Improving Stroke Care in Times of the COVID-19 Pandemic Through Simulation: Practice Your Protocols!

During the coronavirus disease 2019 (COVID-19) pandemic, infectious disease control is of utmost importance in acute stroke treatment. This is a new situation for most stroke teams that often leads to uncertainty among physicians, nurses, and technicians who are in immediate contact with patients. The situation is made even more complicated by numerous new regulations and protocols that are released in rapid succession. Herein, we are describing our experience with simulation training for COVID-19 stroke treatment protocols. One week of simulation training allowed us to identify numerous latent safety threats and to adjust our institution-specific protocols to mitigate them. It also helped our physicians and nurses to practice relevant tasks and behavioral patterns (eg, proper donning and doffing PPE, where to dispose potentially contaminated equipment) to minimize their infectious exposure and to adapt to the new situation. We therefore strongly encourage other hospitals to adopt simulation training to prepare their medical teams for code strokes during the COVID-19 pandemic.Copyright © 2020 Lippincott Williams and Wilkins. All rights reserved.

Administration of Vaccine Mechanism for COVID-19 Using Blockchain

Information related to Covid-19 either it is vaccination status of the country or the active Covid-19 cases both are the confidential matters. The privacy is utmost important concern in pandemic situation to secure access of patient vaccine data. Blockchain technique is one of the good techniques that affirm the privacy and data security. The consensus mechanisms in blockchain confirm that data stored in it, is authentic and secured. Proof of Work is one of the consensus algorithms, where miners in the blockchain network solves the puzzle and receive the reward accordingly. The difficulty level of the puzzle decides the security of the data in the network. Hence, this paper proposes blockchain based framework to store the vaccination data of patient by enhancing security using proof of work consensus algorithm. The performance of the proposed framework is measured on different level of difficulties, corresponding to time. The result shows that higher the difficulty level, take more time to solve the puzzle, results in more secure data. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Machine Learning, Deep Learning-Based Optimization in Multilayered Cloud

The ongoing COVID-19 pandemic has resulted in the loss of lives and economic losses. In this scenario, social distancing is the only way to protect ourselves. In such a scenario, to boost the economy, a large number of industries and businesses have shifted their system to cloud, for example education, shipping, training and many more globally. To support this transition cloud services are the only solution to provide reliable and secure services to the user to sustain their business. Due to this, the load on the existing cloud infrastructure has drastically increased. So it is the responsibility of the cloud to manage the load on the existing infrastructure to maintain reliability and provide high-quality services to the user. Task allocation in the cloud is one of the key features to optimize the performance of cloud infrastructure. In this work, we have proposed a prediction-based technique using a pre-trained neural network to find a reliable resource for a task based on previous training and the history of cloud and its performance to optimize the performance in overloaded and underloaded situations. The main aim of this work is to reduce faults and provide high performance by reducing scheduling time, execution time, average start time, average finish time and network load. The proposed model uses the Big Bang-Big Crunch algorithm to generate huge datasets for training our neural model. The accuracy of the BB-BC ANN model is improved with 98% accuracy. © 2022 selection and editorial matter, Punit Gupta, Mayank Kumar Goyal, Sudeshna Chakraborty, Ahmed A Elngar;individual chapters, the contributors.

Perinatal COVID-19 Infection and Outcomes: A Retrospective Observational Study from a Low–Middle Income Setting

Aim: Coronavirus disease 2019 (COVID-19) pandemic is an ongoing emergency with limited data on perinatal outcomes. The aim of the study was to describe key maternal, perinatal, and neonatal outcomes of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection from low–middle income settings. Materials and methods: We conducted a retrospective observational study in a tertiary level public hospital in India. All pregnant women admitted from May 2020 to July 2020 were included in the study. Maternal demography, medical and obstetric complications, clinical characteristics, and investigations were described. Symptomatic infected women were compared with the asymptomatic group for important outcomes. Key perinatal outcomes such as early pregnancy losses, fetal distress, stillbirths, and placental changes were evaluated. Neonatal characteristics of SARS-CoV-2 positive and negative pregnancies were described and compared. Results: Among the 702 pregnant women enrolled, the incidence of SARS-CoV-2 infection was 16.2%, with the majority being asymptomatic. Infected women had an increased mortality, while symptomatic women had a significant risk of stillbirth. Mean placental weight of infected women was significantly higher. Neonatal infection rate was 9.1%, with 50% manifesting mild respiratory symptoms without any mortality. Conclusion: This study provides a comprehensive description of important antenatal, intrapartum and neonatal complications and outcomes in a low–middle income setting characterized by high disease burden and an overwhelmed health care system. Clinical significance: Incidence of SARS-CoV-2 infection in pregnancy was 16.2%. The symptomatic infected women had increased stillbirth and mortality. Neonatal transmission was seen in 9.1% with good survival.

