Advanced search in Research products
Research products
arrow_drop_down
Searching FieldsTerms
Any field
arrow_drop_down
includes
arrow_drop_down
Include:
The following results are related to COVID-19. Are you interested to view more results? Visit OpenAIRE - Explore.
86 Research products, page 1 of 9

  • COVID-19
  • Other research products
  • 2013-2022
  • Closed Access
  • Other ORP type
  • English
  • COVID-19

10
arrow_drop_down
Date (most recent)
arrow_drop_down
  • Closed Access English
    Authors: 
    Nardelli, P.; Scandroglio, A.M.; De Piero, M.E.; Mariani, S.; Lorusso, R.;
    Country: Netherlands

    Purpose of reviewCoronavirus disease 2019 (COVID-19) pandemic changed the way we had to approach hospital- and intensive care unit (ICU)-related resource management, especially for demanding techniques required for advanced support, including extracorporeal membrane oxygenation (ECMO).Recent findingsAvailability of ICU beds and ECMO machines widely varies around the world. In critical conditions, such a global pandemic, the establishment of contingency capacity tiers might help in defining to which conditions and subjects ECMO can be offered. A frequent reassessment of the resource saturation, possibly integrated within a regional healthcare coordination system, may be of help to triage the patients who most likely will benefit from advanced techniques, especially when capacities are limited.SummaryIndications to ECMO during the pandemic should be fluid and may be adjusted over time. Candidacy of patients should follow the same prepandemic rules, taking into account the acute disease, the burden of any eventual comorbidity and the chances of a good quality of life after recovery; but the current capacity of healthcare system should also be considered, and frequently reassessed, possibly within a wide hub-and-spoke healthcare system.

  • Closed Access English
    Authors: 
    Sharma, Parvarish; Dhanjal, Daljeet Singh; Chopra, Chirag; Tambuwala, Murtaza M.; Sohal, Sukhwinder Singh; van der Spek, Peter J.; Sharma, Hari S.; Satija, Saurabh;
    Country: Netherlands

    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.

  • Other research product . Other ORP type . 2022
    Closed Access English
    Authors: 
    Kelder, J.M.;
    Country: Netherlands

    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

  • Closed Access English
    Authors: 
    Mohanachandran Nair Sindhu, Swapna;
    Country: Slovenia

    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.

  • Other research product . Other ORP type . 2022
    Closed Access English
    Authors: 
    Mohanachandran Nair Sindhu, Swapna; VIMAL, RAJ; S, Sankararaman;
    Country: Slovenia

    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.

  • Other research product . Other ORP type . 2022
    Closed Access English
    Authors: 
    Swapna, Mohanachandran Nair Sindhu; Sreejyothi, S.; Raj, Vimal; Sankararaman, Sankaranarayana Iyer;
    Country: Slovenia

    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.

  • Closed Access English
    Authors: 
    MOHANACHANDRAN NAIR SINDHU, SWAPNA;
    Country: Slovenia

    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.

  • Closed Access English
    Authors: 
    MOHANACHANDRAN NAIR SINDHU, SWAPNA;
    Country: Slovenia

    This article proposes a unique approach to bring out the potential of graph-based features to reveal the hidden signatures of wet (WE) and dry (DE) cough signals, which are the suggestive symptoms of various respiratory ailments like COVID 19. The spectral and complex network analyses of 115 cough signals are employed for perceiving the airflow dynamics through the infected respiratory tract while coughing. The different phases of WE and DE are observed from their time-domain signals, indicating the operation of the glottis. The wavelet analysis of WE shows a frequency spread due to the turbulence in the respiratory tract. The complex network features namely degree centrality, eigenvector centrality, transitivity, graph density and graph entropy not only distinguish WE and DE but also reveal the associated airflow dynamics. A better distinguishability between WE and DE is obtained through the supervised machine learning techniques (MLTs)—quadratic support vector machine and neural net pattern recognition (NN), when compared to the unsupervised MLT, principal component analysis. The 93.90% classification accuracy with a precision of 97.00% suggests NN as a better classifier using complex network features. The study opens up the possibility of complex network analysis in remote auscultation.

  • Closed Access English
    Authors: 
    MOHANACHANDRAN NAIR SINDHU SWAPNA,, SWAPNA;
    Country: Slovenia

    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.

  • Closed Access English
    Authors: 
    Mohanachandran Nair Sindhu, Swapna; VIMAL, RAJ; A, RENJINI; S, SREEJYOTHI; S, SANKARARMAN;
    Country: Slovenia

    The development of novel digital auscultation techniques has become highly significant in the context of the outburst of the pandemic COVID 19. The present work reports the spectral, nonlinear time series, fractal, and complexity analysis of vesicular (VB) and bronchial (BB) breath signals. The analysis is carried out with 37 breath sound signals. The spectral analysis brings out the signatures of VB and BB through the power spectral density plot and wavelet scalogram. The dynamics of airflow through the respiratory tract during VB and BB are investigated using the nonlinear time series and complexity analyses in terms of the phase portrait, fractal dimension, Hurst exponent, and sample entropy. The higher degree of chaoticity in BB relative to VB is unwrapped through the maximal Lyapunov exponent. The principal component analysis helps in classifying VB and BB sound signals through the feature extraction from the power spectral density data. The method proposed in the present work is simple, cost-effective, and sensitive, with a far-reaching potential of addressing and diagnosing the current issue of COVID 19 through lung auscultation.

