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1.
PLoS ONE [Electronic Resource] ; 17(8):e0273341, 2022.
Article in English | MEDLINE | ID: covidwho-2002327

ABSTRACT

The current coronavirus disease 2019 (COVID-19) pandemic, caused by the coronavirus 2 (SARS-CoV-2), involves severe acute respiratory syndrome and poses unprecedented challenges to global health. Structure-based drug design techniques have been developed targeting the main protease of the SARS-CoV-2, responsible for viral replication and transcription, to rapidly identify effective inhibitors and therapeutic targets. Herein, we constructed a phytochemical dataset of 1154 compounds using deep literature mining and explored their potential to bind with and inhibit the main protease of SARS-CoV-2. The three most effective phytochemicals Cosmosiine, Pelargonidin-3-O-glucoside, and Cleomiscosin A had binding energies of -8.4, -8.4, and -8.2 kcal/mol, respectively, in the docking analysis. These molecules could bind to Gln189, Glu166, Cys145, His41, and Met165 residues on the active site of the targeted protein, leading to specific inhibition. The pharmacological characteristics and toxicity of these compounds, examined using absorption, distribution, metabolism, excretion, and toxicity (ADMET) analyses, revealed no carcinogenicity or toxicity. Furthermore, the complexes were simulated with molecular dynamics for 100 ns to calculate the root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (Rg), solvent-accessible surface area (SASA), and hydrogen profiles from the simulation trajectories. Our analysis validated the rigidity of the docked protein-ligand. Taken together, our computational study findings might help develop potential drugs to combat the main protease of the SARS-CoV-2 and help alleviate the severity of the pandemic.

2.
Acta Polytechnica Hungarica ; 19(7):47-67, 2022.
Article in English | Scopus | ID: covidwho-1970300

ABSTRACT

In this paper, a novel framework is introduced by combining compressive sensing(CS) theory, digital curvelet transform, and Principal Component Analysis to improve the performance of face recognition method. CS is a highly attractive approach in the field of signal processing, which provides an efficient way of data sampling at a lower rate than the Nyquist sampling rate. CS offers numerous advantages, like less memory storage, less power consumption and higher data transmission rate etc. Here, CS is used on the face images, which offers reduction in storage space and computational time. The use of curvelet transform provides dual benefits: (i) sparse representation (ii) improvement on detailed content. To extract the feature vector, the Principal Component Analysis is then applied. The Performance of the proposed face recognition method is computed by applying cross-validation technique, compressive sensing based classifier, neural network, Naive Bayes and Support Vector Machine classifier. The proposed technique can efficiently perform the face recognition, at a low computational cost. Extensive experiments, on ORL and AR face databases, are conducted to validate our claim. The proposed technique also recognizes face images more efficiently than the traditional PCA, with a 1.5% higher recognition rate, if a person wears a face mask, as protection from COVID-19. © 2022, Budapest Tech Polytechnical Institution. All rights reserved.

3.
AIMS Biophysics ; 8(4):346-371, 2021.
Article in English | Scopus | ID: covidwho-1964164

ABSTRACT

The use of Artificial Intelligence (AI) in combination with Internet of Things (IoT) drastically reduces the need to test the COVID samples manually, saving not only time but money and ultimately lives. In this paper, the authors have proposed a novel methodology to identify the COVID-19 patients with an annotated stage to enable the medical staff to manually activate a geo-fence around the subject thus ensuring early detection and isolation. The use of radiography images with pathology data used for COVID-19 identification forms the first-ever contribution by any research group globally. The novelty lies in the correct stage classification of COVID-19 subjects as well. The present analysis would bring this AI Model on the edge to make the facility an IoT-enabled unit. The developed system has been compared and extensively verified thoroughly with those of clinical observations. The significance of radiography imaging for detecting and identification of COVID-19 subjects with severity score tag for stage classification is mathematically established. In a Nutshell, this entire algorithmic workflow can be used not only for predictive analytics but also for prescriptive analytics to complete the entire pipeline from the diagnostic viewpoint of a doctor. As a matter of fact, the authors have used a supervised based learning approach aided by a multiple hypothesis based decision fusion based technique to increase the overall system’s accuracy and prediction. The end to end value chain has been put under an IoT based ecosystem to leverage the combined power of AI and IoT to not only detect but also to isolate the coronavirus affected individuals. To emphasize further, the developed AI model predicts the respective categories of a coronavirus affected patients and the IoT system helps the point of care facilities to isolate and prescribe the need of hospitalization for the COVID patients © 2021. the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)

