SARS-CoV-2 Infection in Children with Cancer: Experience from a Tertiary Care Center in North India

Introduction Children with cancer are immunocompromised due to the disease per se or anticancer therapy. Children are believed to be at a lower risk of severe coronavirus disease 2019 (COVID-19) disease.Objective This study analyzed the outcome of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in children with cancer.Materials and Methods A retrospective analysis was performed on patients (<= 14 years) with cancer attending the pediatric oncology services of our institute who tested positive for the SARS-CoV-2 infection and those who had COVID-19 disease between August 2020 and May 2021. Real-time reverse transcriptase-polymerase chain reaction performed on the nasopharyngeal swab identified the SARS-CoV-2 infection. The primary endpoints were clinical recovery, interruption of cancer treatment, and associated morbidity and mortality.Results Sixty-six (5.7%) of 1,146 tests were positive for the SARS-CoV-2 infection. Fifty-two (79%) and 14 (21%) patients had hematolymphoid and solid malignancies. Thirty-two (48.5%) patients were asymptomatic. A mild-moderate, severe, or critical disease was observed in 75% (18/24), 12.5% (3/24), and 12.5% (3/24) of the symptomatic patients. The "all-cause" mortality was 7.6% (5/66), with only one (1.5%) death attributable to COVID-19. Two (3%) patients required ventilation. Two (3%) patients had a delay in cancer diagnosis secondary to COVID-19 infection. Thirty-eight (57.6%) had a disruption in anticancer treatment.Conclusion Children with cancer do not appear to be at an increased risk of severe illness due to SARS-CoV-2 infection. Our findings substantiate continuing the delivery of nonintensive anticancer treatment unless sick. However, SARS-CoV-2 infection interrupted anticancer therapy in a considerable proportion of children.

Fusion-Based Segmentation and Classification of Novel Coronavirus or Pneumonia from Chest X-ray Images Using Machine Learning Techniques

The deadfall widespread of coronavirus (SARS-Co V-2) disease has trembled every part of the earth and has significant disruption to health support systems in different countries. In spite of such existing difficulties and disagreements for testing the coronavirus disease, an advanced and low-cost technique is required to classify the disease. For the sense of reason, supervised machine learning (ML) along with image processing has turned out as a strong technique to detect coronavirus from human chest X-rays. In this work, the different methodologies to identify coronavirus (SARS-CoV-2) are discussed. It is essential to expand a fully automatic detection system to restrict the carrying of the virus load through contact. Various deep learning structures are present to detect the SARS-CoV-2 virus such as ResNet50, Inception-ResNet-v2, AlexNet, Vgg19, etc. A dataset of 10,040 samples has been used in which the count of SARS-CoV-2, pneumonia and normal images are 2143, 3674, and 4223 respectively. The model designed by fusion of neural network and HOG transform had an accuracy of 98.81% and a sensitivity of 98.65%. © 2022 IEEE.

What do we know about the idiosyncratic risk of clean energy equities?

The COVID-19 pandemic stimulated the need to invest in clean energy firms for better returns and climate risk mitigation. This study provides a detailed overview of the impact of idiosyncratic risk (IVOL) on excess returns of 95 clean energy stocks. Overall, investors in clean energy stocks are guided by the pessimist group of investors who underprice the high IVOL stocks and demand high-risk premiums to diversify the firm-specific risk. Further, during the COVID-19 period, there is no significant relationship between clean energy excess stock returns and IVOL. During this period, clean energy stocks were exposed to higher information asymmetry, limiting the arbitrage opportunities and producing a weaker return-IVOL relation indicating that clean energy stocks reflect the properties of technology stocks. IVOL has a low level of persistence which may be helpful in forecasting. This study offers valuable insights for regulators and investors from the investment decisions, asset pricing, and diversification perspective.

D-dimer testing: A narrative review.