AN INTEGRATIVE CONCEPTUAL EXPLORATION ON ETIOPATHOGENESIS OF POST-COVID-EOSINOPHILIA WITH SPECIAL REFERENCE TO VATAJA KASA: A REVIEW

COVID19 has contributed to one of the most concerning issues in the health field because of its extremely high transmission capacity, mortality rate, diverse nature, and adverse effects. Due to the current COVID-19 pandemic, all efforts are currently focused on understanding this new infectious disease, specifically in unravelling the pathophysiological mechanisms for prevention of the disease, literal treatment, and avoiding the lingering symptoms or complications of COVID 19. Eosinophilia is one of the standard laboratory findings after the COVID 19 infection. Out of 242 patients visited at Post-COVID OPD of Kayachikitsa department, ITRA, Jamnagar;in 159 patients, the differential eosinophilic count increased while both differential and the absolute eosinophilic count was raised in 134 patients. Many studies suggest that SARS-CoV-2 was directly or indirectly responsible for eosinophilia due to infection or recovery. The conventional treatment for eosinophilia lowers its count but may not provide satisfactory relief. Also, reducing eosinophil count below the specified limit is not advisable in concern with COVID 19 as eosinopenia may indicate a poor prognosis, and in this way, the eosinophil counts have important value as an indicator of severity as well as complications regarding the patients of COVID-19. Therefore, it is indispensable to understand the precise mechanism of eosinophilia integratively, i.e., by conventional and ancient science, to understand the exact etiopathology and to determine the proper treatment protocol. Considering symptomatology, eosinophilia so far can be correlated with Vataja Kasa from an Ayurvedic point of view. This review aims to illustrate the possible integrative knowledge of eosinophilia with respect to COVID 19.

Quantitative Estimation of Anti-Spike SARS-CoV-2 IgG Antibody Response After Covishield Vaccination in Healthcare Workers

Background: Most studies of COVID vaccination focused on cell-mediated immunity and serum IgG antibodies, overlooking the role of anti-Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) neutralizing IgA antibodies in preventing viral infection. SARS-CoV-2 vaccine generates variable Anti-Spike IgG responses following one or two vaccine doses in almost all individuals for protection. Aim: The study aimed to quantify and estimate the Anti-Spike SARS-CoV-2 IgG antibody response after the second dose of the Covishield vaccine in healthcare workers (HCWs) over the time frame of one, three, and six months. Material and Methods: 30 HCWs who had received both doses of the Covishield vaccine were selected and divided into three groups based on the time elapsed after the second dose of vaccine for serological analysis. Post-vaccination antibody responses were measured using the SARS-CoV-2 IgG Quantitative assay (detection threshold: & GE;50 AU/ml) using chemiluminescent microparticle immunoassay (CMIA). Data were analyzed using the Kolmogorov-Smirnov test, Kruskal-Walli's test, and Mann-Whitney U test. Result: Vaccination leads to measurable anti-spike IgG antibodies in HCWs. Only 1 individual was seronegative. The highest antibody titer was reported after one month of the second dose (3615.3 AU/ml). The lowest antibody titer (491.5 AU/ml) was seen after six months of the second dose of Covishield is statistically significant. Conclusion: Anti-Spike SARS-CoV-2 IgG antibody determination is necessary for an immune response after vaccination. This titer decreases with time consequently as the duration after the second dose of the Covishield vaccine increases. This helps assess the requirement of a booster dose for effective immunity against coronavirus.

Correlation of Radiological Findings on High-Resolution Computed Tomography Chest in Vaccinated and Unvaccinated COVID-19 Patients from Eastern India

Background: In this coronavirus disease-2019 (COVID-19) pandemic, safe and effective preventative vaccines are essential to contain the pandemic, which has had severe medical, economic, and societal consequences, despite some people still becoming infected after receiving immunisation. Methods: A total of 200 patients were examined and split into two groups: (1) 100 consecutive COVID-19-positive cases who had been vaccinated and (2) 100 consecutive COVID-19-positive patients with no vaccination. At the time of the scan, the patient's vaccination status was noted. Results: The computed tomography severity score (CTSS) of unvaccinated individuals was found to be considerably greater than that of partly or fully vaccinated patients (median 13 vs. 7, P < 0.001). Completely vaccinated individuals had a considerably lower median CTSS than partly vaccinated patients (6 vs. 9, P < 0.001). Conclusions: Individuals should be thoroughly vaccinated to avoid major lung disease. As a result, stronger dedication and motivating efforts should be made worldwide to improve the COVID-19 vaccination program.