4.
International Conference on Electrical and Electronics Engineering, ICEEE 2022 ; 894 LNEE:503-511, 2022.
Article in English | Scopus | ID: covidwho-1826338

ABSTRACT

High temperature can be a symptom of various critical and semi-critical diseases. In the present COVID-19 pandemic, the demand for non contact based body temperature measurement devices are quite high. However, such devices are not only costlier than their contact based counterparts, but also their accuracy greatly depends on the distance and the location of the subject from the sensor (usually within 2–4 cm). To increase the accuracy of the measurement, researchers usually couple an ultrasonic sensor that can measure the distance of the object and instruct the sensor to take the reading only when the object is within the acceptable distance. However, the measurement of distance by the ultrasonic sensor depends on various aspects such as the impurities (such as sweat), location of the measurement area (forehead, hand etc.)and the initial distance. To overcome these limitations, the present work designed a low cost working prototype based on Arduino together with MLX90614ESF infrared temperature sensor and HC-SR04 ultrasonic sensor with an automatic distance correction mechanism for improved accuracy in temperature measurement. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

5.
PubMed; 2020.
Preprint in English | PubMed | ID: ppcovidwho-333595

ABSTRACT

Containment of the COVID-19 pandemic requires reducing viral transmission. SARS-CoV-2 infection is initiated by membrane fusion between the viral and host cell membranes, mediated by the viral spike protein. We have designed a dimeric lipopeptide fusion inhibitor that blocks this critical first step of infection for emerging coronaviruses and document that it completely prevents SARS-CoV-2 infection in ferrets. Daily intranasal administration to ferrets completely prevented SARS-CoV-2 direct-contact transmission during 24-hour co-housing with infected animals, under stringent conditions that resulted in infection of 100% of untreated animals. These lipopeptides are highly stable and non-toxic and thus readily translate into a safe and effective intranasal prophylactic approach to reduce transmission of SARS-CoV-2. ONE-SENTENCE SUMMARY: A dimeric form of a SARS-CoV-2-derived lipopeptide is a potent inhibitor of fusion and infection in vitro and transmission in vivo .

6.
2nd International Conference on Computer Vision, High-Performance Computing, Smart Devices, and Networks, CHSN 2021 ; 853:247-257, 2022.
Article in English | Scopus | ID: covidwho-1797674

ABSTRACT

Mucormycosis is an infection that occurs due to the presence of filamentous molds. Rhizopus delemar is a major cause of mucormycosis. The infection may be due to the inoculation of spores into wounds, inhalation of the spores, or the consumption of contaminated food. Mucormycosis cases have risen during the second wave of COVID-19 infections in India. Therefore, there is an urgent requirement for a vaccine against mucormycosis. The development of these vaccines is costly and time-consuming. Different methods have been used to decrease the expense and duration of time required for the development of a vaccine. One such method is the use of bioinformatics techniques for the development of vaccines. In this paper, the screening of epitopes through the bioinformatic tools predicts that the RO3G_11882 protein of Rhizopus delemar can be used for preparing immunological constructs. Binding and molecular simulation tests predict that the nanomeric epitope VLALHNFLL has low energy minimization values which provide stability to the peptide-MHC complex and sufficient binding with MHC class II molecules. This peptide sequence needs to further go through wet lab tests, for developing a vaccine against Mucormycosis. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

7.
Physics Education ; 57(3), 2022.
Article in English | Scopus | ID: covidwho-1764482

ABSTRACT

This article presents the study of Fourier series experimentally, using the ExpEYES-17 kit in a different way. This familiar undergraduate experiment has been performed for teaching online laboratory classes when face-to-face classes were not possible, due to the COVID - 19 pandemic. We chose parabolic and sawtooth waveforms, which are not generally considered for this experiment. The amplitudes A n of different harmonics were measured to compare with the theoretical predictions and the ratios An/A1 were found to agree well. © 2022 IOP Publishing Ltd.