D-dimer containing species are soluble fibrin degradation products derived from plasmin-mediated degradation of cross-linked fibrin, i.e., 'D-dimer'. D-dimer can hence be considered a biomarker of in vivo activation of both coagulation and fibrinolysis, the leading clinical application in daily practice of which is ruling out venous thromboembolism (VTE). D-dimer has been further evaluated for assessing the risk of VTE recurrence and helping define optimal duration of anticoagulation treatment in VTE, for diagnosing disseminated intravascular coagulation (DIC), and for screening those at enhanced risk of VTE. D-dimer assays should however be performed as intended by regulatory agencies, as their use outside these indications might make them a laboratory-developed test (LDT). This narrative review is aimed at: (1) reviewing the definition of D-dimer, (2) discussing preanalytical variables affecting D-dimer measurement, (3) reviewing and comparing the assays performance and some postanalytical variables (e.g., different units and age-adjusted cutoffs), and (4) discussing the interest of D-dimer measurement across different clinical settings, including pregnancy, cancer, and coronavirus disease 2019 (COVID-19).

Coordination and Cognition in Pure Nutritional Wernicke's Encephalopathy with Cerebellar Degeneration after COVID-19 Infection: A Unique Case Report.

BACKGROUND: Alcoholic cerebellar degeneration is a restricted form of cerebellar degeneration, clinically leading to an ataxia of stance and gait and occurring in the context of alcohol misuse in combination with malnutrition and thiamine depletion. However, a similar degeneration may also develop after non-alcoholic malnutrition, but evidence for a lasting ataxia of stance and gait and lasting abnormalities in the cerebellum is lacking in the few patients described with purely nutritional cerebellar degeneration (NCD). METHODS: We present a case of a 46-year-old woman who developed NCD and Wernicke's encephalopathy (WE) due to COVID-19 and protracted vomiting, resulting in thiamine depletion. We present her clinical course over the first 6 months after the diagnosis of NCD and WE, with thorough neuropsychological and neurological examinations, standardized clinical observations, laboratory investigations, and repeated MRIs. RESULTS: We found a persistent ataxia of stance and gait and evidence for an irreversible restricted cerebellar degeneration. However, the initial cognitive impairments resolved. CONCLUSIONS: Our study shows that NCD without involvement of alcohol neurotoxicity and with a characteristic ataxia of stance and gait exists and may be irreversible. We did not find any evidence for lasting cognitive abnormalities or a cerebellar cognitive-affective syndrome (CCAS) in this patient.

COVID-19 Disease Prediction Using Weighted Ensemble Transfer Learning

Health experts use advanced technological equipment to find complex diseases and diagnose them. Medical imaging nowadays is popular for detecting abnormalities in human bodies. This research discusses using the Internet of Medical Things in the COVID-19 crisis perspective. COVID-19 disease created an unforgettable remark on human memory. It is something like never happened before, and people do not expect it in the future. Medical experts are continuously working on getting a solution for this deadly disease. This pandemic warns the healthcare system to find an alternative solution to monitor the infected person remotely. Internet of Medical Things can be helpful in a pandemic scenario. This paper suggested a ensemble transfer learning framework predict COVID-19 infection. The model used the weighted transfer learning concept and predicted the COVID-19 infected people with an F1-score of 0.997 for the best case on the test dataset. © 2023, Universidad Internacional de la Rioja. All rights reserved.

Effects of dietary intake behavior, food supply, nutrition, and health during the COVID-19 outbreak

The coronavirus disease 2019 (COVID-19) pandemic, which began in 2019, has far-reaching ramifications, including economic losses and health challenges that still affect various parts of the world. During our review, we learned that the entire world is working to stop the spread of the SARS-CoV-2 outbreak. We explore ways that may lower the danger of SARS-CoV-2 contamination and useful strategies to avoid the possibility of SARS-CoV-2 spreading through food. While hygienic protocols are required in the food supply sector, cleaning, disinfection, and the avoidance of cross-contamination across food categories and other related goods at different stages of the manufacturing process remain especially important because the virus can survive for long periods of time on inert materials such as food packaging. Furthermore, personal hygiene (regular washing and disinfection), wearing gloves and using masks, garments, and footwear dedicated to maintaining hygiene provide on-site safety for food sector personnel, supply chain intermediaries, and consumers. Restrictions imposed in response to the pandemic (e.g., closure of physical workplaces, canteens, cafes, restaurants, schools, and childcare institutions), changes in household grocery shopping frequency, individuals' perceived risk of COVID-19, income losses due to the pandemic, and sociodemographic factors are among the factors. The conclusions drawn from this study consider the implications of healthy diets, food system resilience, behavior change, and nutritional imbalance for policymakers and food supply chain participants, as well as the antimicrobial effects of vitamins and nutrients. During a public health crisis, people should eat less, necessitating preventive policies and nutritional advice to deal with this.

