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Convolutional neural network (CNN) is the most widely used structure-building technique for deep learning models. In order to classify chest x-ray pictures, this study examines a number of models, including VGG-13, AlexN ct, MobileNet, and Modified-DarkCovidNet, using both segmented image datasets and regular image datasets. Four types of chest X- images: normal chest image, Covid-19, pneumonia, and tuberculosis are used for classification. The experimental results demonstrate that the VGG offers the highest accuracy for segmented pictures and Modified Dark CovidN et performs best for multi class classification on segmented images. © 2023 Bharati Vidyapeeth, New Delhi.
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The study proposes a novel deep learning-based model for early and accurate detection of the Tomato Flu virus, also known as tomato fever, which has recently emerged in children under the age of five in the Indian state of Kerala. The model utilizes a deep learning method to classify skin pictures and check whether a person is suffering from the virus or not, with an accuracy of 100% and a validation loss of 0.2463. Additionally, an API is developed for easy integration into various web/app frameworks. The authors highlight the importance of careful management of rare viral diseases, especially in the context of the ongoing COVID-19 pandemic. © 2023 Bharati Vidyapeeth, New Delhi.
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The re-emergence of SARS-CoV, known as SARS-CoV-2, has proven extremely infectious that has infected a huge population worldwide. SARS-CoV-2 genome is translated into polyproteins that is processed by virus-specific protease enzymes. 3CLprotease is named as the main protease (Mpro) enzyme that cleaves nsp4 to nsp16. This crucial role of Mpro makes this enzyme a prime and promising antiviral target. Till date, there is no effective commercially available drug against COVID-19 and launching a new drug into the market is a complicated and time-consuming process. Therefore, drug repurposing is a new but familiar approach to reduce the time and cost of drug discovery. We have used a high-throughput virtual screening approach to examine FDA approved library, natural compound library, and LOPAC 1280 (Library of Pharmacologically Active Compounds, Sigma-Aldrich, St. Louis, MO) library against Mpro. Primary screening identified potential drug molecules for the target, among which ten molecules were studied further using biophysical and biochemical techniques. SPR was used to validate the binding of inhibitors to purified Mpro and using FRET-based biochemical protease assay these inhibitors were confirmed to have Mpro inhibitory activity. Based on the kinetic studies, the antiviral efficacy of these compounds was further analysed by cell-culture based antiviral assays. Four out of ten molecules inhibited SARS-CoV-2 replication in Vero cells at a concentration range of 12.5 to 50 muM. The antiviral activity was evaluated by RT-PCR assay and TCID50 experiments. The co-crystallization of Mpro in complex with inhibitor for determining their structures is being carried out. Collectively, this study will provide valuable mechanistic and structural insights for development of effective antiviral therapeutics against SARS-CoV-2.
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In the past few years, technology has changed drastically and due to COVID-19 pandemic, people spend more time on screen. The use of social media platforms has also been increased and this affects the human mind and decision taking ability. Online career counseling is largely supported these days and hence this paper proposes an online career prediction system using supervised machine learning based on the user's profile. This research attempted to develop a model for the user which predicts the career path in a precise manner and gives actionable feedback and career recommendations to encourage them to make significant career judgments. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.
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For medical image processing analysis, deep learning is one of the most popular research subjects. It is subset of machine learning comprising of one or more neural network layers to simulate human behavior of learning and predicting. The purpose of this work is to investigate the application of deep learning models in image processing for disease analysis and medical innovations. The work showcased a generic deep learning model based on convolutional neural networks to classify diseases upon image analysis. To demonstrate the extensive medical use case of proposed model, the results demonstrated classifying pneumonia x-ray images alongside normal chest x-ray images, wrist r-ray pictures able to distinguish between normal and fractured wrists, etc. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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A novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused a global pandemic that started in China (Wuhan, Hubei region) in December 2019, called Coronavirus disease. This systematic review intends to evaluate the correlation of pre-existing particulate matter (PM2.5) induced comorbidities along with COVID-19 spread and mortality. A search was operated to report the association between PM2.5 and COVID-19 outbreak and evaluating the PM2.5 related disease affected by COVID-19 infection. The research was conducted in consent with the criteria of PRISMA (Preferred Reporting Items for Systematic Reviews, and Meta-Analyses). We filtered the review and research articles published only in the English language and selected these keywords: air pollution, particulate matter, COVID-19, health impact. We obtained a total of 27 appropriate published articles in their final version. Additional articles were rectified by searching through Scopus, PubMed and Google Scholar. We concluded that the values of coagulation biomarkers in all SARS-CoV-2 patients were considerably higher as compared with healthy people. It was noted that Hypertension, Diabetes, COPD, CVD, Asthma and Cancer possess an evident relation with COVID-19 severity. Globally, air pollutants affect the body's immunity, leading to people being more susceptible to pathogens. In addition, transmission from person-to-person dynamic of the new respiratory virus was considered the environmental factors' role in accelerating coronavirus spread and its lethality. COVID-19 patients with pre-existing comorbidities induced by particulate matter show a high risk of mortality as compared to COVID-19 patients without these comorbidities. © 2022 Technoscience Publications. All rights reserved.