8.
3rd International Conference on Communication, Devices and Computing, ICCDC 2021 ; 851:179-190, 2022.
Article in English | Scopus | ID: covidwho-1750656

ABSTRACT

Use of electronic devices has increased many times in our daily life. It is used for many purposes including healthcare. Small sensor devices on patients’ body that reads patients’ physiological data and send those data to a remote server. Doctors and other healthcare professionals can view this data sitting at their home. Thus remote health monitoring is possible known as Wireless Body Area Networks (WBAN). Routing and providing seamless connectivity is a big challenge and a topic of research. In this work, a priority based routing protocol designed for WBAN has been developed where data has been classified into normal and emergency data. This routing protocol is especially applicable for COVID and diabetic patients. Normal data will be processed in cloud server but emergency data will be processed locally. Results obtained prove that our protocol is faster and also gives minimum delay. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

9.
1st International Conference of IoT and its Applications, ICIA2020 ; 825:293-301, 2022.
Article in English | Scopus | ID: covidwho-1750632

ABSTRACT

The unprecedented rise and spread of the pandemic in form of nCOVID-19 has really raised high concerns in the socioeconomic front. The usual diagnosis is made by an RT-PCR test, which is highly specific can incorrectly identify some nCOVID-19 individuals to cause a serious compromise in overall accuracy. Since the drug application in its full swing is still some months away, hence, the need of the hour is to find a more accurate technique which can be used by health care centers having basic point of care facilities. The increase in the number of cases in India and lack of test kits in some of the less known diagnostic centers has added more concerns to the increasing problems. Additionally, the test kits incur a significant cost making it less affordable to some of the diagnostic centers. Hence, this research group in this article has proposed an algorithm centered around the concept of Internet of Things, a dual deep learning based algorithm, and collating the decision by a strong decision fusion technique. The objective of the algorithm is to detect and isolate the nCOVID-19 subjects in a cost-effective way to keep a check on the spread. This pandemic detection and isolation technique (PANDIT) is based on two different radiography image technology and uses a state-of-the-art deep learning algorithm for the purpose. The radiography technique has long been the most acceptable technique for cases related to pneumonia. The group has developed the algorithm based on X-ray and CT scan as its training data. The novelty of this paper is best described by a multi-fold methodology. Firstly, the significance of radiography imaging for detecting and identification of COVID-19 subjects. A simple connected value chain driven by Internet of Things (IoT) would enable the isolation process in an efficient and accelerated manner. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

10.
Indian Journal of Medical Microbiology ; 39:S93, 2021.
Article in English | EMBASE | ID: covidwho-1734526

ABSTRACT

Background: COVID-19 has been affecting mankind round the globe. Coinfection of Mycobacterium tuberculosis (TB) and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has implications beyond morbidity at the individual level and can lead to unintended TB exposure to others. The present study was conducted to better understand the implication of TB and COVID-19 co-infection in Cancer patients. Methods: The study was conducted in the department of MIcrobiology, Tata Memeorial Hospital, Mumbai. Records of all cancer patients tested for Mycobacterium Tuberculosis using Genexpert and /or MGIT during March 2020 to October 2020 in the institute were analysed.We determined if these patients were also tested for SARS CORONAVIRUS 2 during the course of their treatment. If postive for COVID 19, the outcome of the disease was analysed and various demo- graphic paramateres were compared. Results: A total of 1830 samples were analysed for Mycobacterium tuberculosis (MTB) (886 - MGIT & 944 - Genexpert) between March 2020 to October 2020. Of these 171 detected postive for MTB on Genexpert & 75 Detected positive in MGIT. Majority of the sample types were Respiratory samples 121 (39 - MGIT & 82 - GENEXPERT) and pus/ tissue 120 (31 - MGIT & 89 - genex-pert). Of the patients diagnosed with TB, 57 patients were also tested for COVID 19 virus using RTPCR test. 03 among them tested positive for the SARS COVID 19 virus and results of 54 came back negative. [Formula presented] Conclusions: co-infections with TB must always be suspected in addi- tion to COVID-19 in current scenario in patients with RTI with non- specific clinical features and unexplained or prolonged clinical course and utmost consideration should be given to all above concerns impli- cated.

