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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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Coronavirus disease 2019 is a new infectious respiratory disease as named by the World Health Organization. This virus is affecting different individuals in diverse manners. Consequently, studies are going on to identify the factors and parameters disturbing predominantly. According to various studies, the immunity of a person determines the effect of the virus on that individual's health. Thus, immunity is determined by multiple factors like climate, population, geographical location, sanitation facilities. In existing studies, the effect of various climatic factors, such as temperature, relative humidity of diverse countries and areas, on COVID-19 spread is taken. To extend these studies, this paper is an effort to consider almost all the topological parameters of significant countries and different states of India for analysing their effects on the recovery rate due to COVID-19. Finally, these parameters are ranked/sorted as per their impact on recovery rates. © 2023 The authors.
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"COVID-2019,” a recently emerged novel coronavirus disease, is causing serious health issues to the public and becoming more and more fatal every next day. On December 31, 2019, low respiratory infection cases were detected in Wuhan, China, which is in China's Hubei province. The cases were reported to the WHO Office of China and they could not identify the agents for the cause. The first cases were classified to be "pneumonia of unknown etiology.” The investigation program was initiated by the Chinese Center for Disease Control and Prevention (CDC). The etiology of the disease was attributed to a novel virus of the coronavirus (CoV) family. Dr. Tedros Adhanom Ghebreyesus, WHO Director-General, called the disease caused by this CoV the "COVID-19,” which is an acronym for "coronavirus disease 2019.” It is found that "COVID-19” is caused by bête-coronavirus named "severe acute coronavirus-2” (SARS-CoV-2). It belongs to those virus families that appear as pneumonia in the human body. It affects the lower respiratory tract badly. This virus has been identified as another version of the family of severe acute respiratory syndrome coronavirus (SARS-CoV) and the Middle East respiratory syndrome coronavirus (MERS-CoV) [1, 2]. SARS-CoV-2, SARS-CoV, and MERS-CoV possess similarity with them. They have differences in genotypic and phenotypic structure that guide their pathogenesis. So far, as per the findings, this virus originated in bats. It reached humans through contact with unknown animals. The transmission of this virus among humans is via direct contacts, inhalation of infected droplets, and contaminated hands and surfaces. Some of the symptoms of this disease are cough, sore cough, fever, fatigue, and dyspnea/breathlessness. The remedy of this disease is to diagnose the infection at the initial stage, supportive treatment to survive, self-quarantines, mass-quarantines, etc. This paper presents a systematic review of the origin of coronavirus, its types, transmissions, symptoms, and the current developments in diagnosing testing and vaccine trials. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.
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Following the publication of the original article [1], the authors identified that Supreet Singh Nayyar, Rahul Kurkure, Arun Yadav, Jyoti Mishra, Biswajit Das and Shubankar Tiwari were incorrectly assigned to affiliation 3. The authors are assigned to affiliation 2. The original article [1] has been corrected. © 2023 The Author(s).
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Immunity plays a fundamental role in the maintenance and protection of the human body from infectious and pathogenic microorganisms. It requires regular intake of nutrients for proper functioning of the immune system. Due to an unbalanced lifestyle and consumption of ready-to-eat foods, immunity is being affected negatively. Inflammation and immunity are influenced by diet and nutrition. Simple sugars, trans fats, refined carbs, and processed meat, among other meals, may induce inflammation while simultaneously counteracting the anti-inflammatory benefits of omega-3 fatty acids. As a result, unhealthy food intake may enhance systemic inflammation in individuals, boosting the generation of IL-6. Dietary nutrition is a well-known aspect of immune system maintenance, with the significance of micronutrients prominently featured in a variety of scientific literary works. Currently, global population is susceptible viral infection such as COVID-19. This viral strain is directly attacking the immunity of the individual and bringing it at risk. When a patient's immune system isn't operating correctly, COVID-19 is thought to raise the harshness of the infection or make it more vulnerable to contagious diseases. This review paper will help in understanding the immune responses mechanism along with diet balance and maintaining the sufficiency of vitamins and minerals to fight against COVID-19 infection.Copyright © 2023 The Author(s)
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Coronavirus Disease 2019 (COVID-19) is the most prevalent infectious human disease spreading in several parts of the world caused by SARS Coronavirus 2 (SARS-CoV-2). COVID-19 transmission is mainly spreading via the respiratory tract, personal contact, digestive tract and hospital-acquired infections. Health care workers particularly working in clinics practicing traditional medicine need to be in close contact with patients, so they have a higher risk of SARS-CoV-2 infection. In this paper, therefore, the personal-protective measures need to be followed by healthcare workers in traditional medicine clinics during COVID-19 pandemic are emphasized, to enlighten them about self-protection and to improve the safety of such a special group of traditional healers.Copyright © 2021 Bentham Science Publishers.
