Evaluation of RIDASCREEN® and RIDA®QUICK Helicobacter kits for Helicobacter pylori detection in stools

Comparison of the STANDARD F and SD BIOLINE stool antigen tests for diagnosis of Helicobacter pylori infection

We compared the performance of the STANDARD F and SD BIOLINE stool antigen tests in 335 patients. The performance of STANDARD F (sensitivity: 95.6%; specificity: 94%) was highly comparable to that of SD BIOLINE (sensitivity: 92.6%; specificity: 93.5%), suggesting that STANDARD F is useful for the detection of Helicobacter pylori infection.

Comparison of chemiluminiscence versus lateral flow assay for the detection of Helicobacter pylori antigen in human fecal samples

Helicobacter pylori is a Gram-negative bacterium that causes chronic gastric inflammation, which can lead to gastric neoplasia. Therefore, early diagnosis of H. pylori infection is crucial for effective treatment and prevention of complications. The aim of this study was to compare the sensitivity and specificity of the STANDARD™ F H. pylori Ag FIA stool antigen test (SD Biosensor) with the LIAISON® Meridian H. pylori SA for the diagnosis of H. pylori infection. A total of 133 stool samples from patients with suspected H. pylori infection were compared using the STANDARD™ F H. pylori Ag FIA stool antigen test (SD Biosensor), based on lateral flow assay, with the LIAISON® Meridian H. pylori SA. Of the 45 positive samples with LIAISON, 44 were also positive while 1 was negative in the STANDARD™ antigen test. However, this discrepant sample showed a chemiluminescence index of 1.18, very close to the cut-off point of 1. On the other hand, of 88 negative samples obtained with LIAISON, 83 were negative and 5 were positive in the STANDARD™ antigen test. Moreover, STANDARD™ F H. pylori Ag FIA assay has shown a sensitivity of 97.8% (95% CI: 88.2-99.9), a specificity of 94.3% (95% CI: 87.2-98.1), a PPV of 83.9% (95% CI: 68.9-92.4) and a NPV of 99.3% ((95% CI: 95.3-99.9). In conclusion, the STANDARD™ F H. pylori Ag FIA (SD Biosensor) on the STANDARD™ F2400 analyser is a highly sensitive, specific and suitable assay for the detection of H. pylori in stool samples.

A Novel Computational Model for Detecting the Severity of Inflammation in Confirmed COVID-19 Patients Using Chest X-ray Images

Since late 2019, Coronavirus Disease 2019 (COVID-19) has spread all over the world. The disease is highly contagious, and it may lead to acute respiratory distress (ARD). Medical imaging can play an important role in classifying, detecting, and measuring the severity of the virus. This study aims to provide a novel auto-detection tool that can detect abnormal changes in conventional X-ray images for confirmed COVID-19 cases. X-ray images from patients diagnosed with COVID-19 were converted into 19 different colored layers. Each layer represented objects with similar contrast that could be defined as a specific color. The objects with similar contrasts were formed in a single layer. All the objects from all the layers were extracted as a single-color image. Based on the differentiation of colors, the prototype model was able to recognize a wide spectrum of abnormal changes in the image texture. This was true even if there was minimal variation of the contrast values of the detected uncleared abnormalities. The results indicate that the proposed novel method can detect and determine the degree of lung infection from COVID-19 with an accuracy of 91%, compared to the opinions of three experienced radiologists. The method can also efficiently determine the sites of infection and the severity of the disease by classifying the X-rays into five levels of severity. Thus, the proposed COVID-19 autodetection method can identify locations and indicate the degree of severity of the disease by comparing affected tissue with healthy tissue, and it can predict where the disease may spread.