DeepLungNet: An Effective DL-Based Approach for Lung Disease Classification Using CRIs

Infectious disease-related illness has always posed a concern on a global scale. Each year, pneumonia (viral and bacterial pneumonia), tuberculosis (TB), COVID-19, and lung opacity (LO) cause millions of deaths because they all affect the lungs. Early detection and diagnosis can help create chances for better care in all circumstances. Numerous tests, including molecular tests (RT-PCR), complete blood count (CBC) tests, Monteux tuberculin skin tests (TST), and ultrasounds, are used to detect and classify these diseases. However, these tests take a lot of time, have a 20% mistake rate, and are 80% sensitive. So, with the aid of a doctor, radiographic tests such as computed tomography (CT) and chest radiograph images (CRIs) are used to detect lung disorders. With CRIs or CT-scan images, there is a danger that the features of various lung diseases' diagnoses will overlap. The automation of such a method is necessary to correctly classify diseases using CRIs. The key motivation behind the study was that there is no method for identifying and classifying these (LO, pneumonia, VP, BP, TB, COVID-19) lung diseases. In this paper, the DeepLungNet deep learning (DL) model is proposed, which comprises 20 learnable layers, i.e., 18 convolution (ConV) layers and 2 fully connected (FC) layers. The architecture uses the Leaky ReLU (LReLU) activation function, a fire module, a maximum pooling layer, shortcut connections, a batch normalization (BN) operation, and group convolution layers, making it a novel lung diseases classification framework. This is a useful DL-based method for classifying lung disorders, and we tested the effectiveness of the suggested framework on two datasets with a variety of images from different datasets. We have performed two experiments: a five-class classification (TB, pneumonia, COVID-19, LO, and normal) and a six-class classification (VP, BP, COVID-19, normal, TB, and LO). The suggested framework's average accuracy for classifying lung diseases into TB, pneumonia, COVID-19, LO, and normal using CRIs was an impressive 97.47%. We have verified the performance of our framework on a different publicly accessible database of images from the agriculture sector in order to further assess its performance and validate its generalizability. This study offers an efficient and automated method for classifying lung diseases that aids in the early detection of lung disease. This strategy significantly improves patient survival, possible treatments, and limits the transmission of infectious illnesses throughout society.

Structural insights into the effect of mutations in the spike protein of SARS-CoV-2 on the binding with human furin protein.

The SARS COV-2 and its variants are spreading around the world at an alarming speed, due to its higher transmissibility and the conformational changes caused by mutations. The resulting COVID-19 pandemic has imposed severe health consequences on human health. Several countries of the world including Pakistan have studied its genome extensively and provided productive findings. In the current study, the mCSM, DynaMut2, and I-Mutant servers were used to analyze the effect of identified mutations on the structural stability of spike protein however, the molecular docking and simulations approaches were used to evaluate the dynamics of the bonding network between the wild-type and mutant spike proteins with furin. We addressed the mutational modifications that have occurred in the spike protein of SARS-COV-2 that were found in 215 Pakistani's isolates of COVID-19 patients to study the influence of mutations on the stability of the protein and its interaction with the host cell. We found 7 single amino acid substitute mutations in various domains that reside in spike protein. The H49Y, N74K, G181V, and G446V were found in the S1 domain while the D614A, V622F, and Q677H mutations were found in the central helices of the spike protein. Based on the observation, G181V, G446V, D614A, and V622F mutants were found highly destabilizing and responsible for structural perturbation. Protein-protein docking and molecular simulation analysis with that of furin have predicted that all the mutants enhanced the binding efficiency however, the V622F mutant has greatly altered the binding capacity which is further verified by the KD value (7.1 E-14) and therefore may enhance the spike protein cleavage by Furin and increase the rate of infectivity by SARS-CoV-2. On the other hand, the total binding energy for each complex was calculated which revealed -50.57 kcal/mol for the wild type, for G181V -52.69 kcal/mol, for G446V -56.44 kcal/mol, for D614A -59.78 kcal/mol while for V622F the TBE was calculated to be -85.84 kcal/mol. Overall, the current finding shows that these mutations have increased the binding of Furin for spike protein and shows that D614A and V622F have significant effects on the binding and infectivity.

