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COVID-19 has been a crucial factor in causing situations of mental stress in all communities and fraternities. The global pandemic has been stressful, particularly for international students. They have been stranded thousands of kilometers away from their families with no means of air travel and daily news of the spread of the pandemic in their home countries. With various colleges and universities closing their campus and asking them to leave their campus residences, the situation has become extremely stressful for some international students. They are finding themselves helpless, isolated, and financially burdened. The focus of this chapter is to analyze the impact of the COVID-19 pandemic on the mental health of students and recommend how instructors can help international students cope up with this stress by using innovative techniques. © Springer Nature Switzerland AG 2021. All rights reserved.
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Viral diseases are common and natural in human it spreads from animals and other humans. It seeks to identify the proper, reliable, and effective disease detection as quickly as possible so that patients can receive the right care. It becomes vital for medical field searches to have assistance from other disciplines like statistics and computer science because this detection is frequently a challenging process. These fields must overcome the difficulty of learning novel, non-traditional methodologies. Because so many new techniques are being developed, a thorough overview must be given while avoiding some specifics. In order to do this, we suggest a thorough analysis of machine learning which is used for the diagnosis of viral diseases caused in humans as well as plans. Predictions are made which is not obvious at the first glance does machine learning will be more helpful in making decisions. The study focuses on the machine learning algorithms for diagnosis of viral diseases for early diagnosis and treatment of viral diseases with greater accuracy. The work helps the researchers and medical professionals for learning and to give treatment for determining the applications of different machine learning techniques run to evaluate the parameters. Through examination of various parameters new machine learning model is proposed understanding the applications of machine learning in viral disease diagnosis like imaging techniques, plant virus diagnosis and the solution for the problem, Covid 19 diagnosis. © 2023 Bharati Vidyapeeth, New Delhi.
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Human coronaviruses (HCoVs), including severe acute respiratory syndrome coronavirus (SARS-CoV) and 2019 novel coronavirus (2019-nCoV), also known as SARS-CoV-2, have caused global epidemics with high morbidity and mortality. Active research on finding effective drugs against 2019-nCoV/SARS-CoV-2 is going on. In silico screening represents the best approach for hits identification and could shorten the time and reduce cost compared to de novo drug discovery. Recently, CoV2 mutations have been a big concern in India, particularly on non-structural proteins (NSPs) and Spike Protein (B.1.617) which are the key targets that play a pivotal role in mediating viral replication and transcription. Herein, this study analyzed the NSPs and spike's structural aspects of mutant strains of SARS-CoV-2. The three-dimensional structures of NSPs and S Spike proteins were retrieved from the protein data bank or modeled. And a dataset of an antiviral compound library containing 490,000 drug-like ligands and structurally diverse biologically active scaffolds was used for our studies. Initially, the molecular alignment was performed for library compounds with the reference drug molecule to find targets that match the field points. Antiviral compounds having a similarity score >0.6;were selected for further docking studies with wild and mutant NSPs and S Spike protein of SARS-CoV-2 variant B.1.617. The docking studies identified a potent analog MA-11, which exhibited the highest binding affinity towards wild and mutant proteins. Further, molecular dynamics simulation studies of selected compounds confirmed their perfect fitting into NSP12 and spike active sites and offer direction for further lead optimization and rational drug design.
