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The Blood Bank mobile application is an effort of easing the process of receiving and donating blood. This application helps the users to seamlessly donate and receive the required blood and also gives the availability of oxygen and ambulance in nearby hospitals. It gives the user information related to the availability of blood types in different hospitals and blood banks. Taking in mind the COVID-19 pandemic situation, in which the requirement for blood and oxygens were reached an unmanageable level. Blood and Oxygen is an essential part of the healthcare system. Day by day, the requirement for blood and oxygen is increasing, but still, there is unavailability and shortage. This project aims to give people a single platform to resolve these issues. © 2023 IEEE.
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BACKGROUND: There may be differential impact of the COVID-19 pandemic on mental health and burnout rates of healthcare professionals (HCPs) performing different roles. AIMS: To examine mental health and burnout rates, and possible drivers for any disparities between professional roles. METHODS: In this cohort study, online surveys were distributed to HCPs in July-September 2020 (baseline) and re-sent 4 months later (follow-up; December 2020) assessing for probable major depressive disorder (MDD), generalized anxiety disorder (GAD), insomnia, mental well-being and burnout (emotional exhaustion and depersonalization). Separate logistic regression models (at both phases) compared the risk of outcomes between roles: healthcare assistants (HCAs), nurses and midwives (nurses), allied health professionals (AHPs) and doctors (reference group). Separate linear regression models were also developed relating the change in scores to professional role. RESULTS: At baseline (n = 1537), nurses had a 1.9-fold and 2.5-fold increased risk of MDD and insomnia, respectively. AHPs had a 1.7-fold and 1.4-fold increased risk of MDD and emotional exhaustion, respectively. At follow-up (n = 736), the disproportionate risk between doctors and others worsened: nurses and HCAs were at 3.7-fold and 3.6-fold increased risk of insomnia, respectively. Nurses also had a significantly increased risk of MDD, GAD, poor mental well-being and burnout. Nurses also had significantly worsened anxiety, mental well-being and burnout scores over time, relative to doctors. CONCLUSIONS: Nurses and AHPs had excess risk of adverse mental health and burnout during the pandemic, and this difference worsened over time (in nurses especially). Our findings support adoption of targeted strategies accounting for different HCP roles.
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Since the COVID-19 pandemic, the world began a frantic search for possible prophylactic options. We conducted a study to assess the role of hydroxychloroquine for COVID-19 prophylaxis in health-care workers. The study was a prospective cohort with four arms (high, medium, low dose, and control) of HCQ prophylaxis. Participants were grouped as per their opting for any one arm on a voluntary basis as per institute policy. The outcomes studied were COVID-19 positivity by RT-PCR and its severity assessed by WHO COVID-19 severity scale. Total 486 participants were enrolled, of which 29 (6%) opted for low dose, 2 (<1%) medium dose, and none for high dose HCQ while 455 (93.6%) were in the control arm. Of the 164 participants who underwent RT-PCR, 96 (58.2%) tested positive. Out of these 96 positive cases, 79 [82.3%] were ambulatory and were managed conservatively at home. Only 17.7% participants, all from the control group, required hospitalization with the mild-moderate disease. None of the participants had severe disease, COVID-related complications, ICU stay, or death. The difference in the outcome was statistically insignificant (p value >0.05). This single-centre study demonstrated that HCQ prophylaxis in healthcare workers does not cause a significant reduction in COVID-19 as well as mitigating its severity in those infected. At present, most of the trials have not shown any benefit. Though COVID-19 vaccines have reduced the need for prophylaxis, the search for a safe and reasonable chemoprophylaxis should continue until a large population of individuals gets vaccinated, especially in underdeveloped countries.
