Detection of COVID-19 Using CNN and ML Algorithms

As we see coronavirus is the very dangerous diseases and to identify this diseases in one's body is also not as easy. So during identification of diseases there are many false positive cases we see that person does not have corona and still the prediction comes true and also in some cases, it happens that person has corona but it does not get detected (false negative case). So due to this problem, we here come up with the two approaches and make comparison between these two approaches and decide which one is better to analyze the diseases in the body. We are using CNN to scan chest X-ray dataset and ML algorithms for tabular dataset as it contains many text information too. So in this project, we explain in detail, what is CNN, what is ML, how to implement CNN and ML algorithms on particular dataset, what output we will get as a comparison. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Detecting COVID-19 from Lung Computed Tomography Images: A Swarm Optimised Artificial Neural Network Approach

COVID-19 has affected many people across the globe. Though vaccines are available now, early detection of the disease plays a vital role in the better management of COVID-19 patients. An Artificial Neural Network (ANN) powered Computer Aided Diagnosis (CAD) system can automate the detection pipeline accounting for accurate diagnosis, overcoming the limitations of manual methods. This work proposes a CAD system for COVID-19 that detects and classifies abnormalities in lung CT images using Artificial Bee Colony (ABC) optimised ANN (ABCNN). The proposed ABCNN approach works by segmenting the suspicious regions from the CT images of non-COVID and COVID patients using an ABC optimised region growing process and extracting the texture and intensity features from those suspicious regions. Further, an optimised ANN model whose input features, initial weights and hidden nodes are optimised using ABC optimisation classifies those abnormal regions into COVID and non-COVID classes. The proposed ABCNN approach is evaluated using the lung CT images collected from the public datasets. In comparison to other available techniques, the proposed ABCNN approach achieved a high classification accuracy of 92.37% when evaluated using a set of 470 lung CT images. Author

A study on hepatic dysfunction in Covid 19 patients

Background and Objectives - Covid -19 is a pandemic, which is known to be a multi organ disease with complex clinical manifestations. Covid 19 virus has predilection for lung involvement but can also cause hepatic dysfunction. This study aims to analyze the significance of abnormal liver function tests in SARS- COV2 positive patients. METHOD- This retrospective study, involved 150 patients (75 MALES, 75 FEMALES) who tested positive for SARS COV2. After obtaining clearance from the ethical committee, clinical and biochemical data were collected retrospectively from patient records, for a period of six months. They were segregated into severe and non severe SARS COV2 infected individuals. liver function test were compared among patients between these 2 groups. RESULTS-Of the 150 covid- 19 positive patients, 75 were males and 75 were females. The mean age was 50+/-5 years. 95 patients belonged to the non severe covid-19 category (22 hypoxic and 73 non hypoxic patients), who were admitted in the ward. 55 patients belonged to the severe covid -19 category (hypoxic patients who required NIV/ ventilator support), admitted in the ICU. Severe hypoalbuminemia 63%, was observed in the severe category, compared to 6.32% in the non- severe category. Raised transaminases were observed in 60% in the severe category, compared to 23.15% in the non severe category. The incidence of death observed in the ICU in our study was 25.45%, of which 35% were female patients and 64.28% were male patients. CONCLUSION- Hypoalbuminemia, raised transaminases and bilirubin were observed in covid-19 patients admitted in the ICU, indicating, they could be considered as a poor prognostic factor. Copyright © 2022 Ubiquity Press. All rights reserved.

Interleukin-17 Inhibitor in Treatment of Psoriasis during COVID-19 Pandemic: An Ambispective Study