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We are proposing an IoT-based social distancing device as a preventive measure to COVID-19. It uses NodeMCU in conjunction with ultrasonic sensor temperature sensor, while vibrator buzzer is used for an alarming mechanism. The ultrasonic sensor is used to obtain higher accuracy as it uses LOS principle to measure the distance. The alarm will be raised whenever measured distance is found to be less than six feet. Temperature sensor is used to alert the user to isolate them if their body temperature goes above 102 °F, thereby decreasing the transmission possibility of virus in case he is infected with the virus. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Background: Current research on COVID-19-related outcomes in patients with psoriasis, particularly regarding influence of treatments, are subject to lack of comparator group, selection bias, and insufficient statistical power.1 Accordingly, it remains uncertain whether immunomodulatory treatments for psoriasis enhance or decrease the risk of severe COVID-19-related outcomes, including hospitalization. Objective(s): To compare the risk of COVID-19-related hospitalization according to immunomodulator treatment type in patients with psoriasis Methods: Retrospective cohort study of the Explorys database in the United States between March 1st, 2020 and December 31st, 2020. Psoriasis diagnosis was defined by at least 2 ICD-9 or ICD-10 diagnosis codes prior to March 1st, 2020. Drug exposure was classified as biologic or traditional immunosuppressive (methotrexate, cyclosporine, apremilast) treatment based on prescription order in the 3 months preceding March 1st, 2020. Biologic treatments included TNFalpha, IL-12/IL-23, IL-17A, IL-23 and JAK inhibitors. The primary outcome was defined as hospital admission with diagnosis of COVID-19 or positive lab test occurring between admission and discharge date. Propensity score weighting was used to compare COVID-19-related hospitalization between treatment groups, adjusting for comorbidities and demographic characteristics. Result(s): A total of 51,606 psoriasis patients aged 18-88 were included. Crude cumulative incidence of COVID-19 hospitalization per 1,000 psoriasis patients was 3.4 in the biologic group (9/2,669), 9.5 in the traditional immunosuppressive group (15/1,585), and 3.9 in those receiving neither drug class (184/47,352). Incidence was 4.7 (6/1,282) and 14 (13/898) per 1,000 patients among those receiving TNF-alpha inhibitors and methotrexate, respectively. After propensity-score weighting, risk of COVID-19-related hospitalization for patients receiving any biologic was lower than that of patients receiving traditional immunosuppressives (RR 0.39, 95% CI 0.16, 0.92), and those receiving neither drug class (RR 0.66, 95% CI 0.32, 1.34). TNF-alpha inhibitor use was associated with lower risk of hospitalization relative to methotrexate use (adjusted RR 0.39, 95% CI 0.14, 1.06). Adjusted relative risk of hospitalization for methotrexate users relative to those receiving neither drug class was 2.78 (95% CI 1.47, 5.26). Conclusion(s): During the first wave of the pandemic in 2020, psoriasis patients using biologics were at lower risk of COVID-19-related hospitalization compared to those using traditional immunosuppressives, particularly methotrexate. Methotrexate use was associated with a substantial increase in risk of hospitalization relative to those who did not receive systemic treatments.
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Newly emerged COVID-19 performs its activity through spike protein receptor binding domain (RBD). A strong competitive binding on this site can inhibit the COVID-19 (SARS-CoV-2) activity against host cells. A significant plant bioactive molecule, Baicalein (5,6,7-Trihydroxyflavone), has noteworthy effects on viral S protein. The biomolecule was isolated from an endangered medicinal tree Oroxylum indicum L. Vent. Therapeutic use various parts of Oroxylum have been mentioned in ancient literature, Ayurveda and is also being used a folklore medicine in many tribal areas of India. Molecular docking has been applied to screen the binding pattern and bond strength of biomolecule with ten amino acids. The binding site was defined with site findder algorithm. The residues were found Arg403, Glu406, Lys417, Tyr453, Ser494, Tyr495, Gly496, Phe497, Asn501, Tyr505. The biomolecule Baicalein showed effective binding capacity towards active site residues of SARS-CoV-2 spike receptor-binding domain. It was found to have a strong binding affinity with RBD of S-protein of viral residues with high negative binding free energy (-12.5545 kcal/mol). Such competitive interruption of hydrogen bond formation between the viral S-protein and biomolecules' active sites would inhibit the potency of COVID-19 infectivity. Copyright © RJPT All right reserved.