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Heart rate (HR) is a crucial physiological indicator of human health and can be used to detect cardiovascular disorders. The traditional HR estimation methods, such as electrocardiograms (ECG) and photoplethysmographs, require skin contact. Due to the increased risk of viral in- fection from skin contact, these approaches are avoided in the ongoing COVID-19 pandemic. Alternatively, one can use the non-contact HR estimation technique, remote photo- plethysmography (rPPG), wherein HR is estimated from the facial videos of a person. Unfortunately, the existing rPPG methods perform poorly in the presence of facial deformations. Recently, there has been a proliferation of deep learning networks for rPPG. However, these networks require large-scale labelled data for better generalization. To alleviate these shortcomings, we propose a method ALPINE, that is, A noveL rPPG technique for Improving the remote heart rate estimatioN using contrastive lEarning. ALPINE utilizes the contrastive learning framework during training to address the issue of limited labelled data and introduces diversity in the data samples for better network generalization. Additionally, we introduce a novel hybrid loss comprising contrastive loss, signal-to-noise ratio (SNR) loss and data fidelity loss. Our novel contrastive loss maximizes the similarity between the rPPG information from different facial regions, thereby minimizing the effect of local noise. The SNR loss improves the quality of temporal signals, and the data fidelity loss ensures that the correct rPPG signal is extracted. Our extensive experiments on publicly available datasets demonstrate that the proposed method, ALPINE outperforms the previous well-known rPPG methods. © 2023 IEEE.
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Background. There is a continued need for therapeutics for the treatment of COVID-19, including intramuscular (IM) agents, which will enable broader use across a variety of healthcare delivery settings. Methods. COMET-PEAK (NCT04779879) is a 3-part study evaluating the safety, tolerability, pharmacokinetics (Part A), and viral pharmacodynamics (PD) of sotrovimab as treatment in adults >= 18 years with early mild/moderate COVID-19. In Parts B and C, the safety, tolerability and viral PD of sotrovimab administered as a 500 mg intravenous (IV) infusion or as a 500 mg or 250 mg IM injection, respectively, was evaluated. The primary objective for Parts B and C was to compare the virologic response of sotrovimab IM to IV, with an endpoint of mean area under the curve (AUC) of SARS-CoV-2 viral load as measured by qRT-PCR from Day 1 to Day 8 (AUCD1-8) in nasopharyngeal swabs and predefined 90% confidence interval (CI) limits of 0.5-2.0 indicating equivalence. Results. A total of 167 and 157 participants were enrolled in Part B and C, respectively, from February-July 2021. The median age of participants was 47 and 42 years in Part B and C, respectively, and ~50% had >= 1 risk factor for progression to severe disease. The viral load at baseline and through Day 29 of follow-up for each arm is shown in Table 1 and Figure 1. The primary objective was met for both study parts: the ratio of the least square geometric mean viral load AUC(D1-8) of sotrovimab IM vs IV was 1.04 (90% CI, 0.98, 1.09) and 1.02 (90% CI, 0.94, 1.11), for Part B and C, respectively. Through Day 29 of follow-up, the most common adverse event was injection site reactions (ISRs) in the IM arms. A total of 10 (12%) participants in the 500 mg IM group and 4 (5%) participants in the 250 mg IM group experienced an ISR, all Grade 1. Serious adverse events were uncommon, and related to COVID-19 progression, including one death in the 250 mg IM arm (Table 2). ISRs aside, there were few treatment-related AEs (2/84 IV, 1/82 IM) in Part B, none serious. Conclusion. IM administration of sotrovimab 500 mg and 250 mg each demonstrated equivalence to 500 mg sotrovimab IV in viral load assessments. Overall, there were no treatment-related serious AEs and sotrovimab was well tolerated. An 500 mg IM formulation will allow for expanded treatment potential with sotrovimab.