Introduction: Psoriasis is a genetically mediated chronic inflammatory disease that is frequently associated with metabolic co-morbidities. These metabolic co-morbidities have a huge impact in deciding the appropriate immunosupressant of choice in the current scenario of Coronavirus Disease (COVID-19) pandemic. Treatment of psoriasis especially with biologicals is challenging during covid pandemic since immunosuppressive therapy might interfere with antiviral immunity. Aim(s): To report the safety profile of Interleukin-17 (IL-17) inhibitor, namely injection secukinumab in patients of psoriasis vulgaris during COVID-19 pandemic. Material(s) and Method(s): An ambispective interventional study was performed on 23 patients of psoriasis who were administered secukinumab at a dose of 300 mg subcutaneously during COVID-19 pandemic.The study was conducted at the Department of Dermatology, Madras Medical College, Chennai, Tamil Nadu, India among the patients attending the psoriasis clinic between the July 2021 to March 2022. The demographic characteristics of the study group, previous treatment for psoriasis and the relationship between risk of COVID-19 infection and secukinumab were noted. Efficacy of secukinumab was calculated using Psoriasis Area and Severity Index (PASI) scores. Statistical analysis was conducted with Statistical Package for Social Sciences (SPSS) statistics software version 21.0 by Fischer's-exact test. Result(s): Out of 23 patients, 17 patients (11 males, six females) completed full course of nine doses (five weekly doses followed by four monthly doses) of secukinumab. The PASI 75 and PASI 90 were achieved in 9 (52.94%) and 8 (29.41%) patients respectively at the end of 12 weeks. None of the patients developed COVID-19 infection during the course of treatment and three months following therapy. Patients with psoriasis who had a history of COVID-19 infection did not show signs of reinfection when started on secukinumab. Both inactivated vaccine (Covaxin) and vector based vaccine (Covishield) were found to be safe in concomitant use with secukinumab. Conclusion(s): Secukinumab is found to be safe and effective in psoriasis treatment during COVID-19 pandemic. There is no increased risk of COVID-19 infection or reinfection, COVID-19 associated hospitalisation and mortality among patients with psoriasis administered with secukinumab. The drug can also be safely used with COVID-19 vaccines. Copyright © 2023 Journal of Clinical and Diagnostic Research. All rights reserved.

A Study of Knowledge, Attitude and Practices on the Impact of Covid-19 Infection in Final Year Mbbs Students

Background: COVID-19 has caused unprecedented disruption to the medical education process and to healthcare systems worldwide. The highly contagious nature of the virus has made it difficult to continue lectures, thus influencing the medical education methodology, which is based on lectures and patient-based education. The present study aims to determine and analyze the impact of COVID-19 infection on final year MBBS student's education in Saveetha Medical College Hospital. To assess the relationship between Knowledge, Attitude and Practices. Methodology: A cross section survey conducted during a period of 3 months (March -May).The survey involves a question in Google forms which was distributed to 100 final year MBBS students of Saveetha medical College. The questionnaire was divided into 3 sections namely, knowledge about e-learning (5 questions), Attitude (5 questions) and practices and application on medical education (5 questions) with the total of 15 questions, each of which was graded & scored accordingly. SEM Analysis is done. Result(s): There is no absolute relationship between three factors i.e. knowledge, attitude and practices. However Attitude and Knowledge had fair positive relationship by 16.5 %.Also it is of clear evidence that medical student's knowledge affected their practice to a certain extent during lockdown & found that there was no evidence that the student's attitude affected their practice during COVID-19. Conclusion(s): COVID-19 pandemic affected the education of students.Online mode of teaching had both advantages and disadvantages. In our study, impact of COVID-19 infection on final year MBBS student's education was assessed and knowledge towards practice was comparatively affected more than their attitude towards practice. Copyright © 2022 Authors. All rights reserved.

Detection of COVID-19 Using CNN and ML Algorithms

As we see coronavirus is the very dangerous diseases and to identify this diseases in one’s body is also not as easy. So during identification of diseases there are many false positive cases we see that person does not have corona and still the prediction comes true and also in some cases, it happens that person has corona but it does not get detected (false negative case). So due to this problem, we here come up with the two approaches and make comparison between these two approaches and decide which one is better to analyze the diseases in the body. We are using CNN to scan chest X-ray dataset and ML algorithms for tabular dataset as it contains many text information too. So in this project, we explain in detail, what is CNN, what is ML, how to implement CNN and ML algorithms on particular dataset, what output we will get as a comparison. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

A Critical Review of COVID-19 Pandemic Outbreak in the Restaurant Industry

Food Service Industry (FSI) was one of the hardest hit during the outbreak of COVID-19 as they were shutdown to control the spread of the virus across society. The COVID-19 pandemic almost nullified the organized and unorganized restaurant business across the country. Dining out is one of the most popular social activities in a metropolitan city like Coimbatore (popularly known as Manchester of South India), the city has branches of popular restaurant chains from across the globe. Measures such as lockdown, social distancing, and dine-in restrictions adopted by the government to slow down the spread of the virus, have imparted huge loss to the FSI that has in turn severely affected the livelihood of millions of restaurant workers in the country with Coimbatore being no exception. In this background, the authors have studied the case of impact of COVID-19 on restaurants with special reference to Coimbatore. Primary data from restaurants were used for this study. The findings based on the data provide a significant contribution to the FSI, and identify the factors that need to be tapped to frame a strategy to increase the sales volume in upcoming pandemic-like situations. Also, the authors have suggested following the Standard Operating Procedure (SOP) instructed by the government even after the pandemic.

