Increased levels of inflammatory molecules in blood of Long COVID patients point to thrombotic endotheliitis
Turner, S.; Naidoo, C.; Usher, T.; Kruger, A.; Venter, C.; Laubscher, G. J.; Khan, M. A.; Kell, D. B.; Pretorius, E.
Show abstract
The prevailing hypotheses for the persistent symptoms of Long COVID have been narrowed down to immune dysregulation and autoantibodies, widespread organ damage, viral persistence, and fibrinaloid microclots (entrapping numerous inflammatory molecules) together with platelet hyperactivation. Here we demonstrate significantly increased concentrations of Von Willebrand Factor, platelet factor 4,serum amyloid A, -2antiplasmin E-selectin, and platelet endothelial cell adhesion molecule-1, in the soluble part of the blood. It was noteworthy that the mean level of -2-antiplasmin exceeded the upper limit of the laboratory reference range in Long COVID patients, and the other 5 were significantly elevated in Long COVID patients as compared to the controls. This is alarming if we take into consideration that a significant amount of the total burden of these inflammatory molecules has previously been shown to be entrapped inside fibrinolysis-resistant microclots (thus decreasing the apparent level of the soluble molecules). We also determined that by individually adding E-selectin and PECAM-1 to healthy blood, these molecules may indeed be involved in protein-protein interactions with plasma proteins (contributing to microclot formation) and platelet hyperactivation. This investigation was performed as a laboratory model investigation and the final exposure concentration of these molecules was chosen to mimic concentrations found in Long COVID. We conclude that presence of microclotting, together with relatively high levels of six inflammatory molecules known to be key drivers of endothelial and clotting pathology, points to thrombotic endotheliitis as a key pathological process in Long COVID. This has implications for the choice of appropriate therapeutic options in Long COVID. SENTENCE SUMMARYThe presence of fibrinaloid microclots and multiple inflammatory molecules in the soluble part of blood points to thrombotic endotheliitis as a key pathological process in Long COVID.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Utility of thromboelastography with platelet mapping (TEG-PM) for monitoring platelet transfusion in qualitative platelet disorders 95%
- Mice with Reduced PAR4 Reactivity show Decreased Venous Thrombosis and Platelet Procoagulant Activity 95%
- Quantitative super-resolution imaging of platelet degranulation reveals differential release of VWF and VWF propeptide from alpha-granules 94%
Similar papers in this journal
- Pilot study to evaluate hypercoagulation and inflammation using rotational thromboelastometry and calprotectin in COVID-19 patients 95%
- Platelet, erythrocyte, endothelial, and monocyte microparticles in coagulation activation and propagation 94%
- Coagulation disorders in patients with severe hemophagocytic lymphohistiocytosis 94%
Similar papers in this journal
- Precision targeting of the Plasminogen Activator Inhibitor-1 mechanism increases efficacy of fibrinolytic therapy in empyema. 92%
- Prediction of plasma volume and total hemoglobin mass with machine learning 92%
- Sex Differences in Middle Cerebral Artery Reactivity and Hemodynamics Independent from Changes in Systemic Arterial Stiffness in Adult Sprague-Dawley Rats 91%
Similar papers in this journal
- Expression of ACE2 receptor, soluble ACE2, Angiotensin I, Angiotensin II and Angiotensin (1-7), is modulated in COVID-19 patients 94%
- Inflammation and autoimmunity are interrelated in patients with sickle cell disease at a steady-state condition: implications for vaso-occlusive crisis, pain, and sensory sensitivity 93%
- Abnormal thrombosis and neutrophil activation increases the risk of hospital-acquired sacral pressure injuries and morbidity in patients with COVID-19 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.