Automated Online Exam Proctoring System Using Computer Vision and Hybrid ML Classifier

Importance of online education can be seen especially during the ongoing Covid-19 when going to schools or colleges is not possible. So validity of online exams should also be maintained with respect to traditional pen-paper examinations. However, absence of invigilator makes it easy for the examinees to cheat during the exam. Though there are already many systems for online proctoring, not all educational institutes can afford them as the systems are very expensive. In this paper, we have used eye gaze and head pose estimation as the main features to design our online proctoring system. Therefore, the purpose of this paper is to use these features to create an online proctoring system using computer vision and machine learning and stop cheating attempts in exams. © 2021 IEEE.

Asymptomatic Viral Presence in Early Life Precedes Recurrence of Respiratory Tract Infections.

BACKGROUND: Respiratory tract infections (RTIs) in infants are often caused by viruses. Although respiratory syncytial virus (RSV), influenza virus and human metapneumovirus (hMPV) can be considered the most pathogenic viruses in children, rhinovirus (RV) is often found in asymptomatic infants as well. Little is known about the health consequences of viral presence, especially early in life. We aimed to examine the dynamics of (a)symptomatic viral presence and relate early viral detection to susceptibility to RTIs in infants. METHODS: In a prospective birth cohort of 117 infants, we tested 1304 nasopharyngeal samples obtained from 11 consecutive regular sampling moments, and during acute RTIs across the first year of life for 17 respiratory viruses by quantitative PCR. Associations between viral presence, viral (sub)type, viral load, viral co-detection and symptoms were tested by generalized estimating equation (GEE) models. RESULTS: RV was the most detected virus. RV was negatively associated [GEE: adjusted odds ratio (aOR) 0.41 (95% CI 0.18-0.92)], and hMPV, RSV, parainfluenza 2 and 4 and human coronavirus HKU1 were positively associated with an acute RTI. Asymptomatic RV in early life was, however, associated with increased susceptibility to and recurrence of RTIs later in the first year of life (Kaplan-Meier survival analysis: P = 0.022). CONCLUSIONS: Respiratory viruses, including the seasonal human coronaviruses, are often detected in infants, and are often asymptomatic. Early life RV presence is, though negatively associated with an acute RTI, associated with future susceptibility to and recurrence of RTIs. Further studies on potential ecologic or immunologic mechanisms are needed to understand these observations.

Long-term cognitive performance and its relation to anti-inflammatory therapy in a cohort of survivors of severe COVID-19.

Background and objectives: Long-term cognitive performance data in former critically ill COVID-19 patients are sparse. Current evidence suggests that cognitive decline is related to neuroinflammation, which might be attenuated by COVID-19 related anti-inflammatory therapies. The objective of this prospective cohort study was to study long term cognitive outcomes following severe COVID-19 and the relation to anti-inflammatory therapies. Methods: Prospective observational cohort of patients that survived an intensive care unit (ICU) admission due to severe COVID-19. Six months after hospital discharge, we extensively assessed both objective cognitive functioning and subjective cognitive complaints. Furthermore, patients were stratified in cohorts according to their anti-inflammatory treatment (i.e. no immunomodulatory therapy, dexamethasone, or both dexamethasone and interleukin-6 receptor antagonist tocilizumab). Results: 96 patients were included (March 2020-June 2021, median [IQR] age 61 [55-69] years). 91% received invasive mechanical ventilation, and mean ± SD severity-of-disease APACHE-II-score at admission was 15.8 ± 4.1. After 6.5 ± 1.3 months, 27% of patients scored cognitively impaired. Patients that did or did not develop cognitive impairments were similar in ICU-admission parameters, clinical course and delirium incidence. Patients with subjective cognitive complaints (20%) were more likely women (61% vs 26%), and had a shorter ICU stay (median [IQR] 8 [5-15] vs 18 [9-31], p = 0.002). Objective cognitive dysfunction did not correlate with subjective cognitive dysfunction. 27% of the participants received dexamethasone during intensive care admission, 44% received additional tocilizumab and 29% received neither. Overall occurrence and severity of cognitive dysfunction were not affected by anti-inflammatory therapy, although patients treated with both dexamethasone and tocilizumab had worse executive functioning scores (Trail Making Test interference) than patients without anti-inflammatory treatment (T-score 40.3 ± 13.5 vs 49.1 ± 9.3, p = 0.007). Discussion: A relevant proportion of critically ill COVID-19 patients shows deficits in long-term cognitive functioning. Apart from more pronounced executive dysfunction, overall, anti-inflammatory therapy appeared not to affect long-term cognitive performance. Our findings provide insight in long-term cognitive outcomes in patients who survived COVID-19, that may facilitate health-care providers counseling patients and their caregivers.

