Hybrid deep neural network for automatic detection of COVID-19 using chest x-ray images

The 2019 coronavirus (COVID-19), started in China, spreads rapidly around the entire world. In automated medical imagery diagnostic technique, due to presence of noise in medical images and use of single pre-trained model on low quality images, the existing deep network models cannot provide the optimal results with better accuracy. Hence, hybrid deep learning model of Xception model & Resnet50V2 model is proposed in this paper. This study suggests classifying X-ray images into three categories namely, normal, bacterial/viral infections and Covid positive. It utilizes CLAHE & BM3D techniques to improve visual clarity and reduce noise. Transfer learning method with variety of pre-trained models such as ResNet-50, Inception V3, VGG-16, VGG-19, ResNet50V2, and Xception are used for better feature extraction and Chest X-ray image classification. The overfitting issue were resolved using K-fold cross validation. The proposed hybrid deep learning model results high accuracy of 97.8% which is better than existing techniques.

Enhancing Pharmaceutical Supply Chain Resilience: A Study of Pharmaceutical Companies in Multiple Geographies

Background: Pharmaceutical businesses had enormous difficulties in product distribution during COVID-19, and the solution to this perpetual issue is a resilient supply chain. Aim(s): The study aims to understand the vulnerabilities to which it subjected the pharmaceutical product distribution supply chains during the COVID-19 pandemic and further develop an adaptive model through which the pharmaceutical product supply chain can enhance its resilience capabilities. Material(s) and Method(s): The conceptual model is developed for the supply chain of pharmaceutical companies based on the literature survey, and then the conceptual model is explored through factor analysis. Researchers have developed a validated model after a statistical analysis using Cronbach's alpha. Subjective analysis has concluded that the pharmaceutical supply chain's resilience is driven by factors such as "trade cost," which comprises transport cost, business practices, and raw material sourcing cost;"shock propagation," which comprises country-specific shocks, production shocks, and policy changes;and "technological infrastructure bottleneck," which relates to the availability of cold chain storage warehouses and refrigerated transport vehicle facilities. Result(s): An empirical model pertaining to supply chain resilience may be further studied with different geographies, like Pune, Hyderabad, and Delhi NCR, for the purpose of generalizing the study. Conclusion(s): The identified major factors were trade cost, shock propagation, and technological infrastructure bottlenecks. The sensitivity of the issue under investigation required a personal touch to the survey, as the COVID-19 pandemic had left these respondents emotionally vulnerable. As COVID-19 is the recent catastrophe that has hit humanity, it has made the pharmaceutical product distribution channel vulnerable during the pandemic. This difficult time of pandemic has really tested the pharmaceutical products' supply chain capabilities as well.Copyright © 2023, Association of Pharmaceutical Teachers of India. All rights reserved.

Face mask detection using transfer learning and OpenCV in live videos

In 2019, we have seen the biggest epidemic of the century, which claimed many lives worldwide. The epidemic has in fact changed our life in many ways. It changed the way we interact with people. Wearing a mask is now the new normal. Though now the vaccine for the disease is available, still wearing a mask can save us from Covid19, its variants, and other contagious diseases.Especially at places where the large gathering is expected wearing a mask can be made mandatory and our proposed framework can do its monitoring through CCTV cameras.So in this research, we build a deep learning-based framework to detect whether some person is wearing a mask or not through the live video stream. We used a total of three state-of-The-Art transfer learning methods to train our system and used OpenCV to detect faces in the live video stream. We found that efficientnetB1 achieved the highest accuracy of 97.75%. © 2022 IEEE.

The Current Outbreak of COVID-19 with Reference to India

This review summarizes the outbreak of viruses causing the fatal disease, which is highly pathogenic and efficiently transmitted from human to human. This first began in Wuhan, China, and now turned into an epidemic situation worldwide. A novel coronavirus (2019-nCoV) or severe acute respiratory syndrome coronavirus(SARS-CoV)-2 belongs to beta-coronavirus genera originated in bats due to highly identical genome which leads to bat coronavirus. This review highlights the study of Indian Council of Medical Research, India, which determined the detection of pathogenic coronavirus in two different species of Indian bats. Indian Council of Medical Research, India, has successfully isolated the COVID-19 virus strain which was the first step towards diagnosis and the development of vaccines in the country. The outbreaks of coronavirus received worldwide attention for overcoming the challenges faced during this current pandemic as there is no clinically approved antiviral drug or vaccine available yet. However, preventive measures and different treatments were taken to cope with this viral outbreak. In response to this global outbreak, this review tries to explain the Virology, Epidemiology and pathogenesis, and discusses the diagnosis and treatment strategies of COVID-19. This review emphasizes the current update of COVID-19.Copyright © 2021 Bentham Science Publishers.

