Various next generation sequencing (NGS) based strategies have been successfully used in the recent past for tracing origins and understanding the evolution of infectious agents, investigating the spread and transmission chains of outbreaks, as well as facilitating the development of effective and rapid molecular diagnostic tests and contributing to the hunt for treatments and vaccines. The ongoing COVID-19 pandemic poses one of the greatest global threats in modern history and has already caused severe social and economic costs. The development of efficient and rapid sequencing methods to reconstruct the genomic sequence of SARS-CoV-2, the etiological agent of COVID-19, has been fundamental for the design of diagnostic molecular tests and to devise effective measures and strategies to mitigate the diffusion of the pandemic. Diverse approaches and sequencing methods can, as testified by the number of available sequences, be applied to SARS-CoV-2 genomes. However, each technology and sequencing approach has its own advantages and limitations. In the current review, we will provide a brief, but hopefully comprehensive, account of currently available platforms and methodological approaches for the sequencing of SARS-CoV-2 genomes. We also present an outline of current repositories and databases that provide access to SARS-CoV-2 genomic data and associated metadata. Finally, we offer general advice and guidelines for the appropriate sharing and deposition of SARS-CoV-2 data and metadata, and suggest that more efficient and standardized integration of current and future SARS-CoV-2-related data would greatly facilitate the struggle against this new pathogen. We hope that our 'vademecum' for the production and handling of SARS-CoV-2-related sequencing data, will contribute to this objective.

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Next generation sequencing of SARS-CoV-2 genomes: challenges, applications and opportunities.

The coronavirus disease 2019 (COVID-19) pandemic is an exceptional public health crisis that demands the timely creation of new therapeutics and viral detection. Owing to their high specificity and reliability, monoclonal antibodies (mAbs) have emerged as powerful tools to treat and detect numerous diseases. Hence, many researchers have begun to urgently develop Ab-based kits for the detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and Ab drugs for use as COVID-19 therapeutic agents. The detailed structure of the SARS-CoV-2 spike protein is known, and since this protein is key for viral infection, its receptor-binding domain (RBD) has become a major target for therapeutic Ab development. Because SARS-CoV-2 is an RNA virus with a high mutation rate, especially under the selective pressure of aggressively deployed prophylactic vaccines and neutralizing Abs, the use of Ab cocktails is expected to be an important strategy for effective COVID-19 treatment. Moreover, SARS-CoV-2 infection may stimulate an overactive immune response, resulting in a cytokine storm that drives severe disease progression. Abs to combat cytokine storms have also been under intense development as treatments for COVID-19. In addition to their use as drugs, Abs are currently being utilized in SARS-CoV-2 detection tests, including antigen and immunoglobulin tests. Such Ab-based detection tests are crucial surveillance tools that can be used to prevent the spread of COVID-19. Herein, we highlight some key points regarding mAb-based detection tests and treatments for the COVID-19 pandemic. 

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Monoclonal antibodies for COVID-19 therapy and SARS-CoV-2 detection

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The diagnosis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection globally has relied extensively on molecular testing, contributing vitally to case identification, isolation, contact tracing, and rationalization of infection control measures during the coronavirus disease 2019 (COVID-19) pandemic. Clinical laboratories have thus needed to verify newly developed molecular tests and increase testing capacity at an unprecedented rate. As the COVID-19 pandemic continues to pose a global health threat, laboratories continue to encounter challenges in the selection, verification, and interpretation of these tests. This document by the International Federation for Clinical Chemistry and Laboratory Medicine (IFCC) Task Force on COVID-19 provides interim guidance on: (A) clinical indications and target populations, (B) assay selection, (C) assay verification, and (D) test interpretation and limitations for molecular testing of SARS-CoV-2 infection. These evidence-based recommendations will provide practical guidance to clinical laboratories worldwide and highlight the continued importance of laboratory medicine in our collective pandemic response.

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IFCC interim guidelines on molecular testing of SARS-CoV-2 infection

Diagnostic tests have been considered as the main alternative for the control of coronavirus disease (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), as a correct diagnosis allows for decision making when facing the disease, particularly as there is a lack of effective therapeutic protocols and vaccines. Thus, in this review, we summarized the main diagnostic approaches currently available for the diagnosis of SARS-CoV-2 infection in humans based on studies available in article databases. The tests can be organized into two main categories: nucleic acid-based tests, recommended for the initial detection of the virus, and serological tests, recommended for assessing the disease progression. The studies have shown that the performance of diagnostic methods depends on different factors, such as the type of samples and the characteristics of each assay. It was identified that the positivity of the tests is mainly related to the onset of symptoms. We also observed that point-of-care diagnoses are considered as one of the main trends in this area, due to the low-cost and simplicity of the assay; however, the analytical performance must be critically analyzed. Thus, the COVID-19 pandemic has highlighted the critical role of diagnostic technologies in the control of infectious diseases.