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The whole world is suffering from the wave of the novel coronavirus that causes the large-scale death of a population and is proclaimed a pandemic by WHO. As RT-PCR tests to detect Coronavirus are costly and time taking. So now these days, the purpose of the researcher is to detect these diseases with the help of Artificial Intelligence or Machine learning-based models using CT scan images and X-rays images. So the testing cost, time taken and the number of data required could be minimized. In this paper, transfer learning based on three fine-tuned models has been proposed for Covid detection. The performance of these proposed fine-tuned models has been also compared with other competing models to check the accuracy and other matrices. © 2022 IEEE.
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The spread of Coronavirus pandemic and the resulting lockdown has significantly disrupted every facet of human life including education. The education system has never thought of such an unprecedented situation and thus, it had caused a colossal disparity within it. More than 1.2 billion children were out of the classroom, in India almost 32 crore learners stopped going to educational institutions. In India the online learning has many concerns like awareness, its effectiveness, stable internet connectivity, electricity supply, required devices etc. In this study we are trying to address such queries, constraints and to analyse impacts of COVID-19 on the students by understanding their opinion, inclinations and their mental health via an online survey of 399 engineering students in two institutions of Raipur, Chhattisgarh, India. Our results revealed that smartphone is the most popular device since 88.97% used it whereas mobile GPRS is the first choice for the Internet connectivity since 75.18% respondents used it. © 2023, Rajarambapu Institute Of Technology. All rights reserved.
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Purpose: This paper examines the impact of the Covid-19 induced lockdown on selected vegetables to confirm if the vegetable supply chain was disrupted during that period. It attempts to see if direct marketing via FPOs/FPCs helped Indian farmers to cope with adverse situations aroused in vegetable marketing. Design/methodology/approach: This study opted for mixed methods research. First, a granular data set comprising daily observation on wholesale price and the market arrival of vegetables were analysed. Descriptive statistics and Kalmogorov-Smirnov test were used to understand the severity of disruptions in the vegetable supply chain in India during the lockdown. Then, qualitative information from different stakeholders engaged in the vegetable marketing was collected through a phone survey and assessed using content analysis to comprehend how FPOs have helped farmer's during this crisis. Findings: This paper confirms disruptions in the vegetable supply chain. Quantities of chosen vegetables arriving in the mandis were significantly lower than in the previous year for all phases of lockdown. Consequently, prices were much higher than in 2019–2020 for both the lockdown and subsequent phases unlock. Results further suggest that those farmers who are already in networks of FPOs/FPCs are able to get benefited. It was also observed that direct marketing through institutional supports is being more explored in the regions where FPOs/FPCs already exist. Research limitations/implications: Since it is an exploratory study involving a small sample, the research results may lack generalisability. Originality/value: This study provides scope for direct marketing through FPOs/FPCs in improving the food supply chain. © 2021, Emerald Publishing Limited.