Advanced search in Research products
Research products
arrow_drop_down
Searching FieldsTerms
Any field
arrow_drop_down
includes
arrow_drop_down
Include:
The following results are related to COVID-19. Are you interested to view more results? Visit OpenAIRE - Explore.
86 Research products, page 1 of 9
  • Closed Access English
    Authors: 
    Nardelli, P.; Scandroglio, A.M.; De Piero, M.E.; Mariani, S.; Lorusso, R.;
    Country: Netherlands

    Purpose of reviewCoronavirus disease 2019 (COVID-19) pandemic changed the way we had to approach hospital- and intensive care unit (ICU)-related resource management, especially for demanding techniques required for advanced support, including extracorporeal membrane oxygenation (ECMO).Recent findingsAvailability of ICU beds and ECMO machines widely varies around the world. In critical conditions, such a global pandemic, the establishment of contingency capacity tiers might help in defining to which conditions and subjects ECMO can be offered. A frequent reassessment of the resource saturation, possibly integrated within a regional healthcare coordination system, may be of help to triage the patients who most likely will benefit from advanced techniques, especially when capacities are limited.SummaryIndications to ECMO during the pandemic should be fluid and may be adjusted over time. Candidacy of patients should follow the same prepandemic rules, taking into account the acute disease, the burden of any eventual comorbidity and the chances of a good quality of life after recovery; but the current capacity of healthcare system should also be considered, and frequently reassessed, possibly within a wide hub-and-spoke healthcare system.

  • Closed Access English
    Authors: 
    Sharma, Parvarish; Dhanjal, Daljeet Singh; Chopra, Chirag; Tambuwala, Murtaza M.; Sohal, Sukhwinder Singh; van der Spek, Peter J.; Sharma, Hari S.; Satija, Saurabh;
    Country: Netherlands

    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.

  • Other research product . Other ORP type . 2022
    Closed Access English
    Authors: 
    Kelder, J.M.;
    Country: Netherlands

    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

  • Closed Access English
    Authors: 
    Mohanachandran Nair Sindhu, Swapna;
    Country: Slovenia

    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.

  • Other research product . Other ORP type . 2022
    Closed Access English
    Authors: 
    Mohanachandran Nair Sindhu, Swapna; VIMAL, RAJ; S, Sankararaman;
    Country: Slovenia

    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.

  • Other research product . Other ORP type . 2022
    Closed Access English
    Authors: 
    Swapna, Mohanachandran Nair Sindhu; Sreejyothi, S.; Raj, Vimal; Sankararaman, Sankaranarayana Iyer;
    Country: Slovenia

    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.

  • Closed Access English
    Authors: 
    MOHANACHANDRAN NAIR SINDHU, SWAPNA;
    Country: Slovenia

    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.

  • Closed Access English
    Authors: 
    MOHANACHANDRAN NAIR SINDHU, SWAPNA;
    Country: Slovenia

    This article proposes a unique approach to bring out the potential of graph-based features to reveal the hidden signatures of wet (WE) and dry (DE) cough signals, which are the suggestive symptoms of various respiratory ailments like COVID 19. The spectral and complex network analyses of 115 cough signals are employed for perceiving the airflow dynamics through the infected respiratory tract while coughing. The different phases of WE and DE are observed from their time-domain signals, indicating the operation of the glottis. The wavelet analysis of WE shows a frequency spread due to the turbulence in the respiratory tract. The complex network features namely degree centrality, eigenvector centrality, transitivity, graph density and graph entropy not only distinguish WE and DE but also reveal the associated airflow dynamics. A better distinguishability between WE and DE is obtained through the supervised machine learning techniques (MLTs)—quadratic support vector machine and neural net pattern recognition (NN), when compared to the unsupervised MLT, principal component analysis. The 93.90% classification accuracy with a precision of 97.00% suggests NN as a better classifier using complex network features. The study opens up the possibility of complex network analysis in remote auscultation.

  • Closed Access English
    Authors: 
    MOHANACHANDRAN NAIR SINDHU SWAPNA,, SWAPNA;
    Country: Slovenia

    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.

  • Closed Access English
    Authors: 
    Mohanachandran Nair Sindhu, Swapna; VIMAL, RAJ; A, RENJINI; S, SREEJYOTHI; S, SANKARARMAN;
    Country: Slovenia

    The development of novel digital auscultation techniques has become highly significant in the context of the outburst of the pandemic COVID 19. The present work reports the spectral, nonlinear time series, fractal, and complexity analysis of vesicular (VB) and bronchial (BB) breath signals. The analysis is carried out with 37 breath sound signals. The spectral analysis brings out the signatures of VB and BB through the power spectral density plot and wavelet scalogram. The dynamics of airflow through the respiratory tract during VB and BB are investigated using the nonlinear time series and complexity analyses in terms of the phase portrait, fractal dimension, Hurst exponent, and sample entropy. The higher degree of chaoticity in BB relative to VB is unwrapped through the maximal Lyapunov exponent. The principal component analysis helps in classifying VB and BB sound signals through the feature extraction from the power spectral density data. The method proposed in the present work is simple, cost-effective, and sensitive, with a far-reaching potential of addressing and diagnosing the current issue of COVID 19 through lung auscultation.