Comparative Analysis of Machine Learning Models to Predict Depression, Anxiety and Stress

Mental Health Issues are a hidden pandemic which will emerge in the upcoming years. As the world witnessed COVID-19 pandemic and went into lockdown, the cases of Depression, Anxiety and Stress skyrocketed than ever before. This has given rise to the need for exploring the interdisciplinary field of Artficial Intelligence and Psychometry. In this paper, we propose compare various machine learning and ensemble learning methods, on the survey dataset comprising of the DASS-42 Psychometric Test Results and Demographic information. Random Forest, Decision Tree, Support Vector Machine (SVM), AdaBoost, CatBoost, and Extreme Gradient Boosting (XGBoost) are used to classify the level of Depression, Anxiety and Stress into normal, mild, moderate, severe and extremely severe categories. In our experiments on the dataset, Support Vector Machine outperformed and reached a final F1-measure of 94%, 95% and 91% in the prediction of Depression, Anxiety and Stress, respectively. © 2022 IEEE.

A hybrid convolutional neural network model to detect COVID-19 and pneumonia using chest X-ray images

A hybrid convolutional neural network (CNN)-based model is proposed in the article for accurate detection of COVID-19, pneumonia, and normal patients using chest X-ray images. The input images are first pre-processed to tackle problems associated with the formation of the dataset from different sources, image quality issues, and imbalances in the dataset. The literature suggests that several abnormalities can be found with limited medical image datasets by using transfer learning. Hence, various pre-trained CNN models: VGG-19, InceptionV3, MobileNetV2, and DenseNet are adopted in the present work. Finally, with the help of these models, four hybrid models: VID (VGG-19, Inception, and DenseNet), VMI(VGG-19, MobileNet, and Inception), VMD (VGG-19, MobileNet, and DenseNet), and IMD(Inception, MobileNet, and DenseNet) are proposed. The model outcome is also tested using five-fold cross-validation. The best-performing hybrid model is the VMD model with an overall testing accuracy of 97.3%. Thus, a new hybrid model architecture is presented in the work that combines three individual base CNN models in a parallel configuration to counterbalance the shortcomings of individual models. The experimentation result reveals that the proposed hybrid model outperforms most of the previously suggested models. This model can also be used in the identification of diseases, especially in rural areas where limited laboratory facilities are available. © 2022 Wiley Periodicals LLC.

Smart Intravenous Drip Monitoring System with Bubble Detection Indicator using IoT

With the increasing emergence of new diseases and ailments, health care facilities are gradually becoming an integral part of our lives. The most important thing in these healthcare facilities is safety of the patients. With an ever increasing population, automatic health monitoring systems are gaining importance too. They are highly sought after since they provide exact information while remaining cost-effective and minimizing medical practitioners' and patients' stress. Among various health procedures, intravenous therapy is an often used medical method to deliver fluids, medications and nutrients into a patient's veins. It requires manual monitoring by the medical staff and negligence can cause fatal risks. Further, with COVID-19, the burden and pressure on medical professionals increased manifold which enhanced the chances of negligence. The aim of this paper is to create a practical, budget-friendly and Internet of Things based monitoring device for intravenous therapy. The proposed system consists of a light dependent resistor for detection of fluid level in the IV drip bottle and air bubble formation in IV tube. When the fluid in the IV drip bottle reaches a critical level or air bubble is detected, a notification will be received by the concerned staff. Also, an auditory and visual alert will notify the people present in the patient's room. This will enable the medical professionals to immediately attend to the patient without the hassle of periodic monitoring. © 2022 IEEE.

Business Diplomacy and Lobbying During Pandemic

The COVID-19 pandemic has brought with it unprecedented disruptions in the business climate as well as in the geopolitical narratives thereof. This is gradually, yet significantly, influencing the relationships between multinational enterprises and small and medium businesses on one hand, and state and non-state actors on the other. This chapter analyzes the influence that business diplomacy has on reinforcing this relationship to ensure sustainability of businesses and protecting their interests internationally. The chapter also reviews thematic literature to investigate the nature and direction of discourse on business diplomacy. It also outlines the actors in the business diplomacy process and examines their role in the evolving global geo-economics characterized by a pandemic-induced world. The chapter also presents the enablers of a business diplomacy process. Moreover, based on critical geo-economic situations emanating from the pandemic, the chapter examines the influence of business diplomacy on pertinent global business issues including 5G technology, supply chain disruptions and opportunistic takeovers, among others. There is historical and contemporary evidence which has been discussed to develop an improved understanding of the role that business diplomacy plays in safeguarding corporate interests. The chapter carries immense implications for businesses as well as policy-makers in their urge to undertake pragmatic means to revive the world economy. © 2022 selection and editorial matter, Faisal Ahmed and Hardik Gupta.