A Critical Review of COVID-19 Pandemic Outbreak in the Restaurant Industry

Food Service Industry (FSI) was one of the hardest hit during the outbreak of COVID-19 as they were shutdown to control the spread of the virus across society. The COVID-19 pandemic almost nullified the organized and unorganized restaurant business across the country. Dining out is one of the most popular social activities in a metropolitan city like Coimbatore (popularly known as Manchester of South India), the city has branches of popular restaurant chains from across the globe. Measures such as lockdown, social distancing, and dine-in restrictions adopted by the government to slow down the spread of the virus, have imparted huge loss to the FSI that has in turn severely affected the livelihood of millions of restaurant workers in the country with Coimbatore being no exception. In this background, the authors have studied the case of impact of COVID-19 on restaurants with special reference to Coimbatore. Primary data from restaurants were used for this study. The findings based on the data provide a significant contribution to the FSI, and identify the factors that need to be tapped to frame a strategy to increase the sales volume in upcoming pandemic-like situations. Also, the authors have suggested following the Standard Operating Procedure (SOP) instructed by the government even after the pandemic. © 2022, ASERS Publishing House. All rights reserved.

Derivation, Functionalization of (S)-Goniothalamin from Goniothalamus wightii and Their Derivative Targets SARS-CoV-2 MPro, SPro, and RdRp: A Pharmacological Perspective.

The tracing of an alternative drug, Phytochemicals is a promising approach to the viral threats that have emerged over the past two years. Across the world, herbal medicine is a better solution against anti-viral diseases during pandemic periods. Goniothalamus wightii is an herbal plant, which has diverse bioactive compounds with anticancer, antioxidant, and anti-viral properties. The aim of the study was to isolate the compound by chromatography studies and functionalization by FT-IR, LC-MS, and NMR (C-NMR, H-NMR). As a result, the current work focuses on whether (S)-Goniathalamin and its analogue could act as natural anti-viral molecules for multiple target proteins viz., MPro, RdRp, and SPro, which are required for SARS-CoV-2 infection. Overall, 954 compounds were examined and the molecular-docking studies were performed on the maestro platform of Schrodinger software. Molecular-dynamics simulation studies were performed on two complex major compounds to confirm their affinity across 150 simulations. This research suggests that plant-based drugs have high levels of antiviral properties against coronavirus. However, more research is needed to verify its antiviral properties.

Machine Learning Models for Predicting Financially Vigilant Low-Income Households

The COVID-19 pandemic has adversely affected households’lives in terms of social and economic factors across the world. The Malaysian government has devised a number of stimulus packages to combat the pandemic’s effects. Stimulus packages would be insufficient to alleviate household financial burdens if they did not target those most affected by lockdowns. As a result, assessing household financial vigilance in the case of crisis like the COVID-19 pandemic is crucial. This study aimed to develop machine learning models for predicting and profiling financially vigilant households. The Special Survey on the Economic Effects of Covid-19 and Individual Round 1 provided secondary data for this study. As a research methodology, a cross-industry standard process for data mining is followed. Five machine learning algorithms were used to build predictive models. Among all, Gradient Boosted Tree was identified as the best predictive model based on F-score measure. The findings showed machine learning approach can provide a robust model to predict households’financial vigilances, and this information might be used to build appropriate and effective economic stimulus packages in the future. Researchers, academics and policymakers in the field of household finance can use these recommendations to help them leverage machine learning. Author

Evaluation of Clove Phytochemicals as Potential Antiviral Drug Candidates Targeting SARS-CoV-2 Main Protease: Computational Docking, Molecular Dynamics Simulation, and Pharmacokinetic Profiling.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus can cause a sudden respiratory disease spreading with a high mortality rate arising with unknown mechanisms. Still, there is no proper treatment available to overcome the disease, which urges the research community and pharmaceutical industries to screen a novel therapeutic intervention to combat the current pandemic. This current study exploits the natural phytochemicals obtained from clove, a traditional natural therapeutic that comprises important bioactive compounds used for targeting the main protease of SARS-CoV-2. As a result, inhibition of viral replication effectively procures by targeting the main protease, which is responsible for the viral replication inside the host. Pharmacokinetic studies were evaluated for the property of drug likeliness. A total of 53 bioactives were subjected to the study, and four among them, namely, eugenie, syzyginin B, eugenol, and casuarictin, showed potential binding properties against the target SARS-CoV-2 main protease. The resultant best bioactive was compared with the commercially available standard drugs. Furthermore, validation of respective compounds with a comprehensive molecular dynamics simulation was performed using Schrödinger software. To further validate the bioactive phytochemicals and delimit the screening process of potential drugs against coronavirus disease 2019, in vitro and in vivo clinical studies are needed to prove their efficacy.