The Silent Toll of Second COVID-19 Wave: A Dass-21 Questionnaire Survey among Health-Care Workers at a Tertiary-Care Public Hospital, Mumbai

Background: The uncontrolled spread of the COVID-19 disease in India's second wave post-February 2021, put to task the public health system across the nation. This, in turn, exhausted our health-care workforce both physically and mentally. To establish the prevalence of psychological symptoms and guide the action plan in place, the present study was undertaken among COVID-19 health-care workers (HCWs) at tertiary-care public hospital, Mumbai. Materials and Methods: The present cross-sectional study was conducted after due institutional ethical clearance among 212 HCWs engaged in the management of COVID-19 patients during the second wave. A Google Form (R) was created in English, Hindi, and Marathi languages for self-administration. Data were collected under three domains;informed consent, sociodemographic and workplace-related details, and DASS-21 Questionnaire scores. This was further subjected to statistical analysis using SPSS (R) software. Results: This study included 90 (42.5%) doctors, 91 (42.9%) nurses, and 31 (14.6%) other categories of HCWs. Depression was prevalent in 44.3% HCWs, while 43.9% and 36.3% of the HCWs were affected by anxiety and stress, respectively. Younger population, female gender, and doctors were associated (P < 0.05) with an increased likelihood of either of the prevalent psychological symptoms. Other significantly associated (P < 0.05) factors included COVID-19 vaccination status of the HCW, history of COVID-19 infection, infected colleague at workplace, workplace housing facilities and commute, number of dependents on the HCW and hospitalized family member or close friend. Conclusion: The COVID-19 HCWs were found to be under considerable psychological strain. In essence, screening, identifying, and effectively targeting HCWs for psychological interventions is needed to protect and strengthen the health-care system.

Fabrication of Low Cost Food Delivery Robot

In today's world, technology has drastically increased in all sectors of industries and businesses. Automated machines demands have increased rapidly. Most small and medium scale businesses are trying to use this technology to increase the speed and reliability. One of the booming technologies is robotics, which helps businesses in so many ways. This paper discusses the approach and experience in the design, simulation, modelling, testing, and deployment of a Low-Cost food delivery robot in hotels, restaurants etc. These food delivery robots give an enhanced experience for the customers and benefits the restaurant business financially by bringing attention to visitors and act as a publicity. The restaurant industry is also experiencing a downturn because of the COVID-19 breakout. With this method, food can be delivered directly from the kitchen to the customer's table while maintaining all norms and sanitary guidelines. The robot uses microcontroller mounted with DC motors. Ultrasonic sensors and IR sensors are used for mapping and localization of destination tables, motor drivers, obstacle detection, collision avoidance, path detection. The robot performed as per the test and achieved the desired result. © 2022 IEEE.

