ABSTRACT
It is well known that the symptoms of Coronavirus disease (COVID) and common pneumonia (CP) disease are very similar though the first one often leads to severe complications and may even be fatal. Hence, it is of vital importance to be able to correctly distinguish between the two. This paper attempts to achieve this task using whole 3-D CT scans of lungs. A number of models have been experimented with, using convolutional and radiomic features as well as their concatenations, and different classifiers (MLP and Random Forest) with two different sizes of input CT images (50 × 128 × 128 and 25 × 256 × 256 ) and their performances have been compared. The most significant contribution of this work is the postulation of a 3-D dual-scale framework using CT scans, employing both intra-scale and inter-scale information, thereby achieving performance scores which are much higher than the state of the art methods to distinguish between COVID-19 and CP using lung CT scans. Specifically, Accuracy of 98.67% and Receiver Operating Characteristics-Area Under The Curve (AUC) of 99% are worth mentioning. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Global public health will be severely impacted by the successive waves of emerging COVID-19 disease. Since 2019 people get sick and die in our daily lives placing a massive burden on our health system. One of the crucial factors that has led to the virus's fast spread is a protracted clinical testing gap before discovering of a positive or negative result. A detection system based on deep learning was developed by using chest X-ray(CXR) images of Covid19 patient and healthy people. In this regard the Convolution Neural Network along with other DNNs have been proved to produce good results. To improve the COVID-19 detection accuracy, we developed model using the deep learning(CNN) approach where we observed an accuracy of 96%. We validated the accuracy by using same dataset through a pretrained VGG16 model and an LSTM model which produced excellent reliable results. Our aim of this research is to implement a reliable Deep Learning model to detect presence of Covid-19 in case of limited availability of chest-Xray images.
ABSTRACT
Background. Tuberculosis (TB) is a chronic granulomatous inflammation usually involving the lung parenchyma and hilar lymph nodes. Extra-pulmonary involvement is seen in ~20% of all TB cases. Developing tuberculosis following treatment for another primary medical condition is a rare occurrence. Iatrogenic literally means illness caused by medical examination or treatment. Mycobacteria have been used to treat a few medical conditions and the therapeutic use has been validated extensively in literature. The mycobacteria used for therapeutic purposes are supposed to be either attenuated or non pathogenic strains. Growth and dissemination of this mycobacterium is a rare but serious possibility. Reactivation of latent mycobacterial infection following therapy is also a part of iatrogenic mycobacterial infection. Methods. We report a retrospective study investigating adverse events manifesting with development of iatrogenic tuberculosis. The data was obtained from a tertiary care centre over a period of 2 years. Diagnosis of tuberculosis was established based on clinical, radiological(chest X-Ray and High resolution CT scan), sputum smear microscopy and skin/tissue biopsy and Xpert MTB/RIF. Tuberculin skin test(TST) and Interferon gamma release assay (IGRA) were used as additional diagnostic tests. Results. The search yielded 26 cases of iatrogenic tuberculosis, with a median age of 56.5 years. Most common cause was reactivation of latent tuberculosis due to use of anti-TNF alpha biologic agents(53.8%). Development of Tuberculosis verrucosa cutis following BCG inoculation for verruca vulgaris was noted in about 19.2% of cases and Tubercular abscess due to Mycobacterium w or BCG inoculation for Covid-19 was noted in 11.5% cases. Bacillus Calmette Guerin(BCG) induced balanitis secondary to therapy for Carcinoma urinary bladder was noted in 7.69%of cases. Bacillus Calmette-Guerin or BCG induced balanitis following use of intravesical immunotherapy for treating early-stage urinary bladder cancer and successful clearance of lesions post therapy Tuberculosis Verrucosa Cutis following Immunotherapy for plantar warts Conclusion. All patients were managed successfully with anti tubercular therapy. These observations indicate that tuberculosis infection can develop in previously healthy individuals in endemic zones following biologic therapy of use of mycobacterial agents for other therapeutic indications.
ABSTRACT
Current COVID-19 effects are forcing us to think about other deadly viral diseases. Respiratory syncytial virus (RSV) is one of them. Every year thousands of children lost their lives due to respiratory diseases which are occurred by this RSV. Nowadays, bioactive compounds show an enormous effect on many deadly diseases and show excellent therapeutic effects. In this study, we have identified five bioactive compounds from the plant which will be used in the treatment of RSV. Molecular docking on the protein was done by Autodock. Hydrogen was added and routable bonds were fixed in the preparation time of protein for docking. All those compounds show their non-toxic nature which is evaluated by Lipinski's Rule of Five. Molecular docking on RSV matrix protein and surface glycoprotein with those bioactive compounds shows very promising results. Between all those compounds Baicalein appears as a lead compound. It shows -8.1 Kcal/mol in the case of matrix protein and -7.9 kcal/mol in the case of the surface glycoprotein of RSV. Due to its availability and non-toxic nature, it can be used in the treatment of RSV. AS it is derived from plants, it also has very fewer side effects than chemical drugs.
