Background: Deep learning methods are revolutionizing natural science. In this study, we aim to apply such techniques to develop blood type prediction models based on cheap to analyze and easily scalable screening array genotyping platforms.
Methods: Combining existing blood types from blood banks and imputed screening array genotypes for ~111,000 Danish and 1168 Finnish blood donors, we used deep learning techniques to train and validate blood type prediction models for 36 antigens in 15 blood group systems.
Objectives: (Bbsl) and tick-borne encephalitis virus (TBEV) are tick-borne pathogens. This study aimed to investigate the seroprevalence of these pathogens in Danish blood donors.
Methods: A total of 1000 plasma samples equally distributed (n = 200) from all five Danish regions were analyzed.
Background: Surgery induces a temporal change in the immune system, which might be modified by regional anesthesia. Applying a bilateral preoperative anterior quadratus lumborum block has proven to be a safe and effective technique in pain management after abdominal and retroperitoneal surgery, but the effect on the immune response is not thoroughly investigated.
Methods: This study is a substudy of a randomized, controlled, double-blinded trial of patients undergoing laparoscopic hemicolectomy due to colon cancer.
Background: Plasma soluble urokinase-type Plasminogen Activator Receptor (suPAR) predicts disease aggressiveness in renal cell carcinoma (ccRCC), but its prognostic accuracy has not been investigated. To investigate the prognostic accuracy of preoperative plasma suPAR in patients who received curative treatment for initially localized ccRCC.
Methods: We retrospectively analyzed plasma samples stored in the Danish National Biobank between 2010 and 2015 from 235 patients with ccRCC at any stage.