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SARS-CoV-2 viruses engage ACE2 as a functional receptor with their spike protein. The S1 domain of the spike protein contains a C-terminal receptor-binding domain (RBD) and an N-terminal domain (NTD). The NTD of other coronaviruses includes a glycan-binding cleft. However, for the SARS-CoV-2 NTD protein-glycan binding was only observed weakly for sialic acids with highly sensitive methods. Amino acid changes in the NTD of Variants of Concern (VoC) shows antigenic pressure, which can be an indication of NTD-mediated receptor binding. Trimeric NTD proteins of SARS-CoV-2, Alpha, Beta, Delta, and Omicron did not reveal a receptor binding capability. Unexpectedly, the SARS-CoV-2 Beta subvariant strain (501Y.V2-1) NTD binding to Vero E6 cells was sensitive to sialidase pretreatment. Glycan microarray analyses identified a putative 9- O -acetylated sialic acid as a ligand, which was confirmed by catch-and-release ESI-MS, STD-NMR analyses, and a graphene-based electrochemical sensor. The Beta (501Y.V2-1) variant attained an enhanced glycan binding modality in the NTD with specificity towards 9- O -acetylated structures, suggesting a dual-receptor functionality of the SARS-CoV-2 S1 domain, which was quickly selected against. These results indicate that SARS-CoV-2 can probe additional evolutionary space, allowing binding to glycan receptors on the surface of target cells. Graphical abstract: Synopsis: Coronaviruses utilize their N-terminal domain (NTD) for initial reversible low-affinity interaction to (sialylated) glycans. This initial low-affinity/high-avidity engagement enables viral surfing on the target membrane, potentially followed by a stronger secondary receptor interaction. Several coronaviruses, such as HKU1 and OC43, possess a hemagglutinin-esterase for viral release after sialic acid interaction, thus allowing viral dissemination. Other coronaviruses, such as MERS-CoV, do not possess a hemagglutinin-esterase, but interact reversibly to sialic acids allowing for viral surfing and dissemination. The early 501Y.V2-1 subvariant of the Beta SARS-CoV-2 Variant of Concern has attained a receptor-binding functionality towards 9- O -acetylated sialic acid using its NTD. This binding functionality was selected against rapidly, most likely due to poor dissemination. Ablation of sialic acid binding in more recent SARS-CoV-2 Variants of Concern suggests a fine balance of sialic acid interaction of SARS-CoV-2 is required for infection and/or transmission.

Asthma, COPD, COVID-19, EGPA, Lung cancer, and Pneumonia are major chronic respiratory diseases (or CRDs) affecting millions worldwide and account for substantial morbidity and mortality. These CRDs are irreversible diseases that affect different parts of the respiratory system, imposing a considerable burden on different socio-economic classes. All these CRDs have been linked to increased eosinophils in the lungs. Eosinophils are essential immune mediators that contribute to tissue homeostasis and the pathophysiology of various diseases. Interestingly, elevated eosinophil level is associated with cellular processes that regulate airway hyperresponsiveness, airway remodeling, mucus hypersecretion, and inflammation in the lung. Therefore, eosinophil is considered the therapeutic target in eosinophil-mediated lung diseases. Although, conventional medicines like antibiotics, anti-inflammatory drugs, and bronchodilators are available to prevent CRDs. But the development of resistance to these therapeutic agents after long-term usage remains a challenge. However, progressive development in nanotechnology has unveiled the targeted nanocarrier approach that can significantly improve the pharmacokinetics of a therapeutic drug. The potential of the nanocarrier system can be specifically targeted on eosinophils and their associated components to obtain promising results in the pharmacotherapy of CRDs. This review intends to provide knowledge about eosinophils and their role in CRDs. Moreover, it also discusses nanocarrier drug delivery systems for the targeted treatment of CRDs.

From an Ancient Egyptian plague to the Black Death and Spanish flu, epidemics have often spurred societal transformations. Understanding why can help us create a better world after covid-19

Cough signal analysis for understanding the pathological condition has become important from the outset of the exigency posed by the epidemic COVID-19. The present work suggests a surrogate approach for the classification of cough signals - croup cough (CC) and pertussis (PT) – based on spectral, fractal, and nonlinear time-series techniques. The spectral analysis of CC reveals the presence of more frequency components in the short duration cough sound compared to PT. The musical nature of CC is unveiled not only through the spectral analysis but also through the phase portrait features – sample entropy (S), maximal Lyapunov exponent (L), and Hurst exponent (Hb). The modifications in the internal morphology of the respiratory tract, giving rise to more frequency components associated with the complex airflow dynamics, get staged through the higher fractal dimension of CC. Among the two supervised classification tools, cubic KNN (CKNN) and neural net pattern recognition (NNPR), used for classifying the CC and PT signals based on nonlinear time series parameters, NNPR is found better. Thus, the study opens the possibility of identification of pulmonary pathological conditions through cough sound signal analysis.