11.
Indian Journal of Medical Microbiology ; 39:S65-S66, 2021.
Article in English | EMBASE | ID: covidwho-1734492

ABSTRACT

Background:Bacterial co-pathogens are commonly identified in viral respiratory tract infections such as influenza and are an im- portant cause of morbidity and mortality, necessitating timely diagnosis and antibacterial therapy1-3. The prevalence, incidence and characteristics of bacterial infection in patients infected with severe acute respiratory syndrome coronavirus 2 (SARS -CoV-2) is not well understood and has been raised as an important knowledge gap. Methods:This observational study was performed in the department of Microbiology, Tata memorial Hospital, Mumbai. All cancer patients admitted in ICU with COVID-19 for ≥48 hours between April 2020 to July 2020 were included in the study. Results:A total of 9595 cancer patients were tested for SARS Coronavirus 2 between April 2020 to July 2020 in the department of Microbiology, Tata Memorial Hospital, Mumbai. Out of these 2380 (24.80%) were COVID- 19 positive. 30 (1.26%) of the patients tested positive for COVID 19 required ICU admission. Squamous cell carcinona (3), Pancreatic Cancer (3) and Breast Cancer (3) were most com- monly involved cancer types. 20/30 of these patients had bacterial super infections while 10/30 had co infections. NDBAL 22 (31.88%) constituted the major source of infection, followed by BILE 10 (14.49%), PUS, PUS SWAB & WOUND SWAB 9 (13.04%). Most common- ly isolated organisms was E. coli 20 (23.25%), followed by Pseudomonas aeruginosa 19 (22.09%), Acinetobacter spp. 15 (17.44%) and Klebseilla pneumoniae 14(16.27%) respec- tively. E. coli & K. pneumoniae were most commonly sensitive to Amikacin (63.63%) and Tigecycline (57.57%). Ps. aeruginosa was moderately sensitive to commonly used antibi- otics like Piperacillin – tazobactum, Ceftazidime, Cefoperazone sulbactam (42.85%) and Ciprofloxacin, Tobramycin (38.09%) [Formula presented] Conclusions:Understanding the proportion of COVID-19 patients with acute respiratory bacterial co-infection, and the culprit pathogens, is crucial for treating patients with COVID- 19 and to help ensure responsible use of antibiotics and to minimize negative consequenc- es of overuse.

12.
13th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2021 ; : 168-175, 2021.
Article in English | Scopus | ID: covidwho-1701690

ABSTRACT

Modeling the spatiotemporal nature of the spread of infectious diseases can provide useful intuition in understanding the time-varying aspect of the disease spread and the underlying complex spatial dependency observed in people's mobility patterns. Besides, the county level multiple related time series information can be leveraged to make a forecast on an individual time series. Adding to this challenge is the fact that real-time data often deviates from the unimodal Gaussian distribution assumption and may show some complex mixed patterns. Motivated by this, we develop a deep learning-based time-series model for probabilistic forecasting called Auto-regressive Mixed Density Dynamic Diffusion Network (ARM3Dnet), which considers both people's mobility and disease spread as a diffusion process on a dynamic directed graph. The Gaussian Mixture Model layer is implemented to consider the multimodal nature of the realtime data while learning from multiple related time series. We show that our model, when trained with the best combination of dynamic covariate features and mixture components, can outperform both traditional statistical and deep learning models in forecasting the number of Covid-19 deaths and cases at the county level in the United States. © 2021 ACM.