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Background: Allogeneic hematopoietic stem cell transplantation activity is growing globally as one of the curative treatment options for many hematological diseases. A stem cell transplant registry plays an important role in such treatment. Setting up a functional stem cell donor registry is quite challenging with several issues such as resources, donor recruitment, donor attrition, ethnicity, lack of support, and impact of coronavirus disease 2019 (COVID-19). Aim(s): The aim of the current study was to present the experience of a resource-constrained registry in India as well as the effect of COVID-19 on its operations. Settings and Design: The present study was a descriptive study which was designed to study the functioning of a resource-constrained registry from north India. Material(s) and Method(s): The study data for the period of 2012-2020 pertaining to donor recruitment, number of searches, number of matched donors, number of transplants performed, and donor attrition was collected from the registry software "Prometheus." Statistical Analysis: Descriptive statistics such as frequency and percentage was used. Result(s): During the past 9 years of operation, the registry has faced several issues pertaining to lack of funds, donor recruitment, donor attrition, and COVID-19 has exacerbated their pain points significantly. The registry has recruited a total of 20,093 donors, of which only 7794 have been human leukocyte antigen typed, with the remaining samples awaiting funding. Out of this small number of typed donors, registry has performed 15 matched unrelated donor transplants for Indian and international patients. As a result of COVID-19, donor attrition was on the rise and showed a peak in 2020. During the year 2020, the number of searches, donor recruitment camps, and donors all decreased considerably. Conclusion(s): The establishment and operation of a stem cell transplant registry necessitate extensive planning and resources. The resource-constrained registries face a number of issues pertaining to effective functioning and future developments. The external support and awareness for the cause can help minimize the pain points of these registries. Copyright © 2022 Indian Journal of Transplantation.
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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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BACKGROUND: Acute kidney injury (AKI) is a clinical syndrome denoted by an abrupt decline in glomerular filtration rate (GFR) sufficient to decrease the elimination of nitrogenous waste products (urea and creatinine) and other uremic toxins. Based on the type of setting AKI can be Community Acquired (CA-AKI) or Hospital Acquired (HA-AKI). These two types have different epidemiological etiological and outcome profiles and these characteristics have remained inconclusive. As far as the etiological spectrum is concerned;previous studies have demonstrated a varied spectrum in both these groups. Very few studies comparing the outcome of CA-AKI and HA-AKI were found in the literature search. There is a paucity of relevant comparative Indian studies on these two types of AKI. Hence this prospective observational study was undertaken to compare the demographic and clinical spectrum and short-term in-hospital outcomes of patients belonging to both these groups who were admitted to the largest tertiary care government teaching hospital in the state of Uttarakhand. AIM OF THE STUDY: To compare the demographic and clinical spectrum and short-term in-hospital outcomes of community-acquired versus hospital-acquired Acute Kidney Injury in hospitalized patients METHODS: It is a prospective cohort study conducted from October 2020 to December 2021. The study was conducted in the In-Patient Department (IPD) areas of the Department of Nephrology and all those departments whose consultations for patients with suspected AKI were sent to the Department of Nephrology at AIIMS Rishikesh. Patients fulfilling the following inclusion criteria were enrolled in this study- Age -18 years and the patients diagnosed as having AKI as per KDIGO 2012 definition. Those aged <18 years of age and those with CKD or Acute on CKD were excluded from the study. CKD was defined as per the KDIGO 2012 definition. Each enrolled patient was classified as having Community-acquired AKI (CA-AKI) or Hospital-acquired