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Cardiovascular inclusion has been accounted for in patients with serious intense respiratory condition Covid 2 contamination, which might be reflected by electrocardiographic changes. Cardiovascular injury is additionally connected with humanity, need for intensive care, and seriousness of illness in patients due to Coronavirus. Some case features cardiovascular contribution as an intricacy related with Coronavirus, even without indications and indications of interstitial pneumonia. Two Coronavirus incidents in our report displayed diverse ECG indications by means of the sickness caused decay. The main case introduced brief SI QIII TIII sound structure followed by changeable almost whole atrioventricular square, and the second exhibited ST-section height joined by choroidal ventricular tachycardiac. The hidden systems of these ECGs irregularities in the serious phase of Coronavirus might be ascribed to hypoxia and incendiary harm brought about by the infection. Since the scourge of Coronavirus pulled in the consideration, hearsays were encompassing ECG variations in the contaminated people. We pointed toward indicative dissimilar noticed ECG discoveries and talking about their experimental importance. This deliberate audit recommends that recognizing ECG designs that may be connected with Coronavirus is fundamental. Given that doctors don't perceive these examples, they may mistakenly hazard the existences of their patients. Moreover, significant medication instigated ECG changes give attention to the medical care laborers on the dangers of potential treatments. Copyright © 2022 Wolters Kluwer Medknow Publications. All rights reserved.
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Several drugs have sparked interest as potential COVID-19 treatment options. Doxycycline (DOX) has been widely used with other potential agents to reduce COVID-19-induced inflammation. DOX and OFLX, both well-known antimicrobial and anti-inflammatory drugs, were chosen as model pollutants. Fe, Cu-codoped TiO2-SiO2 was synthesised as a novel photocatalyst active under sunlight irradiation to treat model pollutants. The synthesised catalyst samples were meticulously characterised using various techniques to evaluate their morphological, optical, and structural properties. The results of BET analysis showed that the TSFC1 sample has a large specific surface area of 288 m(2)g(-1). Maximum degradation of DOX and OFLX (about 98%) was achieved with the TSFC1 catalyst. The photocatalytic reusability was investigated for up to seven successive cycles, and the composite particles maintained their high photodegradation activity for DOX and OFLX. TFSC1 composite, in particular, demonstrated high catalytic activity as well as excellent recovery potential, and its combination with solar light, silica, and dopants can be introduced as a promising strategy for efficiently destroying both DOX and OFLX antibiotics. This study highlights the feasibility of hybridising doped dual semiconductor nanostructures in implementing solar light-powered pharmaceutical wastewater degradation.
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Introduction: The emergence of newer mutated variants of COVID-19 virus has posed a significant challenge. The present study is aimed at investigating the clinical characteristics of COVID-19 and the parameters that may serve as predictors of severity and mortality related to COVID-19 in an Indian setting. Method(s): The observation study was carried-out by using the data of COVID-19 patients admitted between July 2020 to June 2021 at JLN Medical College, Ajmer, Rajasthan, India. The demographic and clinical data of clinically significant parameters were collected. The statistical difference between recovery and death and between patients who required long-term oxygen and those who did not was evaluated for various demographic and clinical variables. Chi-square and Fisher exact test were performed for categorical variables and t-test for continuous variables. Regression analyses were also carried-out for different variables with respect to survival and death, and for oxygen dependency. Result(s): Variables namely age, duration of hospital stay, overweight, breathlessness, O2 mask therapy, BiPAP support, and ventilator usage were found to be significantly different between recovered and expired subjects (P 0.00). The study has noted hypertension (25.06%) and diabetes (23.73%) as the common comorbidities noted in COVID patients, followed by coronary artery disease (2.98%) and asthma. The study has validated the role of oxygen saturation and requirement of oxygen in predicting mortality among COVID-19 patients. The study identified age as a significant predictor of mortality, obesity as a risk factor in COVID-19 patients, gender as a factor influencing the requirement of oxygen, and fever as an independent factor related to oxygen therapy. Bilevel positive airway pressure was given to majority of expired patients (83%) compared to 10% in recovered patients. Conclusion(s): Variables namely age, BMI, duration of hospital stay, breathlessness, O2 mask therapy, BiPAP support, and ventilator usage could be predictive in COVID-19 severity and mortality. The variables to be considered for predicting oxygen dependency are age, urban/rural, gender, duration of hospital stay, weight, height, BMI, fever, cough, breathlessness, diabetes, hypertension, and CAD. Copyright © 2022, Indian Academy of Clinical Medicine. All rights reserved.