13.
International Journal of Enterprise Information Systems ; 17(4):37-68, 2021.
Article in English | Web of Science | ID: covidwho-1690096

ABSTRACT

Level-based weight assessment (LBWA) model is a recently introduced algorithm for determining criteria weights for multi-criteria group decision making. In this paper, the authors aim to extend the basic framework of LBWA in the picture fuzzy (PF) environment using actual score (AS) measures of the picture fuzzy numbers (PFN). They apply this extended framework in addressing a real-life problem pertaining to social entrepreneurship or social entrepreneurs (SE) in the context of COVID-19. They endeavor to identify the critical challenging factors of SE in the new normal. They list the challenges as revealed through literature review and take the opinion of a group of SEs using PF linguistic scale. They then apply the proposed framework, actual score-based picture fuzzy LBWA. They notice that ability to withstand disruption risk and show resilience and fund availability and creation of a supporting business ecosystems are the major challenges that SEs face in the new normal. They carry out validity checking and sensitivity analysis, which show reasonable consistency and stability in the result.

14.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-321054

ABSTRACT

Cosmic ray muon flux is measured by the coincidence technique using plastic scintillation detectors in the High Energy Physics detector laboratory at Bose Institute, Kolkata. Due to the COVID19 outbreak and nationwide complete lockdown, the laboratory was closed from the end of March 2020 till the end of May 2020. After lockdown, although the city is not in its normal state, we still were able to take data on some days. The lockdown imposed a strict restriction on the transport service other than the emergency ones and also most of the industries were shut down in and around the city. This lockdown has significant effect on the atmospheric conditions in terms of change in the concentration of air pollutants. We have measured the cosmic ray flux before and after the lockdown to observe the apparent change if any due to change in the atmospheric conditions. In this article, we report the measured cosmic ray flux at Kolkata (22.58$

15.
International Journal of Statistics in Medical Research ; 10:146-160, 2021.
Article in English | Scopus | ID: covidwho-1591784

ABSTRACT

Purpose: COVID-19, a global pandemic, first appeared in the city of Wuhan, China, and has since spread differently across geographical borders, classes, and genders from various age groups, sometimes mutating its DNA strands in the process. The sheer magnitude of the pandemic's spread is putting a strain on hospitals and medical facilities. The need of the hour is to deploy IoT devices and robots to monitor patients' body vitals as well as their other pathological data to further control the spread. There has not been a more compelling need to use digital advances to remotely provide quality healthcare via computing devices and AI-powered medical aids. Method: This research developed a deployable Internet of Things (IoT) based infrastructure for the early and simple detection and isolation of suspected coronavirus patients, which was accomplished via the use of ensemble deep transfer learning. The proposed Internet of Things framework combines 4 different deep learning models: DenseNet201, VGG16, InceptionResNetV2, and ResNet152V2. Utilizing the deep ensemble model, the medical modalities are used to obtain chest high-resolution computed tomography (HRCT) images and diagnose the infection. Results: Over the HRCT image dataset, the developed deep ensemble model is collated to different state-of-the-art transfer learning (TL) models. The comparative investigation demonstrated that the suggested approach can aid radiologists inefficiently and swiftly diagnosing probable coronavirus patients. Conclusion: For the first time, our group has developed an AI-enabled Decision Support System to automate the entire process flow from estimation to detection of COVID-19 subjects as part of an Intelligent Value Chain algorithm. The screening is expected to eliminate the false negatives and asymptomatic ones out of the equation and hence the affected individuals could be identified in a total process time of 15 minutes to 1 hour. A Complete Deployable System with AI Influenced Prediction is described here for the first time. Not only did the authors suggest a Multiple Hypothesis based Decision Fusion Algorithm for forecasting the outcome, but they also did the predictive analytics. For simple confined isolation or hospitalization, this complete Predictive System was encased within an IoT ecosystem. © 2021 Lifescience Global. All Rights Reserved.