IVMCT: Image Visualization based Multiclass Malware Classification using Transfer Learning

Computer systems have made it possible to transfer human life from the real world to virtual reality. This process has been accelerated by the Covid-19 virus. Cybercriminals have also switched from a real-life to a virtual one. Online, committing a crime is far easier than in real life. Cybercriminals often use malicious software (malware), to launch cyber-attacks. Apart from this polymorphic and metamorphic malware are used that use obfuscation techniques to create new malware variants. To effectively battle new malware types, you'll need to employ creative approaches that depart from the conventional. Traditionally signature-based techniques are used with machine learning algorithms to detect malware that is unable to catch its variants. Deep learning (DL), which differs from typical machine learning methods, might be a potential approach to the challenge of identifying all varieties of malware. In the present study, an IVMCT framework is introduced which classifies malware using transfer learning. For this purpose, the MalImg dataset is used which is based on grayscale images converted from binaries of malware. The comparison of IVMCT is done with existing techniques which shows that our technique is better than existing techniques. © 2022, Mathematical and Research Society. All rights reserved.

Optimal Resource Allocation during Epidemics using Reinforcement Learning

Epidemics can prove to be disastrous, which has been further emphasized by the recent COVID-19 pandemic, and several countries like India lack sufficient resources to meet the population's needs. It is therefore important that the limited testing and protective resources are utilized such that the disease spread is minimized and their reach to the most vulnerable demographic is maximized. This paper studies the scope of intelligent agents in aiding authorities with such policy-making decisions. This is done by exploring the performance of various action selection methods on custom environments dealing with socio-economic groups and Indian states. Experiments using multi-armed bandit techniques provide greater insight into administrative decisions surrounding resource allocation and their future potential for greater use in similar scenarios. © 2022 IEEE.

Paraquat ingestion and COVID-19 infection: deadly double hit

Objective: To describe two cases of intentional paraquat ingestion as self-harm in patients with COVID-19 infection. Methods: We retrospectively analysed poisoning cases (accidental and suicidal), admitted to a dedicated COVID-19 care facility at our institute. As a protocol, all patients coming to our emergency department were reverse transcriptase-PCR tested for novel coronavirus disease 2019 (nCOVID-19) before being admitted to a high dependency unit (HDU) intensive care unit (ICU). If they tested positive for COVID they were transferred to a dedicated COVID care facility. These patients were treated according to the protocol developed for poisoned patients. All patients were followed until discharge or death. We describe the details of 2 patients with intentional paraquat ingestion. Results: We received nine patients at our dedicated COVID care facility created at our institute during the pandemic. Of these 9, 2 patients had ingested paraquat and presented with acute respiratory distress syndrome (ARDS). Both patients were in the third decade of life and the economic crisis due to the pandemic was the trigger for the ingestion of paraquat. Both had ingested a significant amount of commercially available paraquat. They had significant acute kidney and liver injury at presentation and required dialysis. Haemoperfusion was not performed as the charcoal filters were not available. The clinical picture and chest X-rays were similar to the findings observed in severe COVID-19 patients. Since patients were hypoxic at presentation, monoclonal antibodies were not indicated and were not administered. Both patients were given dexamethasone (6mg daily), as per the “COVID treatment protocol”. We did not administer pulse doses of methylprednisolone or cyclophosphamide due to concerns over exacerbating COVID infection. One of the patients developed significant oesophageal ulceration leading to massive haematemesis. Both developed spontaneous pneumomediastinum and succumbed to their illness after an average stay of 8 days in the HDU. Conclusion: During the pandemic, paraquat ingestion for selfharm with COVID-19 infection poses a challenge to treating physicians. Since the clinical picture of ARDS, is similar to severe COVID infection, the management with immunosuppressive agents becomes difficult.