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Infection and spread of viral diseases is undesirably not easy to regulate. The speed and scope of virus continues to grow due to multiple factors, be it social or environmental leading to sometimes the endemic or pandemic as is the case of SARS-CoV-2, which has appeared as a grave pandemic with a high mortality rate and post-recovery complications. One of the major complications of SARS-CoV-2 is pulmonary deterioration followed by pneumonia and even death. The noncritical patients also developed the potential risk of reducing respiratory strength (RS) even after successful recovery from this virus attack. It has become essential to evolve new and safe techniques to monitor the RS of patients rapidly so as to detect any potential complication and report to the healthcare providers for timely management. This pandemic COVID-19 caused by SARS-CoV-2 has sited new hassles on the health systems worldwide. Despite the terrific efforts of the governments and the healthcare providers across the world to combat this disease and its spread, develop the vaccine using technology, it is also vital to detect, track, and monitor the patients for their RS using internet of things (IoT) sensors. The chapter walks around the probability of monitoring the RS of the patients (also non-patients) from inside as well 110as outside the homes to report to the healthcare providers for quick action. A framework, CoReS is proposed as a life-saving technology to monitor and manage the RS of patients to detect any sign of potential complication. This technology will support patient satisfaction and decrease the mortality rate in health disaster. © 2023 by Apple Academic Press, Inc.
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The recent COVID-19 global pandemic exemplifies the need for efficient, reliable, and real-time tools and technology for forecasting and predicting healthcare disasters as well as for helping to restrict the subsequent spread and fatality of deadly diseases. This new book discusses many of the innovative and state-of-the-art tools and technology that can help meet the challenges of predicting such disasters. The chapters offer a plethora of useful information for designing healthcare disaster management systems that can be dynamically configurable with implementation of today’s modern technology, such as cloud computing, artificial intelligence, IoT, data analytics, and machine learning. These can increase effectiveness in remote sensing technologies, data analytics, data storage, communication networks, geographic information system (GIS), and global positioning System (GPS), to name a few. This book discusses mathematical models using graph-based approaches for analyzing dynamic, heterogeneous, and unstructured data for applications in epidemiology. The authors also address the use of mobile applications for communication efforts and remote monitoring for gauging health and the effectiveness of preventive healthcare measures. The chapters discuss influencing factors that directly or indirectly target public health infrastructure that can lead to or exacerbate global health crises, such as extreme climate changes, refugee health crises, terrorism and cyberterrorism, and technology-related incidents. The book further looks at efficient methods to analyze disasters and how to deliver healthcare in areas of conflict and crisis. This important volume, Global Healthcare Disasters: Predicting the Unpredictable with Emerging Technologies, provides a bounty of useful information for health professionals, academicians, researchers, governmental agencies, and policymakers across the world to predict, mitigate, and manage global health disaster with emerging technologies. © 2023 by Apple Academic Press, Inc.
