Biosensors and nanoscale analytical tools have shown huge growth in literature in the past 20 years, with a large number of reports on the topic of 'ultrasensitive', 'cost-effective', and 'early detection' tools with a potential of 'mass-production' cited on the web of science Yet none of these tools are commercially available in the market or practically viable for mass production and use in pandemic diseases such as coronavirus disease 2019 (COVID-19) In this context, we review the technological challenges and opportunities of current bio/chemical sensors and analytical tools by critically analyzing the bottlenecks which have hindered the implementation of advanced sensing technologies in pandemic diseases We also describe in brief COVID-19 by comparing it with other pandemic strains such as that of severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS) for the identification of features that enable biosensing Moreover, we discuss visualization and characterization tools that can potentially be used not only for sensing applications but also to assist in speeding up the drug discovery and vaccine development process Furthermore, we discuss the emerging monitoring mechanism, namely wastewater-based epidemiology, for early warning of the outbreak, focusing on sensors for rapid and on-site analysis of SARS-CoV2 in sewage To conclude, we provide holistic insights into challenges associated with the quick translation of sensing technologies, policies, ethical issues, technology adoption, and an overall outlook of the role of the sensing technologies in pandemics

Opportunities and Challenges for Biosensors and Nanoscale Analytical Tools for Pandemics: COVID-19.

A Light CNN for detecting COVID-19 from CT scans of the chest

Diagnosis is a critical preventive step in Coronavirus research which has similar manifestations with other types of pneumonia. CT scans and X-rays play an important role in that direction. However, processing chest CT images and using them to accurately diagnose COVID-19 is a computationally expensive task. Machine Learning techniques have the potential to overcome this challenge. This article proposes two optimization algorithms for feature selection and classification of COVID-19. The proposed framework has three cascaded phases. Firstly, the features are extracted from the CT scans using a Convolutional Neural Network (CNN) named AlexNet. Secondly, a proposed features selection algorithm, Guided Whale Optimization Algorithm (Guided WOA) based on Stochastic Fractal Search (SFS), is then applied followed by balancing the selected features. Finally, a proposed voting classifier, Guided WOA based on Particle Swarm Optimization (PSO), aggregates different classifiers’ predictions to choose the most voted class. This increases the chance that individual classifiers, e.g. Support Vector Machine (SVM), Neural Networks (NN), k-Nearest Neighbor (KNN), and Decision Trees (DT), to show significant discrepancies. Two datasets are used to test the proposed model: CT images containing clinical findings of positive COVID-19 and CT images negative COVID-19. The proposed feature selection algorithm (SFS-Guided WOA) is compared with other optimization algorithms widely used in recent literature to validate its efficiency. The proposed voting classifier (PSO-Guided-WOA) achieved AUC (area under the curve) of 0.995 that is superior to other voting classifiers in terms of performance metrics. Wilcoxon rank-sum, ANOVA, and T-test statistical tests are applied to statistically assess the quality of the proposed algorithms as well.

/pdf/novel-feature-selection-and-voting-classifier-algorithms-for-1d4zhbjlhn.pdf

Novel Feature Selection and Voting Classifier Algorithms for COVID-19 Classification in CT Images