COVID Detection Using Chest X-Ray and Transfer Learning

As per World Health Organization, COVID-19 is causing even the most important health systems across the countries under considerable strain. The advanced recognition of COVID 19 will result into decreasing the stress of a lot of health systems. Much similar to the customary usage of Chest X-Rays for detecting different pathologies, COVID-19 can also be detected using X-Ray of patients that indicates a very critical function in the diagnosis of SARS Covid-19. With rampant growth in the area of Deep Learning (DL) as well as Machine Learning (ML), it is much easier to design the framework that can detect COVID-19 infection easily. This paper proposes deep learning-based detection process by incorporating the concept of Transfer Learning for the classification of this pandemic using X-ray images of chest. This non-invasive and early-prediction of the corona virus by observing the X-rays of chest can subsequently be utilized to estimate the expansion of COVID-19 in the patients. This study got a maximum of 97% classifiers' accuracy using ResNet based model. This method can be utilized to upscale the effectiveness of the screening process.

Safety and efficacy of a Siddha Medicine fixed regimen for the treatment of asymptomatic and mild COVID-19 patients.

Background: The Coronavirus disease 2019 (COVID-19) pandemic caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a massive threat to public health worldwide. Siddha system of medicine is one of the traditional medicines of South India. The recommended formulations in Siddha Sasthric Medicines- Fixed Regimen (SSM-FiRe) are Amukkura tablets, Kaba Sura Kudineer (KSK) for asymptomatic COVID-19 positive (RT-PCR) patients, and Athimathuram tablets, Adathodai Manappagu syrup, Thippili Rasayanam, Brahmananda Bairavam tablet, and Notchi Kudineer for mild symptomatic patients. The core objective of the trial was to document the efficacy of SSM-FiRe in the prevention of asymptomatic and mild COVID-19 disease progression to the next level of severity, reduce the severity of symptoms and revert to RT-PCR Negative. Methods: An exploratory, prospective, open-labeled, single-arm, non-randomized trial was designed as per GCP guidelines to assess the efficacy of SSM-FiRe. Sixty RT-PCR positive participants who were asymptomatic or with mild COVID-19 symptoms were recruited for the study at the Siddha COVID Care Centre, Vyasarpadi, Chennai from June to August 2020. Nasal and oropharyngeal swab tests were performed on the 0, 7th, and 14th days. All participants were treated with SSM - FiRe regimen. All the participants were also assessed based on Siddha Yakkkaiyin Ilakkanam, which included Clinical symptoms and vitals. Laboratory investigations such as Haemogram, Liver Function Test, Renal Function Test, HbA1C, Electrolytes, Inflammatory markers, Cardiac profile, Immunoglobulins, and anti-SARS-CoV-2 antibody tests were performed. Results: 83% of COVID-19 patients turned RT-PCR negative on the 7th day and in most of the cases, symptoms were reduced within the first 5 days of admission. The RT-PCR cycle threshold (ct) value increased significantly (<0.001) after treatment and all the participants were RT-PCR negative, except one, who was positive even after 14 days. Anti-SARS-CoV-2 antibodies developed significantly (p-value - 0.006). LFT, RFT, CBC, Total proteins, and electrolytes continued to be in the normal range after treatment, indicating the safety of the intervention. Conclusion: Asymptomatic and mild COVID-19 disease can be well managed by SSM - FiRe treatment, Further studies could be taken up to strengthen the findings.

COVID Detection Using Chest X-Ray and Transfer Learning

As per World Health Organization, COVID-19 is causing even the most important health systems across the countries under considerable strain. The advanced recognition of COVID 19 will result into decreasing the stress of a lot of health systems. Much similar to the customary usage of Chest X-Rays for detecting different pathologies, COVID-19 can also be detected using X-Ray of patients that indicates a very critical function in the diagnosis of SARS Covid-19. With rampant growth in the area of Deep Learning (DL) as well as Machine Learning (ML), it is much easier to design the framework that can detect COVID-19 infection easily. This paper proposes deep learning-based detection process by incorporating the concept of Transfer Learning for the classification of this pandemic using X-ray images of chest. This non-invasive and early-prediction of the corona virus by observing the X-rays of chest can subsequently be utilized to estimate the expansion of COVID-19 in the patients. This study got a maximum of 97% classifiers’ accuracy using ResNet based model. This method can be utilized to upscale the effectiveness of the screening process. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

COVID 19 Outbreaks on the Growth of Self Servicing Technology Using Digital Payments