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Introduction: The clinical diagnosis of COVID-19 is supplemented by clinical severity indices. These indices are the National Early Warning Score (NEWS, which aids in risk stratification), CT severity score (radiological severity score), and Reverse Transcription-Polymerase Chain Reaction (RT-PCR) cycle threshold (Ct value, which provides a semi-quantitative measure of viral load). Aim(s): To assess the correlation between NEWS at admission, RT-PCR Ct value and CT severity score in mild and moderate COVID-19 patients. Methods and Materials: This prospective cohort study was conducted in Maulana Azad Medical College and Lok Nayak hospital, New Delhi, from January to June 2021. The study included 50 subjects (25 with mild COVID-19 and 25 with moderate COVID-19). NEWS was calculated at admission and Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Ct value was estimated using real-time RT-PCR. CT severity score was calculated based on High Resolution Computed Tomography (HRCT) chest findings. The correlation among the parameters was determined using Pearson correlation formula. Result(s): The mean age of subjects in the mild and moderate COVID-19 groups were 49.52 years and 51.84 years, respectively. The mean RT-PCR Ct value of E gene was 24.48 and Rdrp gene was 24.56 in the mild COVID-19 group;while in the moderate group it was 23.72 for both E gene and Rdrp genes. The correlation between NEWS and Ct value of E gene (r-value=-0.06, p-value=0.68), Ct value of Rdrp gene (r-value=-0.03, p-value=0.79) and the correlation between CT severity score and Ct value of E gene (r-value=-0.05, p-value=0.73), Ct value of Rdrp gene (r-value=-0.06, p-value=0.68) was negative and insignificant. The mean CT severity score in mild COVID-19 group was 3.92, and in moderate COVID-19 group was 9.88. A significant positive correlation was found between the CT severity score and NEWS at admission. Conclusion(s): The clinical severity of COVID-19 as estimated by NEWS corroborates with CT severity score while the relationship between RT-PCR Ct value and clinicoradiological severity needs to be ascertained by further research. Copyright © 2022 Journal of Clinical and Diagnostic Research. All rights reserved.
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The coronavirus pandemic (COVID-19) is probably the most disruptive global health disaster in recent history. It negatively impacted the whole world and virtually brought the global economy to a standstill. However, as the virus was spreading, infecting people and claiming thousands of lives so was the spread and propagation of fake news, misinformation and disinformation about the event. These included the spread of unconfirmed health advice and remedies on social media. In this paper, false information about the pandemic is identified using a content-based approach and metadata curated from messages posted to online social networks. A content-based approach combined with metadata as well as an initial feature analysis is used and then several supervised learning models are tested for identifying and predicting misleading posts. Our approach shows up to 93 % accuracy in the detection of fake news related posts about the COVID-19 pandemic. © 2022 IEEE.
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With ever-evolving social, economic, and technological factors, there has been an increased demand for progressive healthcare systems. Recent years have seen extensive adoption and integration of machine learning (ML) and deep learning (DL) paradigms with edge computing, further evolving into an Internet of Things framework based on a distributed computing setup, especially in the time of the COVID-19 pandemic. Although constant research to fight the coronavirus is ongoing, many ML/DL techniques can facilitate smart health applications such as prediction of cardiac arrest, cataract detection, bacterial sepsis diagnoses, and Alzheimer’s disease. To accommodate the exponentially increasing healthcare data, an essential assistant and a prime infrastructure provider in the information and communication technology sector are the upcoming federated learning and decentralized computing. These can help solve several challenges in various types of learning that occur in wireless communication systems, such as security and privacy. Our findings discuss the present scenario of COVID-19 as a case study and review the different approaches of ML and DL in 5G and wireless communication for healthcare. It also gives an overview of the currently used privacy-preserving methods and techniques in healthcare for medical tabular data and imaging. © 2022 Elsevier Inc. All rights reserved.
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COVID-19 has been declared as a “pandemic” by the World Health Organization (WHO) and has claimed more than a million lives and over 50 million confirmed cases worldwide as of 7th November 2020. This virus can be curbed in only two ways: vaccination and other by imposing non-pharmaceutical interventions (NPIs), which are behavioral changes to a person and community. Most of the nations worldwide have imposed NPIs in the form of social distancing and lockdowns, which have been effective in reducing the pace of the virus's spread, but continued implementation has deemed social and economic losses. Hence strategic implementation of NPIs in a burst of periods should be done based on educated decisions using data about population mobility trends to find hot zones that lead to a spike in cases. These decisions will positively impact the virus's spread with lower damage to social and economic aspects. © 2022 Elsevier Inc. All rights reserved.