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Purpose: Our study aims to compile data on the clinical presentation, pathological and radiological findings in cases of post-COVID mucormycosis, and present the management strategy used in our center. Methods: This is a retrospective cohort observational study based at a tertiary healthcare institution in Northern India. All COVID-positive patients presenting with clinical features of mucormycosis were included in the study. They underwent complete otorhinolaryngeal, medical, and ophthalmological examination after thorough history taking. Biochemical tests, biopsy and imaging studies were done for all the patients. The treatment strategy included a multidisciplinary team approach, that is, intravenous antifungals as well as surgical debridement of necrotic tissue via Modified Denker's approach or open maxillectomy, and orbital exenteration, if required. Patients were followed up for six months to look for recurrence. Results: Twenty-three patients were studied, out of which 14 were males and 9 were females. Pathological findings of 13 out of 15 patients, who underwent surgical debridement revealed mucormycosis as a causative agent, received Amphotericin. Aspergillus was found in two cases which received Voriconazole. Eleven out of 20 patients who were treated in our hospital survived. Three patients were lost to follow up. The average hospital stay of discharged patients was 14 days. Conclusion: Post-COVID mucormycosis was reported at an alarming rate after the second COVID wave in India especially after steroid therapies in diabetic patients. Thus a timely, aggressive, team approach using Modified Denkers or open maxillectomy along with proper intravenous antifungals is the key to survival in such patients. © 2023, The Author(s).
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World is facing a new pandemic called covid-19SARS-CoV-2) since a year ago. Unfortunately there is no treatment for Covid 19 nowadays as well as no potential therapies has been developed to overcome from coronavirus pandemic. Some potential drug molecules with combination have ability to respond for covid19 virus. From the research it was found that the reduction of viral load can be treated with hydroxychloroquine and azithromycin combination. We evaluate the mode of interactions of hydroxychloroquine and azithromycin with the dynamic site of SARS-CoV-2 coronavirus main protease. Molecular Structure-based computational approach viz. molecular docking simulations were performed to scale up their affinity and binding fitness of the docked complex of novel SARS-CoV-2 coronavirus protease and hydroxychloroquine and azithromycin. The natural inhibitor N3 of novel SARS-CoV-2 coronavirus protease were exhibited highest affinity in terms of MolDock score (-167.203Kcal/mol), and hydroxychloroquine was found with lowest target affinity (-55.917 Kcal/mol).The amino acid residue cysteine 145 and histidine 41 is bound covalently and formed hydrogen bond interaction with SARS-CoV-2 inhibitor known as inhibitor N3 as such, hydroxychloroquine and azithromycin also formed hydrogen bond interaction. The binding patterns of the inhibitor N3 of SARS-CoV-2 coronavirus main protease could be used as a guideline for medicinal chemist to explore their SARS-CoV-2 inhibitory potential. Copyright © RJPT All right reserved.
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One of the most difficult aspects of the present COVID19 pandemic is early identification and diagnosis of COVID19, as well as exact segregation of non-COVID19 individuals at low cost and the sickness is in its early stages. Despite their widespread use in diagnostic centres, diagnostic approaches based solely on radiological imaging have flaws given the disease's novelty. As a result, to evaluate radiological pictures, healthcare practitioners and computer scientists frequently use machine learning and deep learning models. Based on a search strategy, from November 2019 to July 2020, researchers scanned the three different databases of Scopus, PubMed, and Web of Science for this study. Machine learning and deep learning are well-established artificial intelligence domains for data mining, analysis, and pattern recognition. Deep learning in which data is passed through many layers and automatically learning the composition of each layer from large dataset and it enables a new way that evaluates the complete image without human guidance to discern which insights are valuable, with applications ranging from object detection to medical image. Deep learning with CNN may have a significant effect on the automatic recognition and extraction of crucial features from X-ray and CT Scan images related to Covid19 analysis. According to the results, models based on deep learning possess amazing abilities to offer a precise and systematic system for detecting and diagnosing COVID19. In the field of COVID19 radiological imaging, deep learning software decreases false positive and false negative errors in the identification and diagnosis of the disease. It is providing a once-in-a-lifetime opportunity to provide patients with quick, inexpensive, and safe diagnostic services while also reducing the epidemic's impact on nursing and medical staff. © 2022 ACM.