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Background: The COVID-19 pandemic severely impacted patients with acute lymphoblastic leukemia(ALL) in maintenance phase of chemotherapy. Teleconsultation was introduced to ensure continuity of care for these patients during the lock-down phase of the pandemic and was continued well after its end. Hence, we decided to analyze the impact of teleconsultation in a cohort of ALL patients. Method(s): Our study was a single-centre retrospective analysis of patients with ALL on maintenance chemotherapy. Thirty-five patients records were analyzed, comparison was made between absolute neutrophil counts (ANC) and frequency of consultations before and after the start of teleconsultation, which included 2-weekly phone calls, necessitating visit only once in 3 months as opposed to a monthly visit as required before. Hemograms were done twice a month and sent on WhatsApp. Consultations were done via phone calls and prescriptions sent via WhatsApp. Result(s): The median [IQR] age of our cohort was 7.5 [4.2;9.3] years and age at diagnosis was 5.4 [2.3;7.5] years;23/35 (66%) were male and 30/35 (88%) were phenotypically B-ALL;rest T-ALL/Lymphoma. All patients received chemotherapy as per the ICiCLE (Indian Collaborative Childhood Leukaemia group) protocol. A total of 437 teleconsultations were done (73/month). Before teleconsultation, the mean (SD) ANC was 2272 (644)//microL, and after teleconsultation it was 1754 (461)/microL (p value=0.0001). Teleconsultation improved target ANC (<2000/microL) attainment in our cohort of patients (31% vs 80%, p value=0.0002). Prior to teleconsultation, majority (27/35, 77%) visited the hospital once a month which reduced to once in 3 months, after teleconsultation. Conclusion(s): Teleconsultation is time saving, economical and reduces the gap in schooling in a child with ALL. It also helps optimize compliance during this maintenance phase of chemotherapy, a key in management of leukemia patients, contributing to the continuum of care and improvement in overall survival of these patients. Copyright © 2022
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Aim: Prevalence of low back pain and osteoporosis in health care workers after the COVID 19 pandemic. Material(s) and Method(s): The present prospective study was conducted among 300 apparently healthy adults who are working as a health care individual in the institute. A questionnaire addressing known risk factors for osteoporosis was made. The severity of the LBP was graded using a visual analogue scale for pain (VAS). The VAS is a reliable scale used to register the intensity of chronic pain where 0 signifies no pain and 10 signifies the worst pain imaginable. Those who had chronic LBP were also questioned on whether the onset of LBP preceded the Covid-19 pandemic, and whether the severity of the LBP had increased during the pandemic. Result(s): Light, moderate, sedentary and vigorous physical activity was revealed in 50.1%, 33.6%, 11.1% and 5.2% of the subjects respectively. >1 hour sun exposure in a day was reported among 15.4% of the subjects.In this study, low back was found among 42.7% of the subjects. Mean BMD level was -0.49+/-2.40. Mean BMD level was lower in subjects having back pain, sedentary/vigorous physical activity and no sun exposure as compared to counterparts. Conclusion(s): The confinement decreed due to the COVID-19 pandemic led to a significant increase in LBPintensity among health care workers. Copyright © 2022 Ubiquity Press. All rights reserved.
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The consumption of antidepressants has increased on a global scale. These medications are frequently prescribed to treat mental health-related disorders and their usage is expected to rise in the future because the COVID-19 pandemic has intensified these problems significantly. These compounds have recently been detected in wastewater treatment plants and surface waters, raising concerns about their potential impacts on the envi-ronment. In this regard, the current review aims to critically evaluate the available information on the worldwide consumption of antidepressants, their occurrence, possible toxicological effects on aquatic organisms, and removal techniques. Several analytical methods for the extraction and quantification of antidepressant com-pounds have also been discussed. Additionally, risk quotients (RQs) have been estimated which indicates that sertraline posed the highest risk (RQ: 4.88) to the aquatic life followed by citalopram (RQ: 1.55) and bupropion (RQ: 1.12). It was observed that the aquatic organisms encountered behavioral, physical, cardiovascular, and reproductive changes after being exposed to antidepressant compounds. Some of these compounds have been satisfactorily removed (>85%) using a sequencing batch reactor with aerobic granulation of sludge. Physico-chemical processes such as photocatalysis, photochemical oxidation, and electrocatalysis exhibited more than 90% degradation efficiency in most cases. Moreover, integrating two or more physicochemical processes improved the treatment efficiency further. This study may help researchers to understand the threats posed by antidepressants to the environment and result in the development of innovative technologies for their removal.
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Even more than two years, Coronavirus Disease 2019 (COVID-19) has been emerging as a harshening name which influence the health indicator of human being by life threatening illness in all over world. It had been revealed first time in Wuhan, China, in December 2019. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the etiological sources of COVID-19. There is currently no precise treatment or vaccine against COVID-19. India is second highly condensed country in the world, where is the limited sources of earning, education as well as technology. Therefore, in the shortcoming of pharmaceutical preparation, the advanced implementation of precautions and hygienic measures will be essential to control and to minimize human transmission of the virus. In response to the rapidly escalating number of publications on the emerging disease, this review attempts to provide a timely and comprehensive review of recent development and present situation of India in view of COVID-19. It has been more than two years of this havoc, still it is not eradicated completely. We will cover the preliminary knowledge towards the epidemiology, etiology, virology, diagnosis, treatment, prognosis, and prevention of the disease in the world as well as in India. Meanwhile many questions will be arising day by day;we are expecting that this review helps in the understanding and eradication of the threatening disease as a future outcome. Copyright © 2022, Anka Publishers. All rights reserved.
