Memory Augmented Distributed Monte Carlo Tree Search Algorithm-Based Content Popularity Aware Content Recommendation Using Content Centric Networks

The rapid advancements of social media networks have created the problem of overloaded information. As a result, the service providers push multiple redundant contents and advertisements to the users without adequate analysis of the user interests. The content recommendation without user interests reduces the probability of users reading them and the wastage rate of network load increases. This problem can be alleviated by providing accurate content recommendations with consideration of users' precise interests and content similarity. Content centric networking has been developed as the trending framework to satisfy these requirements and improve access to relevant information and reception by the desired user. The uses of message entity by giving a proper name, the users' real-time interests are identified and then the accurate and popular contents with high contextual similarity are recommended. An efficient content recommendation scheme is presented in this paper using Memory Augmented Distributed Monte Carlo Tree Search (MAD-MCTS) algorithm for ensuring minimum energy consumption in the CCN. The big data context of the users' social media data is considered in this study so that the complexity can be visualized and controlled to minimize the network complexities. Experiments are conducted on a benchmark as well as an offline collected Twitter dataset on Covid-19 and the results implied that the accuracy and convergence of the proposed MAD-MCTS outperform the other content recommendation algorithms. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Associated impact of the COVID-19 induced lockdown on air quality of Kolkata Metropolitan Area (India)

As a pandemic, COVID 19 spread worldwide in early 2020. Primarily densely populated countries had remained vulnerable due to this biological hazard. Many people were forced to stay home owing to nature of the disease and no respite. A nationwide lockdown was implemented in India for 29 days (March 24th to April 21st) of 2020 during the wake of the COVID-19 pandemic. During the nationwide lockdown, industries, transport, and other commercial activities were suspended, except for necessary services. During the entire pandemic situation, an affirmative impact was observed as the air quality was reported to have improved worldwide. The complete economic lockdown to check COVID-19, brought unforeseen relief from severe condition of air quality. An apparent, reduction in level of PM2.5 and Air Quality Index (AQI) was experienced over Mumbai, Delhi, Kolkata, Hyderabad, and Chennai. Present work explores the various metrics of air pollution in Kolkata, West Bengal, India (imposed as a result of containment measure for COVID-19). The polluting parameters (e.g., PM10, PM2.5, SO2, NO2, CO, O3, and NH3) were chosen for seven monitoring stations (Ballygunge, Fort William, Victoria, Bidhannagar, Jadavpur, Rabindra Bharati, Rabindra Sarabar), which are spread across the metropolitan area of Kolkata. National Air Quality Index (NAQI) has been used to show pre-and during-lockdown air quality spatial patterns. The findings showed major changes in air quality throughout the lockdown period. The highest reduction in pollutants emission was observed for: PM10 (- 60.82%), PM2.5 (-45.05%) and NO2 (-62.27%), followed by NH3 (- 32.12%) and SO2 (-32.00%), CO (-47.46%), O3 (15.10%). During the lockdown, the NAQI value was reduced by 52.93% in the study area. © 2022 University of Craiova, Faculty of Social Sciences, Department of Geography. All rights reserved.

Role of Thermal Sensor in Detection of Early Stage of COVID-19

COVID-19 is the common enemy of all of us in this world. It created lot of deaths, loss of economy and many more. The pandemic has created a lot of innovation to bring solutions in helping fight the spread of novel coronavirus and other diseases. In this paper, various thermal sensors for detection of COVID-19 in early stage are discussed. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Comprehensive Therapeutic Interventions against Sars-Cov-2: A Review and Prospective

In December 2019, Wuhan City, Hubei Province, China, first reported pneumonia like symptoms with unknown aetiology caused by a novel coronavirus. The novel coronavirus was renamed as Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) by Coronaviridae Study Group of the International Committee on Taxonomy of Viruses and the disease was termed as Coronavirus Disease 2019 (COVID-19). As of 19 August, 2022, the infection has reached above 220 countries, areas or territories with a total of 591 683 619 confirmed cases and 6 443 306 deaths, as published by the World Health Organization (WHO). SARS-CoV-2 is strongly contagious as it has R0, 2.2-2.6, in comparison to SARS-CoV (<1) and Middle East respiratory syndrome coronavirus (MERS-CoV) (1.4-2.5), respectively. SARS-CoV-2 might become less virulent than the SARS-CoV and MERS-CoV, with the currently analyzed mortality of COVID-19 is 3.4%. The original SARS-CoV-2 has undergone "virus evolution"with the occurrence of numerous variants such as Alpha, Beta, Gamma and Delta etc. Recently, the circulating variant of concern is Omicron subvariants. Currently, real-time reverse transcription-polymerase chain reaction-based detection of the viral genome (RNA) is the gold standard for diagnosis of SARS-CoV-2 infection. At present, Remdesivir (RDV) and Baricitinib drugs as well as vaccines Pfizer-BioNTech and Moderna have been approved for the treatment of COVID-19 by Food and Drug Administration (FDA). In this review, we summarized the existing state of knowledge on approved antiviral therapy, combination therapy, blood-derived therapeutics and immunomodulators to treat COVID-19 pandemic. Copyright © 2022 The Authors.