Fault aware intelligent resource allocation using Big Bang-Big Crunch trained neural network for cloud infrastructure

This Ongoing COVID-19 epidemic situation, which has resulted in the loss of lives and economics. In this scenario, social distancing is the only way to prevent ourselves. In such a scenario to boost the economy, a globally large number of industries and businesses have shifted their system to cloud-like education, shipping, training and many more globally. To support this transition cloud services are the only solution to provide reliable and secure services to the user to sustain their business. Due to this, the load over the existing cloud infrastructure has drastically increased. So it is the responsibility of the cloud to manage the load over the existing infrastructure to maintain reliability and serve high-quality services to the user. Task allocation in the cloud is one of the key features to optimize the performance of cloud infrastructure. In this work, we have proposed a prediction-based technique using a pre-trained neural network to find a reliable resource for a task based on previous training and history of cloud and its performance to optimize the performance under the overloaded and under loaded situation. The main aim of this work is to reduce the fault and provide high performance by reducing scheduling time, execution time and network load. The proposed model uses the Big Bang Big Crunch algorithm to generated huge datasets for training our neural model. The accuracy of the BB-BC-ANN model is improved with 98% accuracy. © 2022 - IOS Press. All rights reserved.

Impact of Covid Vaccination: A Machine Learning Approach

The speed with which the COVID-19 pandemic has spread is astounding, but the global response is based on lessons learned from earlier sickness outbreaks in recent years. In a human test immunization study, solid volunteers are given a test antibody and afterward intentionally presented to the life from making the sickness check whether the antibody works or not. Nonetheless, there are significant moral contemplations that should be tended to especially for another infection like COVID-19, which perhaps not yet completely comprehend as yet it tends to figure out how to treat. It could be hard for the clinical local area and expected volunteers to appropriately appraise the possible dangers of taking an interest in a COVID-19 human test study. The investigation is completed utilizing managed AI calculations where it has been attempted to foresee the yield with greatest exactness. © The Electrochemical Society

Spontaneous pneumothorax complicating COVID-19 pneumonia in a patient having silicosis

Spontaneous pneumothorax is a very uncommon occurrence in patients having coronavirus disease 2019 (COVID-19) pneumonia. It is mostly seen due to barotrauma in patients receiving mechanical ventilation. Although it may occur at different courses of COVID pneumonia and in patients with no underlying lung disease, it has been seen in patients having underlying asthma, chronic obstructive pulmonary disease, and bronchiectasis. This report describes spontaneous pneumothorax in a silicosis patient during the course of COVID-19 pneumonia with successful outcomes. Possible mechanism of pneumothorax in COVID-19 pneumonia and contributing the role of silicosis is also mentioned with the importance of detecting such complications in time to reduce mortality in such patients.

Spectrum of barotraumatic events in COVID-19 patients on high-resolution computed tomography

Barotrauma has many different presentations, including pneumothorax, subcutaneous emphysema, pneumoperitoneum, and pneumomediastinum. We have presented and analyzed some interesting cases of barotrauma in this case series. Case 1 in our series developed a thin-walled new cavity due to barotrauma, mimicking pneumatocele and fungal cavity. Case 2 presented with coexistence of pneumothorax and cavity with fungal infestation. Severity of barotrauma due to positive pressure ventilation has been shown in case 3. An interesting case of barotrauma in a 36-week primigravida, post cesarean section, causing dehiscence of scar, presented as case 4 in our series. Early and rapid imaging diagnosis of barotrauma should be pursued. In patients with mechanical ventilation, identifying small changes in imaging characteristics of cavitary lesions, such as fungal, bacterial, or transient cavities, would aid physicians in offering a correct treatment plan.

The rising burden of invasive fungal infections in COVID-19, can structured CT thorax change the game

Background: The occurrence of invasive fungal infections in COVID-19 patients is on surge in countries like India. Several reports related to rhino-nasal-sinus mucormycosis in COVID patients have been published in recent times;however, very less has been reported about invasive pulmonary fungal infections caused mainly by mucor, aspergillus or invasive candida species. We aimed to present 6 sputum culture proved cases of invasive pulmonary fungal infection (four mucormycosis and two invasive candidiasis) in COVID patients, the clues for the diagnosis of fungal invasion as well as difficulties in diagnosing it due to superimposed COVID imaging features. Case presentation: The HRCT imaging features of the all 6 patients showed signs of fungal invasion in the form of cavities formation in the pre-existing reverse halo lesions or development of new irregular margined soft tissue attenuating growth within the pre-existing or in newly formed cavities. Five out of six patients were diabetics. Cavities in cases 1, 2, 3 and 4 of mucormycosis were aggressive and relatively larger and showed relatively faster progression into cavities in comparison with cases 5 and 6 of invasive candidiasis. Conclusion: In poorly managed diabetics or with other immunosuppressed conditions, invasive fungal infection (mucormycosis, invasive aspergillosis and invasive candidiasis) should be considered in the differential diagnosis of cavitary lung lesions.