The current COVID-19 pandemic has been affecting the body im age of individuals as well as their practice of physical exercise and their consumption of image- and performance-enhancing drugs (IPEDs) in an attempt to boost their appearance.

The literature has been showing evidence about the impact of restrictive mea sures during the COVID-19 pandemic on different psychological variables and behaviours. This study aims to assess the contribution of anxiety about appearance on the practice of physical exercise and use of image- and performance-enhancing drugs (IPEDs) during the COVID-19 lockdown.

The paper proposes a graph-theoretical approach to auscultation, bringing out the potential of graph features in classifying the bioacoustics signals. The complex network analysis of the bioacoustics signals - vesicular (VE) and bronchial (BR) breath sound - of 48 healthy persons are carried out for understanding the airflow dynamics during respiration. The VE and BR are classified by the machine learning techniques extracting the graph features – the number of edges (E), graph density (D), transitivity (T), degree centrality (Dcg) and eigenvector centrality (Ecg). The higher value of E, D, and T in BR indicates the temporally correlated airflow through the wider tracheobronchial tract resulting in sustained high-intense low-frequencies. The frequency spread and high-frequencies in VE, arising due to the less correlated airflow through the narrow segmental bronchi and lobar, appears as a lower value for E, D, and T. The lower values of Dcg and Ecg justify the inferences from the spectral and other graph parameters. The study proposes a methodology in remote auscultation that can be employed in the current scenario of COVID-19.

Objectives: The present work reports the study of 34 rhonchi (RB) and Bronchial Breath (BB) signals employing machine learning techniques, timefrequency, fractal, and non-linear time-series analyses. Methods: The timefrequency analyses and the complexity in the dynamics of airflow in BB and RB are studied using both Power Spectral Density (PSD) features and non-linear measures. For accurate prediction of these signals, PSD and nonlinear measures are fed as input attributes to various machine learning models. Findings: The spectral analyses reveal fewer, low-intensity frequency components along with its overtones in the intermittent and rapidly damping RB signal. The complexity in the dynamics of airflow in BB and RB is investigated through the fractal dimension, Hurst exponent, phase portrait, maximal Lyapunov exponent, and sample entropy values. The greater value of entropy for the RB signal provides an insight into the internal morphology of the airways containing mucous and other obstructions. The Principal Component Analysis (PCA) employs PSD features, and Linear Discriminant Analysis (LDA) along with Pattern Recognition Neural Network (PRNN) uses non-linear measures for predicting BB and RB. Signal classification based on phase portrait features evaluates the multidimensional aspects of signal intensities, whereas that based on PSD features considers mere signal intensities. The principal components in PCA cover about 86.5% of the overall variance of the data class, successfully distinguishing BB and RB signals. LDA and PRNN that use nonlinear time-series parameters identify and predict RB and BB signals with 100% accuracy, sensitivity, specificity, and precision. Novelty: The study divulges the potential of non-linear measures and PSD features in classifying these signals enabling its application to be extended for low-cost, non-invasive COVID-19 detection and real-time health monitoring.

A first report of unveiling the fractality and fractal nature of severe acute respiratory syndrome coronavirus (SARS CoV-2) responsible for the pandemic disease widely known as coronavirus disease 2019 (COVID 19) is presented. The fractal analysis of the electron microscopic and atomic force microscopic images of 40 coronaviruses (CoV), by the normal and differential box-counting method, reveals its fractal structure. The generalised dimension indicates the multifractal nature of the CoV. The higher value of fractal dimension and lower value of Hurst exponent (H) suggest higher complexity and greater roughness. The statistical analysis of generalised dimension and H is understood through the notched box plot. The study on CoV clusters also confirms its fractal nature. The scale-invariant value of the box-counting fractal dimension of CoV yields a value of 1.820. The study opens the possibility of exploring the potential of fractal analysis in the medical diagnosis of SARS CoV-2.

The paper proposes a novel approach to bring out the potential of complex networks based on graph theory to unwrap the hidden characteristics of cough signals, croup (BC), and pertussis (PS). The spectral and complex network analyses of 48 cough sounds are utilized for understanding the airflow through the infected respiratory tract. Among the different phases of the cough sound time-domain signals of BC and PS – expulsive (X), intermediate (I), and voiced (V) - the phase ‘I’ is noisy in BC due to improper glottal functioning. The spectral analyses reveal high-frequency components in both cough signals with an additional high-intense low-frequency spread in BC. The complex network features created by the correlation mapping approach, like number of edges (E), graph density (G), transitivity (), degree centrality (D), average path length (L), and number of components () distinguishes BC and PS. The higher values of E, G, and for BC indicate its musical nature through the strong correlation between the signal segments and the presence of high-intense low-frequency components in BC, unlike that in PS. The values of D, L, and discriminate BC and PS in terms of the strength of the correlation between the nodes within them. The linear discriminant analysis (LDA) and quadratic support vector machine (QSVM) classifies BC and PS, with greater accuracy of 94.11% for LDA. The proposed work opens up the potentiality of employing complex networks for cough sound analysis, which is vital in the current scenario of COVID-19.