Rationale: In addition to the overwhelming lung inflammation that prevails in coronavirus disease 2019 (COVID-19), hypercoagulation and thrombosis contribute to the lethality of subjects infected with severe acute respiratory syndrome coronavirus 2. Platelets are chiefly implicated in thrombosis. Moreover, they can interact with viruses and are an important source of inflammatory mediators. While a lower platelet count is associated with severity and mortality, little is known about platelet function during COVID-19. Objective: To evaluate the contribution of platelets to inflammation and thrombosis in patients with COVID-19. Methods and Results: Blood was collected from 115 consecutive patients with COVID-19 presenting nonsevere (n=71) and severe (n=44) respiratory symptoms. We document the presence of severe acute respiratory syndrome coronavirus 2 RNA associated with platelets of patients with COVID-19. Exhaustive assessment of cytokines in plasma and in platelets revealed the modulation of platelet-associated cytokine levels in both patients with nonsevere and severe COVID-19, pointing to a direct contribution of platelets to the plasmatic cytokine load. Moreover, we demonstrate that platelets release their alpha- and dense-granule contents in both nonsevere and severe forms of COVID-19. In comparison to concentrations measured in healthy volunteers, phosphatidylserine-exposing platelet extracellular vesicles were increased in nonsevere, but not in severe cases of COVID-19. Levels of D-dimers, a marker of thrombosis, failed to correlate with any measured indicators of platelet activation. Functionally, platelets were hyperactivated in COVID-19 subjects presenting nonsevere and severe symptoms, with aggregation occurring at suboptimal thrombin concentrations. Furthermore, platelets adhered more efficiently onto collagen-coated surfaces under flow conditions. Conclusions: Taken together, the data suggest that platelets are at the frontline of COVID-19 pathogenesis, as they release various sets of molecules through the different stages of the disease. Platelets may thus have the potential to contribute to the overwhelming thrombo-inflammation in COVID-19, and the inhibition of pathways related to platelet activation may improve the outcomes during COVID-19.

Severe acute respiratory syndrome coronavirus 2 is responsible for coronavirus disease 2019 (COVID‐19). While COVID‐19 is often benign, a subset of patients develops severe multilobar pneumonia that can progress to an acute respiratory distress syndrome. There is no cure for severe COVID‐19 and few treatments significantly improved clinical outcome. Dexamethasone and possibly aspirin, which directly/indirectly target the biosynthesis/effects of numerous lipid mediators are among those options. Our objective was to define if severe COVID‐19 patients were characterized by increased bioactive lipids modulating lung inflammation. A targeted lipidomic analysis of bronchoalveolar lavages (BALs) by tandem mass spectrometry was done on 25 healthy controls and 33 COVID‐19 patients requiring mechanical ventilation. BALs from severe COVID‐19 patients were characterized by increased fatty acids and inflammatory lipid mediators. There was a predominance of thromboxane and prostaglandins. Leukotrienes were also increased, notably LTB ₄ , LTE ₄ , and eoxin E ₄ . Monohydroxylated 15‐lipoxygenase metabolites derived from linoleate, arachidonate, eicosapentaenoate, and docosahexaenoate were also increased. Finally yet importantly, specialized pro‐resolving mediators, notably lipoxin A ₄ and the D‐series resolvins, were also increased, underscoring that the lipid mediator storm occurring in severe COVID‐19 involves pro‐ and anti‐inflammatory lipids. Our data unmask the lipid mediator storm occurring in the lungs of patients afflicted with severe COVID‐19. We discuss which clinically available drugs could be helpful at modulating the lipidome we observed in the hope of minimizing the deleterious effects of pro‐inflammatory lipids and enhancing the effects of anti‐inflammatory and/or pro‐resolving lipid mediators.

Inflammatory bowel disease (IBD) is a group of chronic disorders that includes two main disease forms, Crohn’s disease, and ulcerative colitis. The understanding of the intestinal inflammation occurring in IBD has been immeasurably advanced by the development of the now numerous murine models of intestinal inflammation. The usefulness of this research tool in IBD arises from a convergence of underlying genetic susceptibility, immune system dysfunction, environmental factors, and shifts in gut microbiota. Due to the multifactorial feature of these diseases, different animal models have been used to investigate the underlying mechanisms and develop potential therapeutic strategies. The results of preclinical efficacy studies often inform the progression of therapeutic strategies. This review describes the distinct feature and limitations of each murine IBD model and discusses the previous and current lessons from the IBD models.

The purpose of this study is to develop and test machine learning-based models for COVID-19 severity prediction. COVID-19 test samples from 337 COVID-19 positive patients at Cheikh Zaid Hospital were grouped according to the severity of their illness. Ours is the first study to estimate illness severity by combining biological and non-biological data from patients with COVID-19. Moreover the use of ML for therapeutic purposes in Morocco is currently restricted, and ours is the first study to investigate the severity of COVID-19. When data analysis approaches were used to uncover patterns and essential characteristics in the data, C-reactive protein, platelets, and D-dimers were determined to be the most associated to COVID-19 severity prediction. In this research, many data reduction algorithms were used, and Machine Learning models were trained to predict the severity of sickness using patient data. A new feature engineering method based on topological data analysis called Uniform Manifold Approximation and Projection (UMAP) shown that it achieves better results. It has 100% accuracy, specificity, sensitivity, and ROC curve in conducting a prognostic prediction using different machine learning classifiers such as X_GBoost, AdaBoost, Random Forest, and ExtraTrees. The proposed approach aims to assist hospitals and medical facilities in determining who should be seen first and who has a higher priority for admission to the hospital.

The pathogenesis of inflammatory bowel disease (IBD) involves interactions between the host genetic susceptibility, intestinal microflora and mucosal immune responses through the pattern recognition receptor. Polymorphisms in toll-like receptor 4 (TLR4) induce an aberrant immune response to indigenous intestinal flora, which might favor IBD development. In this study, we aimed to determine whether TLR4 gene was associated with Crohn’s disease (CD) and ulcerative colitis (UC) among Moroccan patients, and evaluated its correlation with clinical manifestation of the disease. The study population comprised 117 patients with IBD and 112 healthy unrelated blood donors. TLR4 polymorphisms: Asp299Gly and Thr399Ile were genotyped by polymerase chain reaction-restriction fragment length polymorphism. PCR products were cleaved with Nco I for the Asp299Gly polymorphism and Hinf I for the Thr399Ile polymorphism. Meta-analysis was performed to test the association of 299Gly and 399Ileu carriage with CD, UC and the overall IBD risk. Our study revealed that the frequency of Asp299Gly and Thr399Ile did not differ significantly between patients and controls in the Moroccan population. However, meta-analysis demonstrated significantly higher frequencies of both Asp299Gly and Thr399Ile SNPs in IBD and CD and for 399Ileu carriage in UC patients. The meta-analysis provides evidence that TLR4 polymorphisms confer a significant increased risk for the overall IBD development.