Structural insights into the effect of mutations in the spike protein of SARS-CoV-2 on the binding with human furin protein

The SARS COV-2 and its variants are spreading around the world at an alarming speed, due to its higher transmissibility and the conformational changes caused by mutations. The resulting COVID-19 pandemic has imposed severe health consequences on human health. Several countries of the world including Pakistan have studied its genome extensively and provided productive findings. In the current study, the mCSM, DynaMut2, and I-Mutant servers were used to analyze the effect of identified mutations on the structural stability of spike protein however, the molecular docking and simulations approaches were used to evaluate the dynamics of the bonding network between the wild-type and mutant spike proteins with furin. We addressed the mutational modifications that have occurred in the spike protein of SARS-COV-2 that were found in 215 Pakistani's isolates of COVID-19 patients to study the influence of mutations on the stability of the protein and its interaction with the host cell. We found 7 single amino acid substitute mutations in various domains that reside in spike protein. The H49Y, N74K, G181V, and G446V were found in the S1 domain while the D614A, V622F, and Q677H mutations were found in the central helices of the spike protein. Based on the observation, G181V, G446V, D614A, and V622F mutants were found highly destabilizing and responsible for structural perturbation. Protein-protein docking and molecular simulation analysis with that of furin have predicted that all the mutants enhanced the binding efficiency however, the V622F mutant has greatly altered the binding capacity which is further verified by the KD value (7.1 E−14) and therefore may enhance the spike protein cleavage by Furin and increase the rate of infectivity by SARS-CoV-2. On the other hand, the total binding energy for each complex was calculated which revealed −50.57 kcal/mol for the wild type, for G181V −52.69 kcal/mol, for G446V −56.44 kcal/mol, for D614A −59.78 kcal/mol while for V622F the TBE was calculated to be −85.84 kcal/mol. Overall, the current finding shows that these mutations have increased the binding of Furin for spike protein and shows that D614A and V622F have significant effects on the binding and infectivity.

Employee associations with R&D investment, firm performance, disruption risk, and supply chain performance during the COVID-19 pandemic: A multiple mediational model

Purpose: Scholars have concentrated their efforts on COVID-19's impact on industries worldwide in order to manage timely supply chain disruptions. Epidemic outbursts are a unique supply chain risk that is distinguished by prolonged disruption propagation, disruption existence, and high uncertainty. The purpose of this study was to investigate the role of R&D investment and firm performance in mediating the relationship between disruption risk and supply chain performance in Pakistani manufacturing industries and supply chain employees during the recovery phase of the COVID-19 pandemic via application of dynamic capability theory. Methodology: From July 21 to August 23, 2020, three hundred and eighteen employees from supply chains of manufacturing industries in Rawalpindi and Islamabad, Pakistan, participated in this cross-sectional online web-based survey. The four standard research scales were used to examine the research and development, disruption risk, firm, and supply chain performance. The response link was distributed to respondents via Facebook, WhatsApp, and email. The data was analyzed using structural equation modelling and a partial least squares technique in the study. Results: The study's findings suggest that disruption risk, research and development investment, and firm performance all improve supply chain performance, but the mediation effect is unsupported by the data. These measures help to plan a better supply chain in the face of disruption risk, and they provide one of the timely empirical conclusions on the role of R&D investment in mitigating risk disruptions and improving supply chain performance

A Holistic Approach to Identify and Classify COVID-19 from Chest Radiographs, ECG, and CT-Scan Images Using ShuffleNet Convolutional Neural Network.

