, including acute illness. With approximately 4 to 5 million SNPs in the human genome and recent studies suggesting that a large portion of SNP studies are not reproducible, we suggest that the ultimate clinical utility of SNPs in acute illness depends on validation and quality control measures. To investigate this issue, in December 2018 and January 2019 we searched the literature for peer-reviewed studies reporting data on associations between SNPs and clinical outcomes and between SNPs and pharmaceuticals (i.e., pharmacogenomics) published between January 2011 to February 2019. We review key methodologies and results from a variety of clinical and pharmacological gene association studies, including trauma and sepsis studies, as illustrative examples on current SNP association studies. In this review article, we have found three key points which strengthen the potential accuracy of SNP association studies in acute illness and other diseases: providing evidence of following a protocol quality control method such as the one in Nature Protocols or the OncoArray QC Guidelines; enrolling enough patients to have large cohort groups; and validating the SNPs using an independent technique such as a second study using the same SNPs with new patient cohorts. Our survey suggests the need to standardize validation methods and SNP quality control measures in medicine in general, and specifically in the context of complex disease states such as acute illness.