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Aim and background: Cytokine storm caused by the release of proinflammatory mediators, e.g., IL-6, TNF-, IL2, IL10, G-CSF, etc., is the hallmark of COVID-19 disease. This cytokine storm is characterized by immuno-thrombomodulation. C-Reactive Protein (CRP) and d-dimer are markers of proinflammatory state, which can also be used as a prognostic marker for the underlying disease processes. Objective: To determine the clinical utility of raised C-reactive protein (CRP) and d-Dimer levels as prognostic markers in patients with the diagnosis of COVID-19. Materials and methods: This retrospective observational study will be conducted at Max Super speciality Hospital I.P. Extension, Delhi after ethical committee clearance. Adult (age > 18 years) patients with confirmed diagnosis of COVID-19 admitted to COVID-ICU between 1st April 2021 till 30th June 2021 will be included and checked for CRP and d-Dimer values retrospectively. Correlation between raised CRP and d-dimer on presentation and rising trend of markers with 28-day mortality, Average length of ICU stay, need for invasive mechanical ventilation, and need for Renal Replacement Therapy will be seen. Results: Results will be shared after the completion of the study.
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The recent advances in telemedicine have offered real and practical opportunities to health-care providers in sharing expertise and resources in health care over distances. In India, telemedicine has revolutionized the health-care system by minimizing the cost, avoiding the long-distance travels and in timely providing specialist care in remote areas. The Indian Army is also reaping the benefits of telemedicine, by providing round-the-clock medical care to the troops deployed in high-altitude areas.
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Aim: To study extrapulmonary manifestations in COVID-19 patients. Material and methods: The present retrospective study was conducted among 200 COVID positive patients in the department of medicine, CSS Hospital, Subharti Medical College, Meerut. COVID-19 was diagnosed on the basis of the WHO interim guidance. Patients' diagnosis was identified along with the co-morbidity (if present). Laboratory investigations comprised of CBC and serum albumin detection. Extrapulmonary manifestations were defined as patients having predominantly neurological, gastrointestinal (GI), cardiovascular, cutaneous, and uncommon respiratory symptom such as hemoptysis either concomitant with typical respiratory symptoms or as the sole manifestation. Results: Fever was most frequent complain (n=97, 48.5%), followed by cough (n=76, 38%) and dyspnea was present in 51 subjects (25.5%). The most common respiratory symptoms was dyspnea (n=64, 32%). The most common cardiovascular symptoms was Dyspnea on exertion (n=54, 27%), followed by Palpitations (n=29, 14.5%). The most common GIT Symptoms was diarrhea (n=34, 17%), followed by Vomiting (n=13, 6.5%) and only 8 subjects (4%) reported abdominal pain. Dermatological symptoms were shown in 5 (2.5%) subjects. The most common musculoskeletal Symptoms was fatigue (n=103, 51.5%), followed by Myalgias (n=11, 5.5%) and Joint/Back Pain (n=4, 2%). Conclusion: Patients with COVID-19 require long-term follow-up even after recovery for observation and management of their post-COVID ailments. During the ongoing COVID-19pandemic, most health facilities are overloaded. Hence, arranging follow-up for patients can be a challenge. Therefore, a comprehensive rehabilitation program is essential for such patients during hospitalization and discharge.
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Persistent shortness of breath is one of the most common concerns reported by patients with post-acute sequelae of SARS-CoV-2. Here, we present a case of bilateral diaphragmatic paralysis as a cause shortness of breath that developed after SARS-CoV-2 infection. A middle-aged gentleman with history of sleep apnea and body mass index 27.9 kg/m2 presented to our post-COVID clinic with 3 months of dyspnea and orthopnea after contracting SARS-CoV-2 in November 2020. During acute infection, he was hospitalized for hypoxemia, which improved with steroids and supplemental oxygen. At 3 months, he continued to report dyspnea and orthopnea. On examination, he had tachycardia and increased respiratory rate with paradoxical respiratory abdominal movement. Chest imaging showed elevated bilateral hemidiaphragms without any parenchymal lung disease. Pulmonary function test revealed severe ventilatory defect with restrictive lung disease. He was diagnosed with bilateral diaphragmatic dysfunction which was confirmed by absence of evoked potentials in diaphragm after phrenic nerve stimulation bilaterally. He was advised to use continuous positive airway pressure machine to assist with breathing at night. At his last follow-up (1-year post-infection), he was symptomatically improving without specific interventions.