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Mental disorders are a critical issue in modern society, yet it remains to be consistently neglected. The COVID19 pandemic has made it much more difficult to seek assistance when one needs it. People are feeling increasingly anxious and uncertain about their futures while being socially separated from their friends and relatives. As people continue to quarantine among the limitations imposed by governments, interaction between clinical therapists or social workers and those suffering from mental illness has gotten increasingly limited. Machine learning is a vital approach for allowing virtual analysis of many forms of textual, audio, and visual data for sentiment analysis and understanding the mental health of people utilizing numerous critical parameters in this situation. This chapter aims to provide a systematic review of the current literature investigating COVID-19's impact on mental well-being, as well as studies that explore machine learning and artificial intelligence techniques to detect and treat mental illnesses when traditional therapies are unavailable due to lockdown and social distancing norms imposed. The different machine learning algorithms and deep learning approaches utilized in earlier studies are thoroughly discussed in this chapter. Detailed explanation of the data sources utilized and a review of the types of features investigated in mental disorder identification are included as well. The study's major findings are thoroughly discussed. The obstacles of employing machine learning techniques in biomedical applications are explored, as well as possibilities to enhance and progress the discipline. © 2022 Elsevier Inc. All rights reserved.
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Active inspection of Omicron variant enables rapid and effective analysis of COVID-19 variant. It can diminish the encumbrance on health systems. Detection and forecast models combine many qualities for calculation. The hazards of infection have progressed. The goal is to aid health care professionals globally in case of critical Omicron long-suffering persons, specifically in the situation of inadequate well-being means. The developed deep learning research approach was trained on the dataset of 900 verified persons (among which 500 were inveterate, to partake Omicron variant). The training test-set delimited the dataset from the succeeding days (700 verified persons, of which 300 were inveterate, to partake Omicron). The proposed research prototype spotted and forecasted Omicron examined outcomes with good correctness and little error rate using only six unique features such as gender, age = 50 ages, known traces with a disease distinct, and the presence of four preliminary medical indications. The developed research prototype notices Omicron infected cases via easy qualities retrieved through posing elementary queries or data queries. The proposed research outline can be useful, amid additional attention, to arrange to examine Omicron while checking properties are insufficient. © 2022 IEEE.
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The study aims to examine the existence of a correlation between the stock prices of the energy sector, commodities prices of the energy sector, and market indices. The study uses an empirical approach to develop various VAR (Vector Autoregression) with Variance Decomposition Models for each company under the energy sector indexed in NIFTY50 by considering daily prices for 3 years. For a comparative study, the data have been divided into two parts. The first part is considered pre-COVID era, i.e., from July 1, 2018, to December 31, 2019, and the second part is considered post-COVID era, i.e., from January 1, 2020, to June 30, 2021. While observing the estimates of VAR of different companies, it can be said that crude oil is significant in most of the models during pre-COVID whereas, during post COVID, lag term of crude oil and NIFTYENGERGY are significant. On the other hand, while observing the estimates of variance decomposition in all the VAR models, the first lag term of the particular company’s share price is strongly endogenous. In comparison, the other independent variable, i.e., lag term of the price of crude oil and natural gas, values of NIFTY50 and NIFTY ENERGY are strongly exogenous to the stock prices of the energy sector. © 2022, Econjournals. All rights reserved.