Estimation of mortality rate and disproportionality analysis of anaphylactic reactions following COVID-19 vaccination

The emergency use authorization for the coronavirus disease 2019(COVID-19) vaccine has risen up expectations and concerns. Post market surveillance plays key role in assessing the benefit and risks. Our study aimed to estimate the death rate and analyze the disproportionality of anaphylactic reactions after receiving the COVID-19 vaccine.Data was gathered from the Vaccine Adverse Event Reporting system (VAERS) public data releases.To determine the mortality rate and anaphylactic reactions linked to COVID-19 vaccine provided between December 20, 2020 and June 24, 2022, a thorough study was conducted. Individuals who were 18 years of age or older and had received the COVID vaccines compared with other vaccine of same age group were included. Age, gender, onset of interval and days in hospital has been considered in assessing death rate. By comparing reports of anaphylactic reactions following the COVID-19 vaccines to all other vaccines using the Evans criteria, a disproportional analysis is performed using Proportional Reporting Ratio (PRR).However, ongoing surveillance of older adults who have received vaccinations is necessary. With regard to an anaphylactic reaction linked to the COVID-19 vaccination, no potential signal was observed.To systematically validate the information provided by VAERS, additional epidemiologic investigations are required.

Role of Thermal Sensor in Detection of Early Stage of COVID-19

COVID-19 is the common enemy of all of us in this world. It created lot of deaths, loss of economy and many more. The pandemic has created a lot of innovation to bring solutions in helping fight the spread of novel coronavirus and other diseases. In this paper, various thermal sensors for detection of COVID-19 in early stage are discussed. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Classification of COVID-19 from tuberculosis and pneumonia using deep learning techniques.

Deep learning provides the healthcare industry with the ability to analyse data at exceptional speeds without compromising on accuracy. These techniques are applicable to healthcare domain for accurate and timely prediction. Convolutional neural network is a class of deep learning methods which has become dominant in various computer vision tasks and is attracting interest across a variety of domains, including radiology. Lung diseases such as tuberculosis (TB), bacterial and viral pneumonias, and COVID-19 are not predicted accurately due to availability of very few samples for either of the lung diseases. The disease could be easily diagnosed using X-ray or CT scan images. But the number of images available for each of the disease is not as equally as other resulting in imbalance nature of input data. Conventional supervised machine learning methods do not achieve higher accuracy when trained using a lesser amount of COVID-19 data samples. Image data augmentation is a technique that can be used to artificially expand the size of a training dataset by creating modified versions of images in the dataset. Data augmentation helped reduce overfitting when training a deep neural network. The SMOTE (Synthetic Minority Oversampling Technique) algorithm is used for the purpose of balancing the classes. The novelty in this research work is to apply combined data augmentation and class balance techniques before classification of tuberculosis, pneumonia, and COVID-19. The classification accuracy obtained with the proposed multi-level classification after training the model is recorded as 97.4% for TB and pneumonia and 88% for bacterial, viral, and COVID-19 classifications. The proposed multi-level classification method produced is ~8 to ~10% improvement in classification accuracy when compared with the existing methods in this area of research. The results reveal the fact that the proposed system is scalable to growing medical data and classifies lung diseases and its sub-types in less time with higher accuracy.

Demographic & blood group characteristics of hospitalized COVID-19: Experience of private hospital