AKI (HA-AKI). Those admitted to the hospital with AKI were denoted as having CA-AKI. In contrast, patients were identified as having HA-AKI when AKI was not apparent upon hospital admission but was diagnosed beyond 24 hours of hospitalization. The sample size of 65 in community-acquired AKI and 32 in the hospital-acquired AKI group was calculated. Study subjects underwent detailed history clinical examination and relevant investigations required in the management of AKI episodes. The stage of AKI at presentation was assessed as per KDIGO Clinical Practice Guidelines for Acute Kidney Injury 2012. Ethical clearance was obtained. RESULT(S): A total of 65 patients with CA-AKI and 32 patients with HA-AKI were enrolled. The mean age of patients in the CA-AKI group was 46.7 years and in the HA-AKI group was 45.5 years. The CA-AKI group had significantly higher-baseline serum creatinine (P < 0.001), serum creatinine at admission (P < 0.001), proportion of male patients (P = 0.09), proportion of patients requiring renal replacement therapy (P = 0.02), proportion of patients getting admitted to medical IPDs (P < 0.001), proportion of patients whose baseline creatinine was unknown (P < 0.001), proportion of patients presenting in Stage 3 of AKI (P = 0.001), proportion of patients having oligoanuria (P = 0.09) and hyperkalemia (P = 0.06) at presentation. The HA-AKI group, on the other hand, was found to have a significantly higher- proportion of patients getting admitted to surgical IPDs (P < 0.001), proportion of patients who underwent a prior surgical procedure (P < 0.001), proportion of patients having coexisting lung disease (P = 0.09), liver disease (P = 0.03), heart disease (P = 0.06) and COVID-19 (P = 0.04). Sepsis was found to be the most common cause (70.7%) in the CA-AKI group and was also one of the common causes (28.12%) in the HA-AKI group. Despite more patients in the CA-AKI group being in AKI-Stage 3 at presentation, in-hospital mortality was observed to be lower in this group (35.4% versus 62.5%, P = 0.04). The median survival time of patients was und to be more than double in the CA-AKI than in the HA-AKI group (59 days versus 23 days). However, on comparing the overall survival using the log-rank test, both groups were found to be comparable (chi-square value 1.82, p-value 0.18). Univariate analysis for predictors of mortality showed that the type of AKI (CA vs HA) (P = 0.01), type of admission (ward vs ICU) (P = 0.001), surgical procedure prior to AKI onset (P = 0.018), presence of comorbidities such as DM (P = 0.038), lung disease (P = 0.000), and COVID-19 (P = 0.018) and requirement of vasopressor support (P = 0.009) were significant predictors of mortality of patients with AKI admitted to our center. Also, the length of hospital stay (P = 0.037), serum creatinine at admission (P = 0.002) and serum creatinine at discharge/death (P = 0.003) have been found to predict the mortality of these patients. However, Cox proportional hazard regression analysis for finding out independent predictors of mortality showed that only two factors, i.e., the presence of lung disease (HR 2.65, 95% CI 1.03-6.79, P = 0.042) and the requirement of vasopressor support at presentation (HR 5.28, 95% CI 1.75- 15.97, P = 0.003) predicted the survival of AKI patients. Thus, the present study showed that type of AKI was not an independent predictor of mortality in AKI patients admitted to our center. CONCLUSION(S): The majority of patients in both groups of AKI presented in Stage 3. Sepsis was found to be the most common cause in the CA-AKI group and was also one of the common causes in the HA-AKI group. On comparing the inhospital outcomes of AKI episodes, it was observed that both recovery (complete or partial) and dialysis dependency were more common in the patients with CA-AKI while mortality was found to be more in the HA-AKI group. However, on Cox proportional hazard regression analysis it was found that only two factors, i.e., the presence of lung disease and the requirement of vasopressor support at presentation predicted the survival of AKI patients admitted to our center. Thus, the present study showed that type of AKI was not an independent predictor of mortality in such patients. Further, more long-term and larger multi-center studies are required to study the course and outcome of patients with AKI and to outline the regional variances in its patterns in the Indian population.
