ABSTRACT
BackgroundWhile the biomarkers of COVID-19 severity have been thoroughly investigated, the key biological dynamics associated with COVID-19 resolution are still insufficiently understood. Main bodyWe report a case of full resolution of severe COVID-19 due to convalescent plasma transfusion in a patient with underlying multiple autoimmune syndrome. Following transfusion, the patient showed fever remission, improved respiratory status, and rapidly decreased viral burden in respiratory fluids and SARS-CoV-2 RNAemia. Longitudinal unbiased proteomic analysis of plasma and single-cell transcriptomics of peripheral blood cells conducted prior to and at multiple times after convalescent plasma transfusion identified the key biological processes associated with the transition from severe disease to disease-free state. These included (i) temporally ordered upward and downward changes in plasma proteins reestablishing homeostasis and (ii) post-transfusion disappearance of a particular subset of dysfunctional monocytes characterized by hyperactivated Interferon responses and decreased TNF- signaling. ConclusionsMonitoring specific subsets of innate immune cells in peripheral blood may provide prognostic keys in severe COVID-19. Moreover, understanding disease resolution at the molecular and cellular level should contribute to identify targets of therapeutic interventions against severe COVID-19.
ABSTRACT
The reason for the striking differences in clinical outcomes of SARS-CoV-2 infected patients is still poorly understood. While most recover, a subset of people become critically ill and succumb to the disease. Thus, identification of biomarkers that can predict the clinical outcomes of COVID-19 disease is key to help prioritize patients needing urgent treatment. Given that an unbalanced gut microbiome is a reflection of poor health, we aim to identify indicator species that could predict COVID-19 disease clinical outcomes. Here, for the first time and with the largest COVID-19 patient cohort reported for microbiome studies, we demonstrated that the intestinal and oral microbiome make-up predicts respectively with 92% and 84% accuracy (Area Under the Curve or AUC) severe COVID-19 respiratory symptoms that lead to death. The accuracy of the microbiome prediction of COVID-19 severity was found to be far superior to that from training similar models using information from comorbidities often adopted to triage patients in the clinic (77% AUC). Additionally, by combining symptoms, comorbidities, and the intestinal microbiota the model reached the highest AUC at 96%. Remarkably the model training on the stool microbiome found enrichment of Enterococcus faecalis, a known pathobiont, as the top predictor of COVID-19 disease severity. Enterococcus faecalis is already easily cultivable in clinical laboratories, as such we urge the medical community to include this bacterium as a robust predictor of COVID-19 severity when assessing risk stratification of patients in the clinic.
ABSTRACT
Abdominal pain can be disabling in patients with gastroparesis. The pathogenesis of pain in these individuals is poorly understood. Agents commonly used in clinical practice, including tricyclic antidepressants, gabapentin, and pregabalin, have remained largely unsatisfactory in treating this pain. We report the case of a 50-year-old woman presenting with chronic unrelenting abdominal pain due to severe diabetic gastroparesis that was managed successfully with coeliac plexus block with local anaesthesia and steroid injection. Adequate analgesia was achieved and maintained for 10 weeks following the coeliac plexus block, which allowed elimination of opiate requirements for pain management (and avoidance of narcotic associated constipation), continuation of percutaneous endoscopy jejunostomy tube feedings, and avoidance of long term parenteral nutrition.