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Recent COVID-19 pandemic has highlighted the importance of increase in the ability of public health workforce to detect and respond to the public health threats. For timely implementation of an adequate response and mitigation measure, the standardized and sustainable capacity building programme for frontline public health workforce is the need of hour. National Center for Disease Control (NCDC), Ministry of Health and Family Welfare, in partnership with U.S. Centers for Disease Control and Prevention (CDC), developed a three-month in-service Basic Epidemiology Training programme. This is a tailor-made programme for frontline public health workforce to strengthen epidemiological skills. This training was a practical interactive approach to field epidemiology for three months on the job training for frontline public health workforce that addressed the critical skills needed to conduct surveillance effectively at the local level while focusing on improving disease detection, reporting and feedback. The training also demonstrated the role of learning model in form of interaction between the mentor and the mentees. The importance of handhold support given by the mentors to the mentees in quality outbreak investigations and documentation. © 2022, Indian Association of Preventive and Social Medicine. All rights reserved.
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With an ongoing episode of Covid, the world health security and precaution need reformation and a new approach to be dealt with. The health concerns of the individual is a topic of utmost importance for every nation fighting the pandemic. With limited healthcare staff and the large public to look after, the assistance of Computer vision and AI is needed. Social distancing is a very effective way of containing the spread of a pandemic. Social distancing becomes difficult when dealing with a number of subjects like at gateways of offices, Airports, and many other sectors that have significant footfall in a day. In this paper we have tried to compare the different models for the recognition of mask on the face, for doing so we have used Real world masked face dataset (RMFD) (Iqbal et al, Renewable power for sustainable growth, Springer Nature, Berlin, LNEE, 2020) and Kaggle (Tomar et al, Machine learning, advances in computing, renewable energy and communication, vol 768. Springer Nature, Berlin, LNEE, 2020) dataset. At first we gather the images where face have actual mask on it and also augmented the image with editing the image of unmasked face with mask so that model can learn very details of the image and result will come more accurate and clean. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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This is a paper on disease prediction using machine learning through a python graphical user interface application. The motivation behind this application is the pandemic (Covid- Situation) faced by the whole world and also the idea to robotize the current manual framework of initial diagnosis by the assistance of mechanized supplies and undeniable PC programming so that their important information/data can be put away for a more drawn out period and also for a more useful purpose. This paper introduces the field of diseases prediction, the treatment for the disease, and consulting with the doctors nearby through efficient programming using machine learning. It describes the need for a system of an online artificial doctor, which will not only help them in predicting and understanding the diseases, but it will also advise them of certain medicines that are necessary for controlling or curing those diseases. © 2022 IEEE.
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Objective: To assess safety and outcomes of immediate post-operative transferring of endovascular thrombectomy (EVT) patients to an external hospital ICU. Background: Due to the COVID-19 pandemic, hospital patient volumes increased significantly, resulting in a shortage of ICU beds in various NYC hospitals including the Mount Sinai Hospital system. Patients in the system were often transferred within the network after mechanical thrombectomy was complete if bed availability at EVT site was unavailable. Design/Methods: Reviewed all consecutive EVT cases from January1 2020 - July 31 2021 at the Mount Sinai System for intersystem transfers. Out of the 353 thrombectomy cases that took place between this time frame, 27 patients were transferred to an outside ICU hospital. Patient demographics, co-morbid stroke risk factors (hypertension, diabetes, hyperlipidemia), stroke metrics such as modified rankin score, NIHSS, and TICI score were evaluated for each patient. Key safety outcomes were symptomatic hemorrhage (sICH), groin hematoma requiring manual compression, and unanticipated extubation or hemodynamic instability within the first 24 hours of transfer. Symptomatic ICH was defined as new intracranial hemorrhage associated with NIHSS increase >4 points. Results: Transferred patients were mostly male (n=18) with a mean age was 67.9 years. Twenty- five out of 27 (92.6%) patients achieved a TICI core of 2b or higher. Major neurological improvement, defined as an NIHSS of 0-1 or ≥ 8 point improvement at 24 hours, was achieved in 51.8% of transfer patients. sICH occurred in 3 out of 27 (11%) of patients. There were no unexpected extubation or hemodynamic instability within the first 24hours. All transfer cases had a mRS of 0-3 upon discharge. Conclusions: Transfer post EVT to an outside hospital for close ICU monitoring is associated with 11% sICH risk without any apparent cardiopulmonary risk.