ABSTRACT
The current scenario of COVID-19 makes us to think about the devastating diseases that kill so many people every year. Analysis of viral proteins contributes many things that are utterly useful in the evolution of therapeutic drugs and vaccines. In this study, sequence and structure of fusion glycoproteins and major surface glycoproteins of respiratory syncytial virus (RSV) were analysed to reveal the stability and transmission rate. RSV A has the highest abundance of aromatic residues. The Kyte–Doolittle scale indicates the hydrophilic nature of RSV A protein which leads to the higher transmission rate of this virus. Intra-protein interactions such as carbonyl interactions, cation–pi, and salt bridges were shown to be greater in RSV A compared to RSV B, which might lead to improved stability. This study discovered the presence of a network aromatic–sulphur interaction in viral proteins. Analysis of ligand binding pocket of RSV proteins indicated that drugs are performing better on RSV B than RSV A. It was also shown that increasing the number of tunnels in RSV A proteins boosts catalytic activity. This study will be helpful in drug discovery and vaccine development. © 2022, Jiangnan University.
ABSTRACT
Due to unavailability of FDA approved drug for COVID-19, pursuing of available drugs are highlighted to stop COVID-19. Insilico investigation by molecular docking and molecular dynamics simulation help to identify some FDA pre-approved drugs which have a therapeutic effect on SARS-CoV-2. In this study, four drug compounds have been identified by descriptor properties, molecular docking, and molecular dynamics simulation. Between them, Darunavir appeared as the best drug molecule to inhibit the 3C like main protease of SARS-CoV-2. It showed −9.1 kcal/mol binding energy in molecular docking with 3C like main protease of SARS-CoV-2. This study also enlightens on the theory “one molecule, multiple targets”. Multiple target protein was docked by every single drug compound, to check their high therapeutic effect. Molecular dynamics simulations indicate the stable binding of drugs with the target protein. Until the approval of any drug for COVID-19, Darunavir might use as an anti-covid drug. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Background: The emergence of mucormycosis cases amid the COVID-19 pandemic;fear associated with mucormycosis may turn out to be a terrifying public health issue. This study aimed to assess the association between fear and insomnia status and other predictors of mucormycosis among Bangladeshi healthcare workers. Methods: From May 25, 2021 to June 5, 2021, a cross-sectional study was carried out among healthcare workers. A total of 422 healthcare workers participated in this study. A semi-structured online questionnaire was used for data collection during the COVID-19 pandemic, followed by convenient and snowball sampling methods. A multivariable linear regression model was fitted to assess the association between fear and insomnia status and other predictors of mucormycosis. Results: The results indicated that the respondents with insomnia status had a higher score of mucormycosis fear than not having insomnia, significantly (β = 3.91, 95% CI: 2.49, 5.33, p < 0.001). Alongside, with the increase knowledge score of mucormycosis, the average fear score increased, significantly (β = 0.35, 95% CI: 0.20, 0.50, p < 0.001). The gender, profession, and death of friends and family members due to COVID-19 significantly affected mucormycosis fear increment. Conclusion: This is the first study that focused on assessing the association between mucormycosis fear and insomnia status among the healthcare workers so far. The study findings recommend emphasizing on the mental health aspects and ensuring support to the healthcare workers so that they can tackle the ongoing public health crisis smoothly. © 2021
ABSTRACT
The nationwide lockdown was implemented in India from 25 March 2020 onwards to control the spread of deadly Coronavirus disease 2019 (COVID-19). A sudden shutdown of anthropogenic activities resulted in abrupt decrease of nitrogen dioxide (NO2) across the Indian region. OMI (Ozone Monitoring Instrument) tropospheric column NO2 observations show significantly decreased values during 2020 compared to previous years during 25 March to 19 April. The spatiotemporal variation of tropospheric column NO2 difference between 2020 and average 2017-2019 shows reduction by more than 1 x 1015 molecules/cm2 over the Indo Gangetic Plain, eastern and southern India due to lockdown. However, the western Indian region shows slight enhancement which may be attributed to combined effect of transport of polluted air from Middle East and Pakistan, and relatively higher biomass burning activity during 2020. A significant reduction is also observed on the surface distribution of NOx (NO + NO2) over different Indian cities due to COVID-19 lockdown. Maximum reduction in daily average surface NOx is observed over Kolkata (65.2 +or- 18.7 ppbv to 30.3 +or- 4.6 ppbv) followed by New Delhi (38.8 +or- 17.5 ppbv to 11.5 +or- 2.9 ppbv) which may be attributed to vehicle fleet, type of fuel used, power plants and industrial emissions.