IMPACT OF COVID-19-RELATED REGULATORY CHANGES ON NATIONWIDE ACCESS TO BUPRENORPHINE AND TREATMENT DISRUPTIONS

BACKGROUND: The intersection of the opioid overdose epidemic and COVID-19 pandemic has prompted major regulatory changes to ease access to medications for opioid use disorder via telemedicine. We examined the impact of COVID-19-related health care changes on access to buprenorphine (BUP) by age, gender, insurance category, and prescriber specialty using a nationwide longitudinal prescription database. METHODS: We used an interrupted time series design with IQVIA LRx, a longitudinal database with >90% of all prescriptions dispensed in the US. The study timeline included BUP prescriptions from 52 weeks before (2/23/19- 2/21/20) to 52 weeks after (3/28/20-4/2/21) the initial pandemic period (2/22/ 20-3/27/20). The outcome of interest was total milligrams (MG) of BUP available per week nationwide. We used the CMS NPI database to assign prescriber specialty. Segmented regression was used to estimate relative changes in BUP prescribing at 1, 26, and 52 weeks post- initial-pandemic period compared to the expected baseline trend. We also evaluated treatment disruptions (a gap of 28 days) in previously stable patients, defined as ≥6 months of BUP prescriptions without a treatment disruption. RESULTS: A total of 31,801,061 prescriptions were included. The number of patients with an active BUP prescription was increasing in the 52 weeks prepandemic (trend: 1252 pat./wk.) and increased significantly in the 1st week post- initial-pandemic period (level change: 25786, p<0.001). The total MG BUP dispensed increased at 1, 26, and 52 weeks compared to the expected baseline trend (5.3% [4.9, 5.7], 3.3% [2.8, 3.8], 1.2% [0.48, 1.9]), as did the mean days supplied (9.3% [8.7, 9.9], 4.9% [4.3, 5.5], 6.3% [5.4, 7.3]). Stablytreated patients saw a significant decrease in treatment disruptions at 52 weeks post-initial-pandemic period (-28.4% [-33.7, -23.0]) compared to the expected baseline trend. Older age groups (40+) experienced an increase inMG BUP at 52 weeks (40-49: 4.9 [3.9, 5.9];50-64: 3.0 [0.75, 5.2];65+: 4.5 [3.4, 5.6]), while people aged 18-29 saw a significant decrease in MG BUP (-16.5 [-24.1, -8.8]). Men retained a significant increase in MG BUP compared to women at 52 weeks (1.7% [1.0, 2.4] v 0.5% [-0.34, 1.3]). People with Medicaid had a significant increase in MG BUP at 52 weeks (9.6% [7.7, 11.6]) while people paying with cash (-10.1 [-12.3, -7.9]) and commercial insurance (-4.6 [-5.7, -3.4]) saw significant decreases compared to the expected baseline trend. APPs, compared to physician specialties, had a notable increase in MG BUP dispensed at 1, 26, and 52 weeks (10.0 [8.8, 11.2], 7.1 [5.9, 8.4], 2.8 [0.13, 5.4]). CONCLUSIONS: In the year after the initial COVID-19 pandemic period, patients received longer prescriptions of BUP and overall increased total MG BUP. Stably-treated patients experienced fewer treatment disruptions. Regulatory changes around BUP prescribing may have helped patients maintain access to MOUD during the pandemic.

Solution of Solar Integrated Economic Load Dispatch Problems using COVID-19 Algorithm

Electrical power dispatch at a minimum cost of operation has been a challenging issue for thermal power stations and has research work has been carried out for decades. It has been observed that day by day resources of conventional energy are depleting so, the world is shifting towards renewable energy sources. This paper presents a novel technique COVID-19 Optimizer Algorithm (CVA) for solving the economic load dispatch problem of solar generation systems and thermal generating plants of a power system. The proposed method can be considered for solving the various types of economic load dispatch (ELD) problem considering numerous constraints viz. ramp rate limit & prohibited operating zones. Simulation results proved that the technique proposed performs way better than other modern optimization algorithms both in terms of quality of result obtained as well as computational efficiency. The robust nature of the CVA technique in solving solar integrated ELD problems can be inferred from the results. © 2022 IEEE.

