Ri.MED

Extracorporeal membrane oxygenation (ECMO) is a vital intervention for patients with severe respiratory failure, particularly in unresponsive acute respiratory distress syndrome (ARDS) cases. However, patient selection for ECMO remains a significant challenge. This study aims to identify novel immune-based biomarkers to improve eligibility assessment and predict outcomes in critically ill COVID-19 patients undergoing ECMO. This monocentric observational retrospective cohort study included 80 patients with severe COVID-19-related pneumonia who required ECMO support due to unresponsive ARDS. The patients were admitted to the intensive care unit (ICU) of IRCCS-ISMETT Hospital between September 2020 and April 2021, before the availability of COVID-19 vaccines. All patients were infected with the original SARS-CoV-2 Wuhan strain. Using machine learning approaches, the study analyzed clinical and laboratory data, cytokine levels, RNA sequencing (RNA-seq), and immune cell profiles collected within two days of hospitalization. The analysis identified a 5.56-fold increased mortality risk in patients presenting with a combination of immune factors: a T cell exhaustion profile, low interferon-alpha (IFNα) levels, and high calprotectin levels. These immune markers were strongly associated with poorer outcomes in patients undergoing ECMO. Our findings highlight the critical role of immune profiling in ECMO patient selection and outcome prediction. Incorporating immune-based biomarkers into clinical assessments may enhance the evaluation of ECMO eligibility and guide treatment decisions, ultimately improving patient outcomes.

Blind docking predicts binding interactions between two molecular entities without prior knowledge of the binding site. This approach is essential because it explores the entire surface of the receptor to identify potential interaction sites. Blind docking widely works for both protein–protein and ligand–protein interaction studies. In protein–protein blind docking, the method aims to predict the correct orientation and interface of two proteins forming a complex. Protein blind docking is particularly valuable in studying transient interactions, protein–protein recognition, signaling pathways, tentative and significant biomolecular assemblies where structural data is limited. Ligand–protein blind docking discovers potential binding pockets across the entire protein surface. It is frequently applied in early-stage drug discovery, especially for novel or poorly characterized targets. The method helps identify allosteric sites or novel binding regions that are not evident from known structures. Overall, blind docking provides a versatile and powerful tool for studying molecular interactions, enabling discovery even in the absence of detailed structural information. In this scenario, we reported a timeline of attempts to improve this kind of computational approach with ML and hybrid approaches to obtain more reliable predictions. We dedicate two main sections to protein–protein and protein-ligand blind docking, presenting the reliability and caveats for each approach and outlining potential future directions.

Blood-contacting devices are vital in treating cardiovascular diseases but may trigger coagulation and thrombosis, requiring lifelong anticoagulation or repeated surgeries. Endothelial cells, which regulate vascular homeostasis, form a barrier between blood and tissue. Promoting stable endothelialization on device surfaces may reduce these risks. However, conventional devices often fail due to high wall shear stress (WSS), which damages the endothelium. This study numerically evaluates surface topographies to identify those that lower WSS and enhance cell stability. CFD simulations were used to estimate WSS on quadrangular and grooved patterns (10 × 4 mm) with depths of 1, 5, 12 mm, spaced 20 mm apart. Two flow conditions were considered: steady (0.4 m/s) and pulsatile with a sinusoidal profile. Blood was modeled as a Newtonian fluid (density: 1060 kg/m³, viscosity: 3.5·10⁻³ Pa·s). In-silico simulations assessed the impact of meso- and micro-topographies on WSS across various patterns. After an initial instability, WSS stabilized along the 10 mm section. The highest mean WSS (12 Pa) occurred at the shallowest depth, decreasing to 8 Pa and 2 Pa with greater depths. Notably, 8 Pa and 2 Pa fall within the physiological range favorable to cell survival. The comparison with grooved topographies, showed similar WSS at 1 and 5 mm depths, increasing to 5 Pa at 12 mm. Surface topography affects WSS and thereby endothelial growth. The square pattern best preserves tissue integrity by reducing WSS. Future work will explore additional patterns and flow conditions. Cellular experiments are planned to validate the numerical models.

The development of physiologically relevant in vitro 3D models is crucial for studying lung biology and disease mechanisms. While airway organoids have significantly improved our ability to mimic lung tissue, they lack key nonepithelial components that are essential for tissue homeostasis. Here, we describe the generation of human airway assembloids, combining airway organoids, stromal fibroblasts, and endothelial cells to better replicate the native lung environment. The model was generated from healthy lung tissue donors by using a scaffold-free culture system to promote cell self-organization. Assembloids exhibited long-term viability, maintained typical airway epithelial markers, and demonstrated functional characteristics, such as mucus production and ciliary beating. This technology provides a powerful platform for studying airway physiology, disease mechanisms, and therapeutic approaches, with potential applications in regenerative and personalized medicine. Our study established a novel, reproducible 3D assembloid model of the human airways, bridging the gap between traditional organoid cultures and complex tissue engineering strategies.

Corticosteroid-based anti-inflammatory therapies are poorly effective in smokers and novel strategies are needed to treat inflammation. We have shown that Cigarette Smoke (CS) favors activation of the pore forming proteins gasdermins in human lung macrophages and, in combination with lipopolysaccharide (LPS), promotes caspase-dependent pyroptosis, a lytic form of cell death. Herein we investigated whether damage-associated molecular patterns (DAMPs) released by macrophages following gasdermin activation and cell death may trigger steroid-resistant inflammation. DAMPs gene expression was explored in alveolar macrophages (AM) from smokers and compared to non-smokers. Next, the impact of fluticasone propionate and dexamethasone on cell death and DAMPs release was evaluated in LPS plus CS extract (CSE)-exposed human monocyte-derived macrophages (hMDMs). As control, cell death was inhibited using zVAD (pan caspase inhibitor) and Necrostatin 1 (Nec1). Smokers AM displayed upregulation of DAMPs genes, including heat shock proteins, Galectins and S100. hMDMs exposed to LPS plus CSE, but not LPS alone, displayed caspase-8 activation, cleavage of caspase-3 and gasdermin E, enhanced release of S100A8/9, galectin-3 and -1 and cell death. These effects were not inhibited by steroids but they were reduced by zVAD/Nec1 treatment. Conditioned medium (CM) from LPS plus CSE-treated macrophages induced IL-6 and TNF release in freshly derived hMDMs. zVAD/Nec1 treatment, unlike steroids, reduced CM proinflammatory effects. Overall, our data suggest to consider inhibition of cell death as novel therapeutic approach to treat inflammation in smokers.