Diagnosis of COVID-19 using chest X-ray images based on modified DarkCovidNet model.

Coronavirus disease, also known as COVID-19, is an infectious disease caused by SARS-CoV-2. It has a direct impact on the upper and lower respiratory tract and threatened the health of many people around the world. The latest statistics show that the number of people diagnosed with COVID-19 is growing exponentially. Diagnosing positive cases of COVID-19 is important for preventing further spread of the disease. Currently, Coronavirus is a serious threat to scientists, medical experts and researchers around the world from its detection to its treatment. It is currently detected using reverse transcription polymerase chain reaction (RT-PCR) analysis at the most test centers around the world. Yet, knowing the reliability of a deep learning based medical diagnosis is important for doctors to build confidence in the technology and improve treatment. The goal of this study is to develop a model that automatically identifies COVID-19 by using chest X-ray images. To achieve this, we modified the DarkCovidNet model which is based on a convolutional neural network (CNN) and plotted the experimental results for two scenarios: binary classification (COVID-19 versus No-findings) and multi-class classification (COVID-19 versus pneumonia versus No-findings). The model is trained on more than 10 thousand X-ray images and achieved an average accuracy of 99.53% and 94.18% for binary and multi-class classification, respectively. Therefore, the proposed method demonstrates the effectiveness of COVID-19 detection using X-ray images. Our model can be used to test the patient via cloud and also be used in situations where RT-PCR tests and other options aren't available.

Identification and Engineering of Aptamers for Theranostic Application in Human Health and Disorders.

An aptamer is a short sequence of synthetic oligonucleotides which bind to their cognate target, specifically while maintaining similar or higher sensitivity compared to an antibody. The in-vitro selection of an aptamer, applying a conjoining approach of chemistry and molecular biology, is referred as Systematic Evolution of Ligands by Exponential enrichment (SELEX). These initial products of SELEX are further modified chemically in an attempt to make them stable in biofluid, avoiding nuclease digestion and renal clearance. While the modification is incorporated, enough care should be taken to maintain its sensitivity and specificity. These modifications and several improvisations have widened the window frame of aptamer applications that are currently not only restricted to in-vitro systems, but have also been used in molecular imaging for disease pathology and treatment. In the food industry, it has been used as sensor for detection of different diseases and fungal infections. In this review, we have discussed a brief history of its journey, along with applications where its role as a therapeutic plus diagnostic (theranostic) tool has been demonstrated. We have also highlighted the potential aptamer-mediated strategies for molecular targeting of COVID-19. Finally, the review focused on its future prospective in immunotherapy, as well as in identification of novel biomarkers in stem cells and also in single cell proteomics (scProteomics) to study intra or inter-tumor heterogeneity at the protein level. Small size, chemical synthesis, low batch variation, cost effectiveness, long shelf life and low immunogenicity provide advantages to the aptamer over the antibody. These physical and chemical properties of aptamers render them as a strong biomedical tool for theranostic purposes over the existing ones. The significance of aptamers in human health was the key finding of this review.

A novel G-quadruplex aptamer-based spike trimeric antigen test for the detection of SARS-CoV-2.

The recent SARS-CoV-2 outbreak has been declared a global health emergency. It will take years to vaccinate the whole population to protect them from this deadly virus, hence the management of SARS-CoV-2 largely depends on the widespread availability of an accurate diagnostic test. Toward addressing the unmet need of a reliable diagnostic test in the current work by utilizing the power of Systematic Evolution of Ligands by EXponential enrichment, a 44-mer G-quadruplex-forming DNA aptamer against spike trimer antigen of SARS-CoV-2 was identified. The lead aptamer candidate (S14) was characterized thoroughly for its binding, selectivity, affinity, structure, and batch-to-batch variability by utilizing various biochemical, biophysical, and in silico techniques. S14 has demonstrated a low nanomolar KD, confirming its tight binding to a spike antigen of SARS-CoV-2. S14 can detect as low as 2 nM of antigen. The clinical evaluation of S14 aptamer on nasopharyngeal swab specimens (n = 232) has displayed a highly discriminatory response between SARS-CoV-2 infected individuals from the non-infected one with a sensitivity and specificity of ∼91% and 98%, respectively. Importantly, S14 aptamer-based test has evinced a comparable performance with that of RT-PCR-based assay. Altogether, this study established the utility of aptamer technology for the detection of SARS-CoV-2.