Aims & Objectives: SARS-CoV-2 infection has been rapidly increasing around the world and has already been declared pandemic by the WHO. First case in Chhattisgarh was reported in Mid- March'20. Since then the number is increasing at an alarming rate. Though majority of the cases are mild-moderate who are managed by home isolation and conservative management;patients having severe disease require hospital admission, with several requiring intensive care. Initially, majority admissions were handled by governmenthospitals, but with increasing burden the private-hospitals were also roped in. We studied the demographic and blood grouping profile of hospitalized Covid-19 patients and relation with mortality. Patients/Materials & Methods: We studied 6-months data (April- September'20) of a private-hospital in Chhattisgarh involved in management of Covid-19 patients. Demographic and blood group data maintained in the Laboratory Medicine department of hospital was analyzed. This was correlated with mortality in these patients. Results: Within 6-months, total 403 patients were hospitalized with 72% males and rest females. Most were in age-group of 41-60 years (49%), followed by ≥ 61 years (33%). B + ve (38%) was commonest blood group followed by O + ve, A + ve, AB + ve. Out of 403, 34 patients (8.4%) died, where 70% were males, mostly ≥ 61 years. Blood group of those who died was B + ve (41%), followed by O + ve (29%), A + ve (20%), AB + ve (5%) and 1 death in B-ve. However, the calculated relative risk showed B + ve (RR = 1, 95%CI), O + ve (RR = 0.9, 95%CI), A + ve (RR = 0.9, 95%CI), showing no increased risk of mortality in association with particular blood group. Discussion & Conclusion: Several western studies suggest that Covid-19 infection is commonly associated with blood group A. However, no studies were found in India. Our findings show B + ve to be most common, with no significant risk of increased mortality among particular blood group. This difference can be due to genetic, immunological variations and individual susceptibility for infection from western population. However, as we studied limited data, the generalization of these findings should be avoided. The preventive measures like hand hygiene, face masks, social distancing norms should be followed irrespective of your blood type.

Covid-19: A psychological nightmare

The COVID-19 pandemic has dramatically influenced our lifestyles and sleep habits, compromising our ability to effectively process and regulate emotions. We explore a neurobiological perspective to illustrate that dreams and nightmares during the pandemic may be indicative of an increased emotional load in our waking lives. We also propose that the combined impact of daily stressors and poor sleep behaviours brought on by the COVID-19 pandemic may lead to detrimental psychological health outcomes. These negative effects are ultimately perpetuated through a vicious cycle, necessitating the development of appropriate and timely interventions. We suggest that dreams and nightmares can showcase the role COVID-19 as a chronic population stressor. As this pandemic ensues, researchers should not overlook the importance of dreams and how sleeping habits are linked to waking emotional states. © 2021, University of Toronto. All rights reserved.

Mathematical model to analyse the effect of quarantine on spread and containment of COVID-19

Predictions, without mathematical approach and statistics appear bogus and simply drawn out of air. Hence applicability of mathematical modelling paves way for more authentic approach, the complexity of which depends on the number of parameters incorporated for use in the model. Analysing this, our work involved development of a model, labelled as SIQTRD model, which will signify its role and comprehensiveness in role prediction of quarantine in future containment of Covid19 disease as well as gain insights into the dynamics of disease transmission, based on the data available within a certain time frame. Predictions done this way in understanding the emergence effect and containment of epidemic are more effective and foster hind casting the event. The model predicts the number of active cases to reach 90,000-2,90,000 in India by mid-May to late June. In fact when the virus will enter the plateau phase, the number of active cases may monotonically increase even more depending on the relaxations implemented in lockdown and quarantine approach. This will be followed by a decreasing trend in the number of peak infective cases towards end August and after this the virus will fade out eventually although after effects may be visible in coming years. However the recovery rate may show an increasing trend due to the lockdown and quarantine policies in place providing ample time to the scientists, visionaries and medical practitioners to adopt implement and deploy anti-epidemic procedures including development of vaccines, testing kits etc. which will help out in dimming the disease. © 2020, Universitatea de Vest Vasile Goldis din Arad. All rights reserved.

Prevention of SARS-CoV-2 Spread in Dental Clinic: The Future Challenge in Resuming Clinical Practice

In the present scenario, the pandemic of coronavirus disease 2019 (COVID-19), which is responsible for simple upper respiratory infection to fatal pneumonia and multi-organ failure has become a major public health challenge and a public health emergency of international concern. Apart from secondary and tertiary care, it is very much essential to provide primary care, prevention, and early detection. To prevent the virus from the human-human transmission and to control the situation, the protocols vary at various setups. Due to the uniqueness of dental settings and practice, the risk of cross-infection can be high between patients and dental practitioners. Establishment of strict and effective infection control protocol is necessary owing to the varying sustainability of the virus on different surfaces. The area of concern for a dental professional is the oral cavity and upper respiratory region where the host recipient cell receptor, angiotensin-converting enzyme receptor 2, is present abundantly acts as the host cell entry route for the coronavirus. Dental professionals play an important role in preventing the transmission of SARS-CoV-2;we aim to review the infection control measures in dental practice. © 2020 Wolters Kluwer Medknow Publications. All rights reserved.