COVID-19 and restless leg syndrome

The deadly COVID-19 pandemic has affected more than 200 million people and has killed more than 4 million people worldwide. A number of neurological manifestations of COVID-19 infection have been described, though sleep related problems are less reported. We describe a case of restless leg syndrome related to COVID-19 infection, which improved after treatment with dopaminergic therapy. We discuss the possible pathophysiological mechanisms underlying sleep disturbances and RLS in a patient with COVID-19. The sleep related problems could be another important manifestation of the deadly virus and need to be actively looked for.

Contactless Palm Vein Biometric Authentication and Health Monitoring Device

Today, the novel COVID-19 pandemic has taken a significant toll on countries worldwide and impacted the lives and well-being of people across nations. Measures need to be taken to slow down the spread of such viruses, which can be reduced by taking proper precautions to avoid unnecessary contact and incurring hygienic habits. People can become infected by touching infected objects or surfaces, then touching their eyes, nose or mouth. We need proper and hygienic authentication systems for granting access to authorized users at various places such as companies, universities, banks, etc. Today there are multiple biometric systems that can serve this purpose, but to maintain hygiene we need systems that are contactless in nature thus to reduce spread of infections through touch. The palm vein pattern is distinctive biometric identity of individuals that is also a safe and reliable biometric authentication technique. The major advantage of palm vein biometric that we consider in our proposed system is that this kind of authentication can be done in a contactless way. Here, we propose an authentication system that uses palm vein pattern biometric to authenticate users in a contactless way. We have also added the functionalities of temperature detection and blood oxygen level detection to this system. These health symptoms are the major symptoms of various fatal virus infections such as the coronavirus. By checking the users for these symptoms before granting access, we can further limit the spread of infections and also help detect the infected patients. The proposed system does this in a contactless way. © 2021 IEEE.

Diagnosis of COVID-19 from chest X-ray images using Deep Learning Model

COVID-19 (Coronavirus-2019) is a disastrous pandemic which has affected the whole world damaging the whole ecosystem specially health. Researchers around the world have been contributing to the advancement in these conditions around the world. Medical practitioners have found that chest X-Ray images can used to detect whether a person suffers from covid-19 or not due to anomalies in chest radiography images. Continuing this motivation we have taken around 700 chest X ray images from different resources and applied two different transfer learning techniques to build a model which can detect the existence of covid-19 in a person. It uses VGG16 and Resnet50 deep learning models which utilize transfer learning to train their parameters. We have trained our both models for 50 epochs. VGG16 gives 76% of accuracy on test data and Resnet50 gives 85% accuracy on test data. We have tried to engineer thresholds for probability of classification thus changing specificity and sensitivity and also evaluated our models on various metrics such as classification report, confusion matrix heatmap, roc-auc score. This is by no means to be used for any medical procedures but can help other researchers to take some useful insights from it and carry forward the learning in building something which is production ready for contribution in our fight against COVID-19. © 2021 IEEE.

Anaplastic large cell lymphoma presenting as hemophagocytic lymphohistiocytosis

INTRODUCTION: HLH is a rare syndrome with an incidence of 1.2 cases per 1,000,000 individuals per year. Diagnosis of HLH is challenging given its varied presentation and strict diagnostic criteria. DESCRIPTION: A 40 year-old female with no medical history presented with two weeks of shortness of breath and cervical lymphadenopathy. She was evaluated in the ED on two separate occasions with workup significant for CT chest findings of mediastinal and axillary lymphadenopathy, pulmonary nodules and a left pleural effusion. Within one week, she presented a third time with worsening shortness of breath, fatigue and fevers. She was febrile, tachycardic, hypoxemic, leukopenic and thrombocytopenic. A repeat CT chest revealed increased lymphadenopathy and pleural effusion. She was started on empiric broad spectrum antibiotics and admitted to the hospital. Infectious workup, pleural fluid cytology and peripheral blood flow cytometry were negative. Bone marrow biopsy had markedly increased histiocytes and hemophagocytosis, but no evidence of malignancy. She developed progressive hypoxemia requiring intubation, shock requiring vasopressors and multiorgan failure. Fevers persisted with worsening pancytopenia, DIC, and markedly elevated inflammatory markers, notably a ferritin >33000. Further workup revealed positive Covid antibodies, elevated parvovirus IgG, elevated fungal markers and Candida in respiratory cultures. Secondary HLH due to sepsis or following COVID infection in the setting of malignancy was suspected. A course of antifungals and high dose steroids was given without improvement. Etoposide was considered, but avoided given possible sepsis, multiorgan failure and pancytopenia. A lymph node biopsy was performed with evidence of hemophagocytosis and ALK-positive anaplastic large cell lymphoma. Despite aggressive treatment, she developed progressive multiorgan failure resulting in death. Autopsy confirmed anaplastic large cell lymphoma with metastasis to the lymph nodes, lungs, epicardium and stomach. DISCUSSION: Malignancy is a known etiology of HLH in adults, however, few cases of ALK-positive anaplastic large cell lymphoma presenting as HLH have been reported. The rapid progression of HLH in this case stresses the importance of early diagnosis and treatment of HLH while pursuing an aggressive malignancy workup.