Wuhan to World: The COVID-19 Pandemic.

COVID-19 is a Severe Acute Respiratory Syndrome (SARS), caused by SARS-CoV-2, a novel virus which belongs to the family Coronaviridae. It was first reported in December 2019 in the Wuhan city of China and soon after, the virus and hence the disease got spread to the entire world. As of February 26, 2021, SARS-CoV-2 has infected ~112.20 million people and caused ~2.49 million deaths across the globe. Although the case fatality rate among SARS-CoV-2 patient is lower (~2.15%) than its earlier relatives, SARS-CoV (~9.5%) and MERS-CoV (~34.4%), the SARS-CoV-2 has been observed to be more infectious and caused higher morbidity and mortality worldwide. As of now, only the knowledge regarding potential transmission routes and the rapidly developed diagnostics has been guiding the world for managing the disease indicating an immediate need for a detailed understanding of the pathogen and the disease-biology. Over a very short period of time, researchers have generated a lot of information in unprecedented ways in the key areas, including viral entry into the host, dominant mutation, potential transmission routes, diagnostic targets and their detection assays, potential therapeutic targets and drug molecules for inhibiting viral entry and/or its replication in the host including cross-neutralizing antibodies and vaccine candidates that could help us to combat the ongoing COVID-19 pandemic. In the current review, we have summarized the available knowledge about the pathogen and the disease, COVID-19. We believe that this readily available knowledge base would serve as a valuable resource to the scientific and clinical community and may help in faster development of the solution to combat the disease.

Visual Detection of SARS-CoV-2 RNA by Conventional PCR-Induced Generation of DNAzyme Sensor.

The gold standard for the diagnosis of SARS-CoV-2, the causative agent of COVID-19, is real-time polymerase chain reaction (PCR), which is labor-intensive, expensive, and not widely available in resource-poor settings. Therefore, it is imperative to develop novel, accurate, affordable, and easily accessible assays/sensors to diagnose and isolate COVID-19 cases. To address this unmet need, we utilized the catalytic potential of peroxidase-like DNAzyme and developed a simple visual detection assay for SARS-CoV-2 RNA using a conventional thermal cycler by the PCR-induced generation of DNAzyme sensor. The performance of RT-PCR DNAzyme-based sensor was comparable to that of real-time PCR. The pilot scale validation of RT-PCR DNAzyme-based sensor has shown ~100% sensitivity and specificity in clinical specimens (nasopharyngeal swab, n = 34), with a good correlation (Spearman r = 0.799) with the Ct-value of fluorescence probe-based real-time PCR. These findings clearly indicate the potential of this inexpensive, sensitive, and specific molecular diagnostic test to extend our testing capabilities for the detection of SARS-CoV-2 to curtail COVID-19 transmission.

Data Analytics and Mathematical Modeling for Simulating the Dynamics of COVID-19 Epidemic—A Case Study of India

The global explosion of the COVID-19 pandemic has created worldwide unprecedented health and economic challenges which stimulated one of the biggest annual migrations globally. In the Indian context, even after proactive decisions taken by the Government, the continual growth of COVID-19 raises questions regarding its extent and severity. The present work utilizes the susceptible-infected-recovered-death (SIRD) compartment model for parameter estimation and fruitful prediction of COVID-19. Further, various optimization techniques such as particle swarm optimization (PSO), gradient (G), pattern search (PS) and their hybrid are employed to solve the considered model. The simulation study endorse the efficiency of PSO (with or without G) and G+PS+G over other techniques for ongoing pandemic assessment. The key parametric values including characteristic time of infection and death and reproduction number have been estimated as 60 days, 67 days and 4.78 respectively by utilizing the optimum results. The model assessed that India has passed its peak duration of COVID-19 with more than 81% recovery and only a 1.59% death rate. The short duration analysis (15 days) of obtained results against reported data validates the effectiveness of the developed models for ongoing pandemic assessment.