Key points of technical review for the registration of SARS-CoV-2 antigen/antibody tests

Carbon Dots in the Detection of Pathogenic Bacteria and Viruses

Bacterial and viruses pathogens are a significant hazard to human safety and health. In the imaging and detection of pathogenic microorganisms, the application of fluorescent nanoparticles is very useful. Carbon dots and quantum dots are preferred in this regard as labels, amplifiers, and/or electrode modifiers because of their outstanding features. However, precise diagnostics to identify numerous harmful bacteria simultaneously still face considerable hurdles, yet it is an inevitable issue. With the growing development of biosensors, nanoproduct-based bio-sensing has recently become one of the most promising methods for accurately identifying and quantifying various pathogens at low cost, high sensitivity, and selectivity, with time savings. The most recent applications of carbon dots in optical and electrochemical-based sensors are discussed in this review, along with some examples of pathogen sensors.HighlightsSimultaneous and early detection of pathogens is a critical issue in the management of readily spread to prevent epidemics.Carbon dots-based biosensors are more preferred in detection of pathogens due to high selectivity and sensitivity, as well as quick and cheap point-of-care platform.Summary of recent advances in the design of optical and electrochemical biosensors for the detection of pathogens.

China's NMPA perspective on the clinical performance of SARS-CoV-2 antigen test reagents

The COVID-19 pandemic continues to spread all over the world. In the process of emergency use authorization, the Center for Medical Device Evaluation of the China National Medical Products Administration issued ‘Key Points of Technical Review for the Registration of SARS-CoV-2 Antigen/Antibody Detection Reagents’ as the guidance of registration of antigen and antibody test reagents for the industry. In this document, clinical evaluation requirements of antigen detection reagents are elaborated. Based on the Key Points document and the authors’ review practice, this article explains the evaluation methods and requirements of clinical performance of SARS-CoV-2 antigen-detecting rapid diagnostic tests, then analyzes the application scenarios and intended use of antigen detection reagents.

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.