COVID-19 Infection in Kidney Transplant Recipients: A Single Centre Study from Northern India.

INTRODUCTION: Coronavirus disease 2019 (COVID-19) has resulted in significant morbidity and mortality worldwide. The pre-immunosuppressed state along with other existing co-morbidities can influence the outcomes of COVID-19 in transplant patients. MATERIAL AND METHODS: This was a single centre prospective cohort study done in kidney transplant recipients (KTRs), who underwent kidney transplantation (from December 2012 to November 2020), who were actively followed up at our centre and were diagnosed with COVID-19 disease between 1 April and 30 November 2020. RESULTS: A total of 62 kidney-transplant recipients tested positive for COVID-19. Their median age was 39 (19-61). Males were predominantly infected (87.1%). Fever was the most common symptom (77.42%). Thirteen (20.9%) had mild form of disease, 32 (51.6%) had moderate form and 17 (27.4%) had severe disease. Based on initial symptom, 18 (29.03%) were given home treatment, 29 (46.7%) were treated in isolation wards and 15 (24.1%) were treated in intensive care unit. Decrease in the dose of immunosuppressant (anti-metabolites in 67.7%, calcineurin inhibitor in 22.5%) was predominantly carried out as the initial mode of treatment. Remdesivir in 64.5% and anticoagulant therapy in 54.84% were given as a modality of treatment. Mortality rate in our study was 14.5%. CONCLUSIONS: Patients of kidney transplant are at high risk of getting infected with COVID-19, due to their immunosuppressed state. Initial symptoms in KTRs with COVID-19 are similar to that of the general population. Mortality rate is comparatively higher in KTRs as compared to general population.

Our renal transplant protocols during COVID-19 times - A prospective study from high-volume tertiary center of North India

Introduction: There is a strong temporal association between the increase in coronavirus disease-2019 (COVID-19) infections and a striking reduction in overall solid-organ transplantation procedure. The priority is to select uninfected donors to transplant uninfected recipients while maintaining safety for health-care systems in the backdrop of a virulent pandemic. Our current communication shares a protocol for donor and transplant recipient selection during the COVID-19 pandemic to continue lifesaving kidney transplantation. This protocol was created utilizing the guidelines of various organizations and from the clinical experience of the authors and will continue to evolve as more is understood about severe acute respiratory syndrome coronavirus 2 and how it affects organ donors and transplant recipients. Objectives: In this study, we aimed to review the published literature and compare it with our kidney transplantation is done in 115 patients in our hospital from July 2020 to February 2021 in the time of COVID-19 and what protocols we follow before transplant to reduce the risk of COVID-19 infection. This article also contains currently available immunosuppressive strategies. Methods: A prospective study was done in Mahatma Gandhi Hospital Jaipur for the transplant done from July 2020 to February 2021. A total of 115 patients from 20 th July to 28 th February were considered for the study. Results: All 115 patients who were COVID negative on reverse transcriptase-polymerase chain reaction assay and high-resolution computed tomography were considered for the transplant. COVID status on discharge for all 115 recipients was negative. During workup, two donors were found COVID-19 positive and were shifted to COVID center for treatment and their transplant postpone. Out of 115 renal transplant patients, only 1 patient became COVID-19 positive after 5 days of operation. Conclusions: Every transplant center should put its local policy about transplantation depending on the state of the epidemic in the country. Nowadays, transplantation programs should be limited to urgent cases. We suggest lowering the threshold for diagnosis of COVID-19 among renal transplant recipients.

Work life balance during Covid pandemic

Work life balance has always been challenging to achieve for every individual working in different sector especially for women as it is not easy to do day to day chores at home and office work together while working from home. This paper intent to discover the mental and physical exhaustion and difficulties and challenges encountered by employees during pandemic and small solutions or advices for it. The existing problem has pushed the employees to increase their limits to fight in such situation. And have made them capable of managing both job and home under one roof leading more towards work life integration than work life balance. Employees who have accepted the changes and embraced it and have upgraded themselves with the digitalization are able to meet all the demands relating to time, home, family, work, health and responsibilities. © 2021 Karadeniz Technical University. All rights reserved.