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Core-shell quantum dot ZnS/CdSe screen-printed electrodes were used to electrochemically measure human blood plasma levels of exogenous adrenaline administered to cardiac arrest patients. The electrochemical behavior of adrenaline on the modified electrode surface was investigated using differential pulse voltammetry (DPV), cyclic voltammetry, and electrochemical impedance spectroscopy (EIS). Under optimal conditions, the linear working ranges of the modified electrode were 0.001-3 µM (DPV) and 0.001-300 µM (EIS). The best limit of detection for this concentration range was 2.79 × 10-8 µM (DPV). The modified electrodes showed good reproducibility, stability, and sensitivity and successfully detected adrenaline levels.
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Background The COVID-19 infection has spread rapidly since its emergence and has affected a large part of the global population. With the increasing number of cases, researchers are trying to predict the prognosis of patients by using different data with artificial intelligence methods such as machine learning (ML). In this study, we aimed to predict mortality risk in COVID-19 patients using ML algorithms with different datasets. Methodology In this retrospective study, we evaluated the fever, oxygen saturation, laboratory results, thorax computed tomography (CT) findings, and comorbid diseases at admission to the hospital of 404 patients whose diagnosis was confirmed by the reverse transcription polymerase chain reaction test. Different datasets were created by combining the data. The Synthetic Minority Oversampling Technique was used to reduce the imbalance in the dataset. K-nearest neighbors, support vector machine, stochastic gradient descent, random forest, neural network, naive Bayes, logistic regression, gradient boosting, XGBoost, and AdaBoost models were used to create the ML algorithm, and the accuracy rates of mortality prediction were compared. Results When the dataset was created with CT parenchyma score, pulmonary artery and inferior vena cava diameters, and laboratory results, mortality was predicted with an accuracy of 98.4% with the gradient boosting model. Conclusions The study demonstrates that patient prognosis can be accurately predicted using simple measurements from thorax CT scans and laboratory findings.
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Acute appendicitis is the most common cause of acute abdominal pain, requiring emergency surgery. Approximately one third of cases have pain unexcepted location due to its various anatomical location. Acute appendicitis is a very rare cause of left lower quadrant pain; if it occurs, a few congenital anomalies should be considered such as Situs Inversus totalis and Midgut Malrotation (MM). MM is a rare congenital anomaly; it occurs due to error in process of rotation or fixation of intestines around the superior mesenteric vessels and it refers to nonrotation or incomplete rotation of intestines. Here we report a case who presented with left lower abdominal pain and was diagnosed with acute perforated appendicitis with intestinal nonrotation. Clinicians should be aware that intestinal nonrotation may be presented with left lower quadrant pain and complicated by acute appendicitis.