COVITALE 2020 from eastern Indian population: imageologists perspective, a learning curve

Background: High-resolution computed tomography (HRCT) chest becomes a valuable diagnostic tool for identifying patients infected with Coronavirus Disease 2019 (COVID-19) in the early stage, where patients may be asymptomatic or with non-specific pulmonary symptoms. An early diagnosis of COVID-19 is of utmost importance, so that patients can be isolated and treated in time, eventually preventing spread of the disease, improving the prognosis and reducing the mortality. In this paper, we have highlighted our radiological experience of dealing with the pandemic crisis of 2020 through the study of HRCT thorax, lung ultrasonography, chest X-rays and artificial intelligence (AI). Results: Results of CT thorax analysis have been given in detail. We had also compared CT severity score (CTSS) with clinical and laboratory parameters. Correlation of CTSS with SpO2 values and comorbidities was also studied. We also analysed manual CTSS with the CTSS scored calculated by the AI software. Conclusions: CTSS and use of COVID-19 Reporting and Data System (CORADS) result in accuracy and uniform percolation of information among the clinicians. Bed-side X-rays and ultrasonography have played a role where the patients could not be shifted for CT scan. The possibility of predicting impending or progression of hypoxia was not possible when SpO2 mapping was correlated with the CTSS. AI was alternatively tried with available software (CT pneumonia analysis) which was not so appropriate considering the imaging patterns in the bulk of atypical category.

Use of office based transnasal oesophagoscopy in management of Head & Neck conditions during the COVID-19 pandemic at the Royal Albert Edward Infirmary, Wigan, United Kingdom

OBJECTIVES: To assess the clinical and cost effectiveness of transnasal oesophagoscopy (TNO) in cases of suspected upper aerodigestive tract malignancy and define its role as a safe alternative to panendoscopy. We have also analysed if the implementation of TNO during the COVID-19 pandemic was beneficial in order to provide uninterrupted care to the patients with the limited resources available in these challenging times. METHODS: All patients who underwent TNO guided biopsies or dilatation attempted over a 7 month period during COVID- 19 pandemic were included by searching the hospital and department database at The Royal Albert Edward Infirmary. A comparative group of patients who underwent panendoscopy over 9 months were included for comparison. Demographic data, histological diagnosis, second procedure and cost involved were recorded. RESULTS: During this period, 20 TNO procedures (16 biopsies and 4 dilatations) were attempted which were compared with 20 panendoscopy procedures. The diagnostic accuracy of TNO biopsy for identifying benign and malignant pathology was 81.1%. The sensitivity and specificity for identifying malignancy was 76.9% and 100% respectively. The most common lesion location was laryngeal (43.8%) followed by oropharyngeal (37.5%), more specifically located at the tongue base. The median waiting period between the procedure being listed and TNO being performed was 5.5 days compared to 12 days for panendoscopy. There were 12/16 patients who did not require further interventions for histological diagnosis of the tumor. The TNO procedure was well tolerated with no complications and all were done under local anaesthesia as outpatient procedure without need for admission. TNO resulted in cost saving of 356 per case on a standard NHS tariff. CONCLUSION: TNO is a valuable diagnostic tool for patients with suspected UADT malignancy and dysphagia and has proven to be an asset during the COVID-19 pandemic when we have to make the best use of the limited theatre time and resources. Also, the cost analysis showed that outpatient based TNO can provide significant cost savings for the current standard of care. Furthermore, it has shown better patient tolerability, lesser complications and shortened the time for diagnosis and hence starting timely treatment for these patients.

Student performance prediction for adaptive e-learning systems

The growth of e-learning systems has changed current learning behavior and tries to present a new framework for the learners. E-learning platforms have become common and approachable for a vast set of audiences. The COVID-19 pandemic in 2020 has triggered the application of these online learning platforms. The number of e-learning platforms has been increasing rapidly to fulfill the requirement. This chapter tries to estimate the three factors consisting of learner’s personality, learning style and knowledge level in order to recommend the content that is best suited to the learner. An ensemble approach to solving this problem has been used, which utilizes a genetic algorithm and KNN to find the content appropriate for the learner. © The Institution of Engineering and Technology 2021.