Early and precise COVID-19 identification and analysis are pivotal in reducing the spread of COVID-19. Medical imaging techniques, such as chest X-ray or chest radiographs, computed tomography (CT) scan, and electrocardiogram (ECG) trace images are the most widely known for early discovery and analysis of the coronavirus disease (COVID-19). Deep learning (DL) frameworks for identifying COVID-19 positive patients in the literature are limited to one data format, either ECG or chest radiograph images. Moreover, using several data types to recover abnormal patterns caused by COVID-19 could potentially provide more information and restrict the spread of the virus. This study presents an effective COVID-19 detection and classification approach using the Shufflenet CNN by employing three types of images, i.e., chest radiograph, CT-scan, and ECG-trace images. For this purpose, we performed extensive classification experiments with the proposed approach using each type of image. With the chest radiograph dataset, we performed three classification experiments at different levels of granularity, i.e., binary, three-class, and four-class classifications. In addition, we performed a binary classification experiment with the proposed approach by classifying CT-scan images into COVID-positive and normal. Finally, utilizing the ECG-trace images, we conducted three experiments at different levels of granularity, i.e., binary, three-class, and five-class classifications. We evaluated the proposed approach with the baseline COVID-19 Radiography Database, SARS-CoV-2 CT-scan, and ECG images dataset of cardiac and COVID-19 patients. The average accuracy of 99.98% for COVID-19 detection in the three-class classification scheme using chest radiographs, optimal accuracy of 100% for COVID-19 detection using CT scans, and average accuracy of 99.37% for five-class classification scheme using ECG trace images have proved the efficacy of our proposed method over the contemporary methods. The optimal accuracy of 100% for COVID-19 detection using CT scans and the accuracy gain of 1.54% (in the case of five-class classification using ECG trace images) from the previous approach, which utilized ECG images for the first time, has a major contribution to improving the COVID-19 prediction rate in early stages. Experimental findings demonstrate that the proposed framework outperforms contemporary models. For example, the proposed approach outperforms state-of-the-art DL approaches, such as Squeezenet, Alexnet, and Darknet19, by achieving the accuracy of 99.98 (proposed method), 98.29, 98.50, and 99.67, respectively.

A Novel CovidDetNet Deep Learning Model for Effective COVID-19 Infection Detection Using Chest Radiograph Images

The suspected cases of COVID-19 must be detected quickly and accurately to avoid the transmission of COVID-19 on a large scale. Existing COVID-19 diagnostic tests are slow and take several hours to generate the required results. However, on the other hand, most X-rays or chest radiographs only take less than 15 min to complete. Therefore, we can utilize chest radiographs to create a solution for early and accurate COVID-19 detection and diagnosis to reduce COVID-19 patient treatment problems and save time. For this purpose, CovidDetNet is proposed, which comprises ten learnable layers that are nine convolutional layers and one fully-connected layer. The architecture uses two activation functions: the ReLu activation function and the Leaky Relu activation function and two normalization operations that are batch normalization and cross channel normalization, making it a novel COVID-19 detection model. It is a novel deep learning-based approach that automatically and reliably detects COVID-19 using chest radiograph images. Towards this, a fine-grained COVID-19 classification experiment is conducted to identify and classify chest radiograph images into normal, COVID-19 positive, and pneumonia. In addition, the performance of the proposed novel CovidDetNet deep learning model is evaluated on a standard COVID-19 Radiography Database. Moreover, we compared the performance of our approach with hybrid approaches in which we used deep learning models as feature extractors and support vector machines (SVM) as a classifier. Experimental results on the dataset showed the superiority of the proposed CovidDetNet model over the existing methods. The proposed CovidDetNet outperformed the baseline hybrid deep learning-based models by achieving a high accuracy of 98.40%.

Access and Use Experience of Personal Protective Equipment Among Frontline Healthcare Workers in Pakistan During the COVID-19 Emergency: A Cross-Sectional Study.

Healthcare workers are at the highest risk of contracting novel coronavirus disease 2019 (COVID-19) and, therefore, require constant protection. This study assesses access to personal protective equipment (PPE), availability of adequate information about PPE use, self-reported ability to correctly wear and remove (donning and doffing) PPE, and risk perceptions associated with COVID-19 among frontline healthcare workers in Pakistan. Using a structured and validated questionnaire, an online survey was conducted from May 9 to June 5, 2020. Responses were received from 453 healthcare workers. Of these, 218 (48.12%) were doctors, 183 (40.40%) were nurses, and 52 (11.48%) were paramedical staff. Only 129 (28.48%) healthcare workers reported having adequate access to PPE at all times, whereas 156 (34.44%) never had access to PPE and 168 (37.09%) had access to PPE occasionally. Lack of access to PPE led the majority (71.74%) of healthcare workers to use coping strategies such as reuse of N95 and surgical masks. A total of 312 (68.87%) respondents believed that the risk of contracting COVID-19 in the work environment was high and the majority (62.69%) adopted precautionary measures at home to keep their families safe. A significantly high (n = 233, 51.43%, P = .03) number of respondents reported self-medicating. Of all the respondents, only 136 (30.02%) were tested for COVID-19 at least once, of which 32 (23.53%) ever tested positive. These findings suggest that healthcare workers in Pakistan had limited access to PPE. Adequate provision and training is vital to protect the healthcare workforce during the COVID-19 pandemic.