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The Main Molecular and Serological Methods for Diagnosing COVID-19: An Overview Based on the Literature.

Laboratory diagnosis of coronavirus disease-2019 (COVID-19).

Using monitoring devices could help avoid injuries and even death. Currently, wearable sensors such as motion sensors and other sensors are used to detect when the patient is having a seizure and alarm their caregivers. However, the development phase of these devices requires labor-intensive work on labeling the collected data, resulting in difficulties in developing wearable monitoring devices. Thus, a more automated auxiliary method of labeling seizure data and a wearable device to detect seizures for daily monitoring use are necessary. We collected data from epileptics outside the hospital with our proposed bracelet. The subjects were asked to press the mark button after they had seizures. We also presented an automatically extraction and annotation of moving segments (EAMS) algorithm to exclude nonmoving segments. Then, we used a two-layer ensemble model (TLEM) using machine learning methods to classify seizures and non-seizure moving segments, which was designed to deal with imbalanced dataset. Then, we build two individual TLEM models separately for the overall (all day and night) seizure detection case and the night seizure detection case, owing to different imbalance of these datasets. The EAMS algorithm exclude 93.9% raw inactive data. The TLEM model achieved 76.84% sensitivity (SEN) and 97.28% accuracy (ACU) for the overall case and achieved 94.57% SEN and 91.37% ACU for the night case. These results indicate that this bracelet can capture seizures efficiently, and our proposed TLEM has higher SEN and ACU than single-layer machine learning models.

https://pure.tue.nl/ws/files/205464982/A_Two_Layer_Ensemble_Method_for_Detecting_Epileptic_Seizures_Using_a_Self_Annotation_Bracelet_With_Motor_Sensors.pdf

A Two-Layer Ensemble Method for Detecting Epileptic Seizures Using a Self-Annotation Bracelet With Motor Sensors

DNA mixture interpretation is one of the most challenging problems in forensics. Complex DNA mixtures are more difficult to analyze when there are more than two contributors or related contributors. Microhaplotypes (MHs) are polymorphic genetic markers recently discovered and employed in DNA mixture analysis. However, the evidentiary interpretation of the MH genotyping data needs more debate. The Random Man Not Excluded (RMNE) method analyzes DNA mixtures without using allelic peak height data or the number of contributors (NoC) assumptions. This study aimed to assess how well RMNE interpreted mixed MH genotyping data. We classified the MH loci from the 1000 Genomes Project database into groups based on their Ae values. Then we performed simulations of DNA mixtures with 2-10 unrelated contributors and DNA mixtures with a pair of sibling contributors. For each simulated DNA mixture, incorrectly included ratios were estimated for three types of non-contributors: random men, parents of contributors, and siblings of contributors. Meanwhile, RMNE probability was calculated for contributors and three types of non-contributors, allowing loci mismatch. The results showed that the MH number, the MH Ae values, and the NoC affected the RMNE probability of the mixture and the incorrectly included ratio of non-contributors. When there were more MHs, MHs with higher Ae values, and a mixture with less NoC, the RMNE probability, and the incorrectly included ratio decreased. The existence of kinship in mixtures complicated the mixture interpretation. Contributors' relatives as non-contributors and related contributors in the mixture increased the demands on the genetic markers to identify the contributors correctly. When 500 highly polymorphic MHs with Ae values higher than 5 were used, the four individual types could be distinguished according to the RMNE probabilities. This study reveals the promising potential of MH as a genetic marker for mixed DNA interpretation and the broadening of RMNE as a parameter indicating the relationship of a specific individual with a DNA mixture in the DNA database search. 

Yunfeng Wang

Papers

Laboratory diagnosis of coronavirus disease-2019 (COVID-19).

A Two-Layer Ensemble Method for Detecting Epileptic Seizures Using a Self-Annotation Bracelet With Motor Sensors

Evaluation of large-scale highly polymorphic microhaplotypes in complex DNA mixtures analysis using RMNE method.