16.
Marketing Science ; : 14, 2021.
Article in English | Web of Science | ID: covidwho-1581923

ABSTRACT

To what extent do mass media outlets influence viewers' trust in scientific evidence and compliance with behavior recommended by scientific experts? Exploiting the U.S. lockdown period of the COVID-19 pandemic in early 2020, we analyze a large longitudinal database that combines daily stay-at-home behavior from approximately 8 million mobile phones and local viewership of cable news networks. Early in the pandemic, several of Fox News' hosts downplayed the severity of the pandemic and the risks associated with the transmission of the virus. A combination of regression analysis and a natural experiment finds that a 10% increase in viewership of Fox News in a zip code causes a 0.76 percentage-point reduction in compliance with stay-at-home behavior. The results imply a media persuasion rate that is larger than typical advertising persuasion rates on consumer behavior. Similar analyses using viewership of MSNBC and CNN, which supported lock down measures, were inconclusive but suggested a smaller, positive effect on compliance with social distancing regulations.

17.
Bali Journal of Anesthesiology ; 5(4):230-233, 2021.
Article in English | Scopus | ID: covidwho-1566732

ABSTRACT

Telemedicine is a modality which utilizes technology to provide and support health care across large distances. It has redefined the practices of medicine in many specialties and continues to be a boon for clinicians on many frontiers. Its role in the branch of anesthesia remains largely unexplored but has shown to be beneficial in all the three phases: pre-operative, intra-operative, and post-operative. Now time has come that anesthesiologists across the globe reassess their strategies and utilize the telemedicine facilities in the field of anesthesia. © 2021 EDP Sciences. All rights reserved.

18.
Egyptian Journal of Radiology and Nuclear Medicine ; 52(1), 2021.
Article in English | EMBASE | ID: covidwho-1526671

ABSTRACT

Background: Coronavirus disease 2019 (COVID-19) is known to be associated with a myriad of viral, fungal, and bacterial co-infections. Rhino-orbital mucormycosis is a rare angio-invasive fungal infection which has shown a rising trend in the setting of COVID-19. Case presentation: We describe the imaging findings in 3 cases of rhino-orbital mucormycosis in patients with history of COVID-19. All cases had varying involvement of paranasal sinuses extending into the orbital compartment while case 3 had intracranial extension of infection. Conclusions: Rhino-orbital mucormycosis can have aggressive necrosis of the involved paranasal sinuses and orbits with or without cerebral extension. Hence, the correct diagnosis is imperative as prompt antifungal drugs and surgical debridement can significantly reduce mortality and morbidity.

19.
2021 IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2021 ; 2021-August:408-411, 2021.
Article in English | Scopus | ID: covidwho-1447887

ABSTRACT

The application of microfluidics in Point of Care (PoC) testing was a breakthrough in PoC diagnostics, yet high precision testing remains a challenge. In order to extract and isolate pure RNA or DNA from biofluids containing a virus or virion, small spherical magnetic particles called magnetic beads are commonly used in laboratories. Magnetic beads have many applications ranging from purification of nucleic acids, magnetic cell separation, targeted drug delivery to brain or malignant tumors, magnetic chaining etc. Unfortunately, integrating magnetic beads into PoC devices is challenging. This paper delves into a comparative study of the behaviour and parameters of five different sizes of carboxyl coated paramagnetic beads along with silica coated dynabeads. Their behavior is characterized in a microcapillary channel for a SARS-CoV-2 virus PoC testing kit to enrich the sample, concentrating the nucleic acid. This step would increase the sensitivity and specificity for any nucleic acid-based PoC testing device. © 2021 IEEE.

20.
Ieee Consumer Electronics Magazine ; 10(5):64-69, 2021.
Article in English | Web of Science | ID: covidwho-1369307

ABSTRACT

Contagious diseases are prevalent even in the current era of advanced technology. A uniformed initiative is required to build a reliable interactive information exchange service targeting vaccination data management and other medical services. The conventional data exchange mechanism is centralized, creating many vulnerable issues such as a single point failure, data leakage, access control, etc. This article introduces a Blockchain-based medical data-sharing framework (called GlobeChain) to overcome the technical challenges to handle the outbreak records. The challenges that might arise due to the proposed Blockchain-based framework are also presented as a future direction that grabs the proposal's effectiveness.

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