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The COVID-19 pandemic has caused a global emergency, as human life is under constant threat and medical infrastructure is being pushed to the limits. Scientists, engineers, and healthcare experts collaboratively have come up with new innovative technologies to tackle the threat posed by the disease. Artificial intelligence (AI) and machine learning (ML) have played pivotal roles in several technologies being developed. In this study, we aim to review the applications of AI and ML in developing models for diagnosis and clinical outcome prediction of the COVID-19 disease by analyzing data from Electronic Health/Medical Records (EHR/EMR) of patients. In this chapter, we reviewed various ML algorithms used by researchers to extract key features from the EHR necessary to develop predictive models;we also reviewed the performance of various predictive models developed which employed ML to assist decision-making for healthcare professionals. Gaps in the current methodology of acquiring and storing clinical data in a digital format have also been discussed thoroughly. Scientists, clinicians, healthcare experts, policymakers, academics, and ML enthusiasts might find the review useful, as important studies related to the application of ML in the field of medical decision making and alike by studying EHR has been systematically analyzed and critically reviewed, the discussion on the challenges faced by the existing researchers and possible solutions have also been discussed;which should stimulate further research in this domain. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Objective: The world is facing the pandemic of COVID-19, which has led to a considerable level of stress and depression in mankind as well as in society. Statistical measurements can be made for early identification of the stress and depression level and prevention of the pre-vailing stressful conditions. Several studies have been carried out in this regard. The Machine learning model is the best way to predict the level of stress and depression in humankind by statistically analyzing the behavior of humans which helps in the early detection of stress and de-pression. This helps to prevent society from psychological pressures from any disaster like COVID-19. COVID-19 pandemic is one of the public health emergencies that are of great international concern. It imposes a great physiological burden and challenges on the population of the country facing the calamity caused by this disease. Methods: In this paper, the authors conducted a survey based on some questionnaires related to depression and stress and used the machine learning approach to predict the stress and depression level of humankind in the pandemic of COVID-19. The data sets were analyzed using the Multiple Linear Regression Model. The predicted score of stress and depression was mapped into DASS-21. The predictions have been made over different age groups, gender, and categories. The machine learning model is the best way to predict the level of stress and depression in humans by statistically analyzing their behavior which helps in the early detection of stress and depression. Results: Women, in general, were more stressed and depressed than men. Moreover, the people who are 45+ years of age were found to be more stressed and depressed, including male and fe-male students. The overall analysis showed that the people of India were stressed and depressed at “Serve” level due to COVID-19. It may be because students were more depressed about their study and career, women were stressed about their business as well as their salary and aged people were depressed due to their health concerns in COVID-19 disaster. Conclusion: The researchers conducted an analysis of data based on DASS-21 parameters defined for anxiety, depression, and stress at the global level. By the analysis defined in section 5, researchers concluded that the people of India are more stressed and depressed at "Serve" level due to COVID-19. © 2022 Bentham Science Publishers.
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Background COVID pandemic has added to the stress during medical residency Stress during residency has been found associated with burnout Personality is associated to coping with stress and resulting outcome Personality factors are likely to modify the effect of stress on Burnout & Professional FulfilmentTable 1 Personality profile of Resident DoctorsExtraversion6.22.3Agreeableness 7.71.7Conscientiousness 6.51.5Neuroticism 5.72.2Openness 6.61.8Table 2 Personality Correlation with StressPersonality factorStressPearson CorrelationpExtraversion-0.140.08Agreeableness -0.330.00Conscientiousness -0.130.11Neuroticism 0.470.00Openness 0.120.13Table 3 Conditional effect of Stress at Conscientiousness levelsCEffect of Stress on Burnouttp5.000.068.880.006.000.059.600.008.000.045.370.00CEffect of Stress on PFtp5.00-0.02-2.170.036.00-0.03-3.600.008.00-0.04-4.090.00 Aim To study the role of personality on the association of stress with burnout and professional satisfaction among resident doctors during COVID second wave in India Methods A cross-sectional observational study among resident doctors in India using Google forms, approved by Institutional Ethics Committee Stanford Professional Fulfilment Index for Professional Fulfilment and Burnout 10 item Big five inventory (BFI) assed personality and Depression, Anxiety & Stress Scale - 21 to sassed Stress Pearson correlation to assess correlation and Hayes Process for moderation using SPSS V24 © IBM Results 152 participants 49.3% Males and 50.7% Females Mean age - 29.6 years (SD 4.5) 35.5% reported significant stress Average Stress score on DASS - 12.9 (SD 11.1) Mean Professional fulfilment (PF) score - 2.0 (SD 1.0) Mean Burnout score - 1.3 (SD 0.9) 20.4% residents felt professionally fulfilled 41.4% residents had burnout Stress negatively correlated with Agreeableness Stress positively correlated with Neuroticism Stress positively correlated with Burnout (ρ 0.67;p 0.00) Stress negatively correlated with Professional fulfillment (ρ -0.41;p 0.00) Only Conscientiousness moderated the effect of stress on Burnout and Professional fulfillment Conclusion COVID pandemic generated significant stress among resident doctors Stress is associated with Burnout and Professional fulfilment Higher Conscientiousness reduced burnout due to stress Residents with higher Conscientiousness would experience more negative effect of stress on their professional fulfillment