Idiopathic Pan-Colonic and Small-Intestine Varices

Idiopathic colonic varices represent a rare source of gastrointestinal haemorrhage with a presumed incidence around 0.0007%. Herein, we present a case of idiopathic colonic and small-intestine varices. According to our knowledge, this case report is the first description of both pan-colonic and small-intestine idiopathic varices of this extent. A young male patient without any previous notable medical history was admitted to the hospital because of massive enterorrhagia with haemodynamic instability. Colonoscopy revealed massive pan-colonic varices. After stabilization, numerous diagnostic procedures were performed in order to investigate the aetiology of pan-colonic varices without any explanation of the patient’s condition. In addition, capsule endoscopy revealed varices through the whole length of the small intestine. The final diagnosis was idiopathic varices of the colon and small intestine. Because of the rapid clinical stabilization, the single incident of haemorrhage and the extension of the disease, a conservative approach was chosen (venotonics and β-blockers). During the 12-month follow-up period, the patient reported no gastrointestinal haemorrhage

Persistence of Metabolomic Changes in Patients during Post-COVID Phase: A Prospective, Observational Study

Several relatively recently published studies have shown changes in plasma metabolites in various viral diseases such as Zika, Dengue, RSV or SARS-CoV-1. The aim of this study was to analyze the metabolome profile of patients during acute COVID-19 approximately one month after the acute infection and to compare these results with healthy (SARS-CoV-2-negative) controls. The metabolome analysis was performed by NMR spectroscopy from the peripheral blood of patients and controls. The blood samples were collected on 3 different occasions (at admission, during hospitalization and on control visit after discharge from the hospital). When comparing sample groups (based on the date of acquisition) to controls, there is an indicative shift in metabolomics features based on the time passed after the first sample was taken towards controls. Based on the random forest algorithm, there is a strong discriminatory predictive value between controls and different sample groups (AUC equals 1 for controls versus samples taken at admission, Mathew correlation coefficient equals 1). Significant metabolomic changes persist in patients more than a month after acute SARS-CoV-2 infection. The random forest algorithm shows very strong discrimination (almost ideal) when comparing metabolite levels of patients in two various stages of disease and during the recovery period compared to SARS-CoV-2-negative controls