In comparison to any other International crisis, Covid19 was sudden and did not leave much time for individuals or Governments to prepare in terms of the impact it had on healthcare infrastructure or trade-in various sectors. The modern world is highly connected and stopping the inter-country movement of people is very difficult. Given the rapid increase of cases, Covid19 was declared as a pandemic and for lack of any other viable option, most Governments chose the way of locking down the economy. There was little information on how Covid19 spreads mortality rate or recovery rate, etc. Impetus on social distancing forced people to get wary of any contact including the exchange of cash which in turn resulted in the rapid adoption of alternate measures such as digital payments. Supply chain management was badly hit and demand for essential products and services increased significantly. Although overall volumes of digital payments went down due to adverse impact on several sectors, its usage as a replacement of cash increased significantly. This sudden increase and adoption by people who are not technology-savvy gave rise to frauds and cyber-attacks. Thus there arose a need for stringent regulations, the evolution of technology, and enhanced user education. There has been a significant push by the Government for achieving a cashless economy and digital payments surely will provide robust support for this objective. RBI has also proposed a self-regulatory body for digital payment and has taken initiatives like making NEFT available 24·7 and removing applicable charges. There are rising impetus and applications of digital payment technologies in day-to-day and business-related trade transactions.

Visual Understanding of COVID-19 Knowledge Graph for Predictive Analysis

This study aims to effectively analyze and visualize the concept to concept network derived from the COVID-19 Open Research Dataset (CORD-19) dataset, where we have more than 48,000 concepts with more than 300,000 relationships between concepts. In analyzing networks, we focus on finding relationship patterns between the coronavirus disease 2019 (COVID-19) concepts and other concepts. Given the node and edge datasets, we construct directional graphs and calculate all pair shortest paths based on multiple edge weight schemes. However, statistical metrics are not sufficient to identify specific relationships represented in the network. Therefore, we also propose a visual analytics approach to effectively understand the knowledge graph. Our highly interactive visual analytics allows users to effectively analyze the evolving graphs and (COVID-19) concept nodes and other nodes related to the COVID-19 nodes. We envision that this study will pave the path to develop strategies to provide more accurate and scalable predictive analysis on knowledge graphs related to CORD19 and other biomedical knowledge graphs. © 2021 IEEE.

Mucormycosis (black fungus) ensuing COVID-19 and comorbidity meets - Magnifying global pandemic grieve and catastrophe begins.

Post COVID-19, mucormycosis occurred after the SARS-CoV-2 has rampaged the human population and is a scorching problem among the pandemic globally, particularly among Asian countries. Invasive mucormycosis has been extensively reported from mild to severe COVID-19 survivors. The robust predisposing factor seems to be uncontrolled diabetes mellitus, comorbidity and immunosuppression acquired through steroid therapy. The prime susceptive reason for the increase of mucormycosis cases is elevated iron levels in the serum of the COVID survivors. A panoramic understanding of the infection has been elucidated based on clinical manifestation, genetic and non- genetic mechanisms of steroid drug administration, biochemical pathways and immune modulated receptor associations. This review lime-lights and addresses the "What", "Why", "How" and "When" about the COVID-19 associated mucormycosis (CAM) in a comprehensive manner with a pure intention to bring about awareness to the common public as the cases are inevitably and exponentially increasing in India and global countries as well. The article also unearthed the pathogenesis of mucormycosis and its association with the COVID-19 sequela, the plausible routes of entry, diagnosis and counter remedies to keep the infection at bay. Cohorts of case reports were analysed to spotlight the link between the pandemic COVID-19 and the nightmare-mucormycosis.

Novel Drug Discovery Using Generative Model for COVID-19

Drug discoveries often need expertise knowledge and insanely complex biochemical tests for the discovery of molecular chemical properties. In recent years, there has been an increasing trend of using AI and deep learning-based tools which aid the domain expect to speed up the process of drug design. The use of dynamically un-supervised deep learning systems is used to identify certain properties of atoms of molecules that could have aided the pharmaceutical scientist at many times. The drug discovery comes under sixth goal of millennium development goals which is used as a standard procedure to combat diseases such as HIV, AIDS, dengue, malaria, and another global pandemic. In this paper, we propose a new un-supervised molecular embedding procedure, which provides a continuous vector of molecule in their latent space. These molecules in their latent space can aide in generation of new atoms or a combination of atoms which have relevant chemical nature and can be used as a direct and effective replacement for the existing molecules. The model proposed in this paper is an LSTM autoencoder (long short-term memory) to model the sequence to sequence approach since the molecule input is taken a string format called simplified molecular-input line-entry system (SMILES). © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.