Introduction; Pulmonary fibrosis is a frequently reported COVID-19 sequela in which the exact prevalence and risk factors are yet to be established. This meta-analysis aims to investigate the prevalence of post-COVID-19 pulmonary fibrosis (PCPF) and the potential risk factors. Methods; CINAHL, PubMed/MEDLINE, Cochrane Library, Web of Science, and EMBASE databases were searched to identify English language studies published up to December 3, 2021. Results; The systematic search initially revealed a total of 618 articles - of which only 13 studies reporting 2018 patients were included in this study. Among the patients, 1047 (51.9%) were male and 971 (48.1%) were female. The mean age was 54.5 years (15-94). The prevalence of PCPF was 44.9%. The mean age was 59 years in fibrotic patients and 48.5 years in non-fibrotic patients. Chronic obstructive pulmonary disease was the only comorbidity associated with PCPF. Fibrotic patients more commonly suffered from persistent symptoms of dyspnea, cough, chest pain, fatigue, and myalgia (p-value < 0.05). Factors related to COVID-19 severity that were associated with PCPF development included computed tomography score of ≥18, ICU admission, invasive/non-invasive mechanical ventilation, longer hospitalization period, and steroid, antibiotic and immunoglobulin treatments (p-value < 0.05). Parenchymal bands (284/341), ground-glass opacities (552/753), interlobular septal thickening (220/381), and consolidation (197/319) were the most common lung abnormalities found in fibrotic patients. Conclusion, About 44.9% of COVID-19 survivors appear to have developed pulmonary fibrosis. Factors related to COVID-19 severity were significantly associated with PCPF development. 

https://doi.org/10.1016/j.amsu.2022.103590

Post COVID-19 pulmonary fibrosis; a meta-analysis study

Pneumonia is one of the foremost lung diseases and untreated pneumonia will lead to serious threats for all age groups. The proposed work aims to extract and evaluate the Coronavirus disease (COVID-19) caused pneumonia infection in lung using CT scans. We propose an image-assisted system to extract COVID-19 infected sections from lung CT scans (coronal view). It includes following steps: (i) Threshold filter to extract the lung region by eliminating possible artifacts; (ii) Image enhancement using Harmony-Search-Optimization and Otsu thresholding; (iii) Image segmentation to extract infected region(s); and (iv) Region-of-interest (ROI) extraction (features) from binary image to compute level of severity. The features that are extracted from ROI are then employed to identify the pixel ratio between the lung and infection sections to identify infection level of severity. The primary objective of the tool is to assist the pulmonologist not only to detect but also to help plan treatment process. As a consequence, for mass screening processing, it will help prevent diagnostic burden.

Harmony-Search and Otsu based System for Coronavirus Disease (COVID-19) Detection using Lung CT Scan Images.

The role of CT in case ascertainment and management of COVID-19 pneumonia in the UK: insights from high-incidence regions.

Complications after discharge with COVID-19 infection and risk factors associated with development of post-COVID pulmonary fibrosis.

Introduction: COVID-19 arrived as a new disease entity to the UK in early 2020. As one of the tertiary hospitals to admit COVID-19 patients, we collected data in order to characterise patients Method: Data was collected on 771 patients admitted with confirmed COVID-19 during the first 3 months of the pandemic, who had already been discharged or died. Presenting symptoms, co-morbidities, frailty, observations, ethnicity and mortality were noted. Results: The median age was 72 with a median clinical frailty score (CFS) and Charlson co-morbidity index (CCI) of 4. The most frequently reported symptoms were fever(62%), cough(60%) and breathlessness (57%) but older patients had more presentations with falls (34%) and confusion (37%). Older patients presented earlier compared with younger patients (2 vs 7 days). Patients with a CCI of 0 had a median age of 38 and frailty score of 2, whilst patients with CCI ≥5 had a median age of 82 and frailty score of 6. The most common co-morbidities were hypertension(42%), diabetes mellitus (31%)and chronic respiratory disease(16%). Data on ethnicity and mortality is undergoing further analysis Conclusion: This is a large single site cohort of COVID-19 patients. Older patients presented sooner after symptom onset. They were more likely to present with falls and confusion and have a higher frailty score and co-morbidity index. Further multivariate analysis is ongoing to examine additional variables.

Late Breaking Abstract - Clinical variation in presenting characteristics of COVID-19 at St George's Hospital, London

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The role of CT in case ascertainment and management of COVID-19 pneumonia in the UK: insights from high-incidence regions.

Complications after discharge with COVID-19 infection and risk factors associated with development of post-COVID pulmonary fibrosis.

Late Breaking Abstract - Clinical variation in presenting characteristics of COVID-19 at St George's Hospital, London