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The research paper aims to examine the impact of digitalization and IoT technologies on the business world and the ways in which it also impacts the global economy. The paper examines that digital Human Resource Management can increase the productivity and the efficiency of both the HR professionals and the employees of the organisation. The findings of the research also reveals that through digitalisation the HR managers can build better relationships between the employee and the company, can think about the wellbeing of the employees better, and make them feel more valued and appreciated. It can also help in creating a competitive atmosphere in the workplace which can motivate the employees. The research further reveals that the active IoT technologies are increasing in the business world. The articles also analyses the advantages and disadvantages of implementing digitalisation and IoT in the workplace and there exists some requirements that need to be met for the successful implementation of the IoT technology. The research also estimates that in future companies can develop their business by adopting digitalisation. In the paper it is also mentioned that during the pandemic the global economy has decreased and to balance the condition it is important to implement digitalisation in the business world. © 2021 IEEE.
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Background: In the year 2019 the National medical commission was ready to roll open its newly framed competency-based curriculum (CBME) and the colleges and medical universities were all geared up and trained to do the same. The tale began at a new pace with the academic session 2020 but was jolted soon by the spreading tentacles of COVID-19 pandemic. This disease made a drastic impact on education delivery system and the medical graduates were soon facing the challenge of not only revised curriculum but also the revised methodology of teaching. The present study compares the outcome of online education for students with CBME and with traditional variant of medical education. Material & Methods: A retrospective survey analysis questionnaire was created on google forms, on the basis of DREEM questionnaire [appendix 1]. The student's perspective was scored on Likert scale. The students were divided into study groups following traditional and CBME curriculum. The SPSS system was utilized to find the mean score of responses and student t-test and chi square tests were used. Results: The comparison of results for student's perception towards online education suggested statistically non-significant outputs between the genders but significant difference for study groups i.e. traditional vs. CBME curriculum. Conclusion: This survey highlighted that curating the new format of curriculum for delivery in an online format would produce better outputs and making availability of resources for use during online classes can increase the performance of students to be better aligned with graduate medical regulations.
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COVID-19 is a disease caused by SARS-CoV-2 that can arouse a respiratory tract infection. Therefore, a rapid identification of clearly visualized infections is urgently needed, which can assist early diagnosis and save the lives of suspected COVID-19 patients.Recent technological progress has made it possible to fuse deep learning classification and medical images that can accelerate and improve the accuracy of results when leveraged. This could particularly be important for disease where faster result and increased accuracy can help early detection of COVID-19 cases vis-à-vis the traditional RT-PCR tests. DNN classifier is designed such that, it automatically detects virus present in lungs using chest image is termed as Bimodal. This research article proposes an automatic frame work for identifying COVID -19 as early using chest X-ray images and CT Scan Images. For this experiment, 3 types of data set are used, 1) COVID X-ray chest 2) CT-scan SARS-COV-2, 3), X-Ray images in the chest (Pneumonia). This deep learning model can detect positive COVID-19 cases more quickly than RT-PCR tests for the detection of COVID-19 cases. The proposed model provides a relationship between COVID-19 patients and pneumonia patients. Color visualization approach on the basis of Grad-CAM is used to clearly interpret image radiology detection. The proposed deep learning model has achieved a total accuracy of 92.33%, with precision and recall of 0.94% and 0.93%. © 2021 IEEE.