ABSTRACT
Objective: The study objectives are to assess the challenges faced, individual awareness of pandemic, attitudes, and compliance of guidelines during lockdown. Design and method: This telephonic survey of 404 adult individuals were administered among hypertensive population with and without comorbidities of a longitudinal cohort in Barrackpore, West Bengal, India in Aug-Sept'2020. Comorbidities comprised with cardiovascular diseases, diabetes, asthma, OSA, BMI, epilepsy, stroke, arthritis, and cancer. Convenience sampling was considered to outline socio-demographics;chronic illness status;knowledge, attitude and practices;mood changes;and difficulties faced during lockdown. Association between variables have been conformed through multivariate logistic regression. Results: A total of 404 respondents, lone hypertensive 6.4%, hypertensive with other comorbidities 93.6%. Overall mean score of knowledge was 18.4±5.2 (Range 1-23), practices 6.1±1.1 (Range 2-8). Direct impacts on income 25.7%. Compliance of prescribed handwashing 93.3%, frequently hand sanitization 82.9%, using mask appropriately 91.1%, physical distancing 95.1%. Awareness of pandemic being contagious respiratory virus infection 97.8%, dispersion from human-to-human close contact 97.2%, curable 13.6%, could be fatal 4.5%, regarding symptoms 94.6%. Adverse impact due to the non-availability of medicine at home 4.5%, in pharmacy 2.2%;absence of doctors 9.4%;procured medicine at higher cost 6.2%;inaccessibility of transport 2.7%. On 3 or more drugs 33.2%, stored drugs 34.7%. Required and received medical advice due to polypharmacy 2.5%. Inadequate knowledge regarding 14-days isolation 4.5%;isolation and treatment reduce spread 2.7%;Lockdown was not an effective measure 11.4%;unconcerned regarding family members protection 34.2%;vaccine available in market 12.4%;and non-compliance of personal hygiene 6.2%. Pandemic still uncontrolled 14.4%. Multiple physical and sedentary activities less among hypertensive with comorbidities compared to lone hypertensives (AOR=0.96, CI: 0.95, 0.97, p<.0001). Hypertensives with comorbidities expressed better knowledge and practices compared to lone hypertensives. Conclusions: Short term impact during lockdown on hypertensive with or without comorbidities individuals was not significant. For effective control of the pandemic each and every individual of the cohort needs fully to comply with the prescribed isolation regime, personal protective measures and physical distancing beside real understanding of preventive function of safe-effective vaccine for everyone when available.
ABSTRACT
Objectives: In the past few months, the COVID-19 pandemic has drastically invaded the globe with its high infectivity. In this situation, people’s mental health is of utmost importance but poorly reported, especially in patients. We conducted this cross-sectional study among laboratory-confirmed hospitalized patients to evaluate the burden of depression, anxiety, and stress symptoms. Methods: We used depression, anxiety and stress scale 21 (DASS-21) to evaluate respective mental health components. A total of 114 hospitalized patients participated in this study. Of which, 65.79% were male patients. Results: The reported depression, anxiety, and stress were 77.2%, 84.2%, and 54.4%, respectively. An inverse relationship of the total DASS-21 score was found with the age of the participants. Conclusion: Such a high prevalence of mental health outcome suggests the need for further evaluation and addressing the problem with immediate concern.
ABSTRACT
The nationwide lockdown was implemented in India from 25 March 2020 onwards to control the spread of deadly Coronavirus disease 2019 (COVID-19). A sudden shutdown of anthropogenic activities resulted in abrupt decrease of nitrogen dioxide (NO2) across the Indian region. OMI (Ozone Monitoring Instrument) tropospheric column NO2 observations show significantly decreased values during 2020 compared to previous years during 25 March to 19 April. The spatiotemporal variation of tropospheric column NO2 difference between 2020 and average 2017–2019 shows reduction by more than 1 × 1015 molecules/cm² over the Indo Gangetic Plain, eastern and southern India due to lockdown. However, the western Indian region shows slight enhancement which may be attributed to combined effect of transport of polluted air from Middle East and Pakistan, and relatively higher biomass burning activity during 2020. A significant reduction is also observed on the surface distribution of NOx (NO + NO2) over different Indian cities due to COVID-19 lockdown. Maximum reduction in daily average surface NOx is observed over Kolkata (65.2 ± 18.7 ppbv to 30.3 ± 4.6 ppbv) followed by New Delhi (38.8 ± 17.5 ppbv to 11.5 ± 2.9 ppbv) which may be attributed to vehicle fleet, type of fuel used, power plants and industrial emissions. [ABSTRACT FROM AUTHOR] Copyright of Current Science (00113891) is the property of Indian Academy of Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
ABSTRACT
With existing tracing mechanisms, we can quickly identify potentially infected people with a virus by choosing everyone who has come in contact with an infected person. In the presence of abundant resources, that is the most sure-fire way to contain the viral spread. In the case of a new virus, the methods for testing and resources may not be readily available in ample quantity. We propose a method to determine the highly susceptible persons such that under limited testing capacity, we can identify the spread of the virus in a community. We determine highly suspected persons (represented as nodes in a graph) by choosing paths between the infected nodes in an underlying contact graph (acquired from location data). We vary parameters such as the infection multiplier, false positive ratio, and false negative ratio. We show the relationship between the parameters with the test positivity ratio (the number of infected nodes to the number of suspected nodes). We observe that our algorithm is robust enough to handle different infection multipliers and false results while producing suspected nodes. We show that the suspected nodes identified by the algorithm result in a high test positivity ratio compared to the real world. Based on the availability of the test kits, we can run our algorithm several times to get more suspected nodes. We also show that our algorithm takes a finite number of iterations to determine all the suspected nodes. © Springer Nature Switzerland AG 2021.