Automated System for Multiplexing Detection of COVID-19 and Other Respiratory Pathogens.

OBJECTIVE: Infectious diseases are global health challenge, impacted the communities worldwide particularly in the midst of COVID-19 pandemic. The need of rapid and accurate automated systems for detecting pathogens of concern has always been critical. Ideally, such systems shall detect a large panel of pathogens simultaneously regardless of well-equipped facilities and highly trained operators, thus realizing on-site diagnosis for frontline healthcare providers and in critical locations such as borders and airports. METHODS & RESULTS: Avalon Automated Multiplex System, AAMST, is developed to automate a series of biochemistry protocols to detect nucleic acid sequences from multiple pathogens in one test. Automated processes include isolation of nucleic acids from unprocessed samples, reverse transcription and two rounds of amplifications. All procedures are carried out in a microfluidic cartridge performed by a desktop analyzer. The system was validated with reference controls and showed good agreement with their laboratory counterparts. In total 63 clinical samples, 13 positives including those from COVID-19 patients and 50 negative cases were detected, consistent with clinical diagnosis using conventional laboratory methods. CONCLUSIONS: The proposed system has demonstrated promising utility. It would benefit the screening and diagnosis of COVID-19 and other infectious diseases in a simple, rapid and accurate fashion. Clinical and Translational Impact Statement- A rapid and multiplex diagnostic system proposed in this work can clinically help to control spread of COVID-19 and other infectious agents as it can provide timely diagnosis, isolation and treatment to patients. Using the system at remoted clinical sites can facilitate early clinical management and surveillance.

COVID-19 Delta variants-Current status and implications as of August 2021.

The SARS-CoV-2 Delta variant has evolved as the dominant strain of the current pandemic. Studies have shown that this variant has increased infectivity/viral load, and reduced neutralization by the host antibodies from convalescent patients/vaccinees. Clinically, Delta variant infection has been observed/documented in convalescent patients/vaccinees, although with less incidence of severe diseases, but can serve as reservoir to spread the infection to the unvaccinated. The current understanding (as of 18 August 2021) on the virologic aspect (including the amino acid substitutions), clinical implications, and public health implications will be discussed in this mini review, and recommendations to health authorities will be provided.

A deep-learning pipeline for the diagnosis and discrimination of viral, non-viral and COVID-19 pneumonia from chest X-ray images.

Common lung diseases are first diagnosed using chest X-rays. Here, we show that a fully automated deep-learning pipeline for the standardization of chest X-ray images, for the visualization of lesions and for disease diagnosis can identify viral pneumonia caused by coronavirus disease 2019 (COVID-19) and assess its severity, and can also discriminate between viral pneumonia caused by COVID-19 and other types of pneumonia. The deep-learning system was developed using a heterogeneous multicentre dataset of 145,202 images, and tested retrospectively and prospectively with thousands of additional images across four patient cohorts and multiple countries. The system generalized across settings, discriminating between viral pneumonia, other types of pneumonia and the absence of disease with areas under the receiver operating characteristic curve (AUCs) of 0.94-0.98; between severe and non-severe COVID-19 with an AUC of 0.87; and between COVID-19 pneumonia and other viral or non-viral pneumonia with AUCs of 0.87-0.97. In an independent set of 440 chest X-rays, the system performed comparably to senior radiologists and improved the performance of junior radiologists. Automated deep-learning systems for the assessment of pneumonia could facilitate early intervention and provide support for clinical decision-making.

Zooming past the coronavirus lockdown: online spirometry practical demonstration with student involvement in analysis by remote control.

This paper describes the process involved in conducting an online spirometry practical through Zoom. The teacher demonstrated the practical from the medical school, and the students observed the procedure from the comfort of their own homes. Students were able to analyze the graphs captured in the teacher's laptop by remotely controlling the teacher's laptop. This method may be useful for places where face-to-face classes are suspended due to the COVID-19 pandemic.