An Online Mobility Management System to Automatically Avoid Road Blockage and COVID-19 Hotspots

The unprecedented road blockage mostly generates unnecessary hindrance, delay, and interruption during travel. Moreover, the emergence of the sudden third wave through the rapid spread of the omicron CoV-2 variant along with its new combinations with delta SARS-CoV-2 is leading to newer travel restrictions through various hotspots and containment zones contributing to enhanced travel interruption. While traveling, passengers in vehicles are unaware of road conditions during transit time due to blockage in a route. This causes a huge confusion and decision crisis at the edge of such blockage to find the most suitable alternative route. We have developed a software-based system including a mobile application that is capable of handling real-time constraints, transit service, and actual road conditions of a route. This system can be used to find and display alternative routes or maps without any confusion in case of sudden route blockage caused by mass gathering, accidents, or road construction in transit time. During this pandemic COVID-19, this system can also be used to avoid the localized hotspot for a safe and convenient journey. As this system is developed at the time of the pandemic, it is called an Automatic COVID-19 hotspot avoidance navigation system (ACHANS) that generates a unique optimal travel path while traveling to avoid road blockage/COVID-19 hotspot areas. The system works from the user perspective with coordination among the ACHANS Database, map routing server, ACHANS web applications, and ACHANS mobile applications. The process works by creating a buffer-centric radius generation, considering open and closed hotspot regions, and controlling clusters of hotspots. ACHANS database cum alternative roadways bypass system will be independently executed to avoid the localized hotspot for a safe and convenient journey. The proposed system is theoretically, experimentally, and statistically evaluated and verified for various traffic conditions where the performance dictates the efficacy of the scheme and can thereby establish travel and trade during the pandemic. © 2022, Ohmsha, Ltd. and Springer Japan KK, part of Springer Nature.

Bidirectional Encoder Representations from Transformers for the COVID-19 vaccine stance classification

Vaccine-related information is awash on social media platforms like Twitter and Facebook. One party supports vaccination, while the other opposes vaccination and promotes misconceptions and misleading information about the risks of vaccination. The analysis of social media posts can give significant information into public opinion on vaccines, which can help government authorities in decision-making. In this work, an ensemble-based BERT model has been proposed for the classification of COVID-19 vaccine-related tweets into AntiVax, ProVax, and neural sentiment classes. The proposed model performed significantly well with a micro F1-score of 0.532 and an accuracy of 0.532 and achieved the second rank in the shared competition. © 2021 Copyright for this paper by its authors.

Healthcare Data Security Using Lightweight Protocol for Cyber Physical System

Healthcare systems are equipped with the latest technological advancement and remotely diagnose the patients. In critical conditions, the patients need continuous monitoring by health experts, which is almost impossible in many cases—for example- in the recent COVID-19 crisis when the hospitals are full of infected people. The advanced cyber-physical system (CPS) based medical devices supplement this monitoring system. Health specialists can connect with patients remotely and receive updated health reports simultaneously using Internet-enabled CPS devices. Due to the openness of security protocols, transferring information in the CPS module is a challenging task. Securing health data, on the other hand, is critical. Existing data security techniques, such as RSA and DSA, have drawbacks;one of the most prominent drawbacks of all existing data security strategies is a lack of resources. This study proposed a lightweight data security technique for sharing information in real-time to address this problem. The proposed approach is generalized, as it will work with all categories of data and provide security to the critical information of healthcare data. Additionally, the model is tested with the cross-platform dataset of different categories like.txt, .pdf, .doc, .png, etc., and found promising outcomes. IEEE

Deep diffusion-based forecasting of COVID-19 by incorporating network-level mobility information

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

Automated People Monitoring System Using OpenCV and Raspberry Pi

Over the last decade, there has been a quantum leap in terms of the evolution of new methodologies to better our quest to understand artificial intelligence and machine learning. One such field, where there has been an unparalleled advancement, is computer vision. The paper aims to design and structure an automated monitoring system that automates the monitoring of the number of people in this COVID-19 scenario in a designated enclosure. We have deployed the system on Raspberry Pi module and integrated a HOG detector which transcends ordinary Haar cascades in terms of performance. This model can then subsequently be connected and integrated with other modules to further enhance its applicability and spectrum of usage. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.