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PurposeThis paper aims to proffer a broad overview of publications in the International Journal of Contemporary Hospitality Management (IJCHM) by conducting bibliometric analyses for the duration ranging from 1989 to 2022. Design/methodology/approachThe research approach analyses the top authors, publications, most collaborative countries and top co-occurring keywords and significant themes published in IJCHM with the help of the Scopus database. The study entails performance analyses on IJCHM. A de-duplicating process was used to study the evolution of themes, so that the keywords identified from co-occurrences of authors' keywords and thematic evolution map were refined to first- and second-order themes, further leading to the development of inductive analysis proposing aggregate themes. FindingsThe findings of this study not only help paint a comprehensive picture of the customer experience, but also illustrate how topics have evolved in the literature and reveal the most relevant upcoming fields of research. The thematic evolution map reveals thematic areas. There is evidence of contributions by authors across the world and spanning a multitude of themes such as business ethics, corporate and firm performance, stakeholders and avenues for the management of disruption, specifically in times of the COVID-19 pandemic outbreak. Research limitations/implicationsSignificant trends in authors, publications, nations, authors' keywords and themes as uncovered by this study can greatly help budding authors understand the expectations and emerging research themes that define the IJCHM. Originality/valueThrough extensive bibliometric analyses, this study has created a historical log of the publications in IJCHM. It has identified the key research trends for future research and presented a conceptual framework based on the keyword analysis map and thematic evolution.
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An appropriate mask protects individuals from infectious illness and greatly minimises the spread of COVID-2019 in public spaces like institutions and temples. This needs surveillance technology capable of detecting persons wearing correctly fitted masks. However, this is not the purpose of the face detection algorithms that are currently in use.The researchers suggest a two-stage technique for identifying mask wear using hybrid machine learning algorithms in this paper. The first step involves identifying as many possible candidate locations for wearing masks as possible employing Faster RCNN and ResNet V2 structures.In comparison, the second step entails employing a massive learning system to validate the real face masks. It is achieved by the training of a model with two classes. Additionally, this article describes a data collection conducted during the Market, Malls and contains 2804 realistic images. The suggested method exceeds all other techniques that are already in use, with an accuracy rate of 99.2 percent for straightforward circumstances. Copyright © 2022, Anka Publishers. All rights reserved.
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As the COVID-19 outbreak spread from early 2020 on, synchronous and asynchronous online learning became the predominant delivery method in the education system. This is the inaugural time that educational programs have indeed been totally given online across the state. So, this research aims to study the Indian student's perception of synchronous and asynchronous online courses amid COVID-19. This study involved 655 responses from UG students of various Indian educational institutions. In this study, we utilized basic random sampling to gather data, and SPSS was used to analyse the data. To narrow down the enormous dimensionality, the acquired data were subjected to a factor analysis utilizing a principal component analytical method. The results of the study demonstrate that synchronous can be challenging at times and puts more responsibility on the students. Asynchronous learning also gives the learners the chance to independently investigate and explore the subjects that have been given to them. Another reason why asynchronous exercises were perceived as burdensome by students was the large number of handwritten tasks that had to be turned in quickly. The COVID-19 outbreak has indeed been difficult for both students and teachers nationwide. Yet, teachers have supported students' use of digital learning tools. Therefore, asynchronous and synchronous online courses together have produced balanced learning.
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Disasters are a complex phenomenon that calls for a steady and multi-disciplinary approach to help create a multi-layered picture of the vulnerability of hazards and risks for the community. India is highly vulnerable to natural disasters, losing about 2 percent of the Gross Domestic Product annually (World Bank 2003). Almost every part of the country falls in at least one hazard zone or other. The socioeconomic vulnerability makes it difficult for people to cope with the impact of these hazards. Humans adapt in a variety of ways that may often remain unnoticed and unorganized. Further, in order to manage the effects of climate-change- induced disasters, autonomous adaptation is frequently overlooked - both at national and international levels. This chapter looks at various disasters across Indian subcontinent, in which the community has been affected badly and how human resilience and adaptation have revived the society and led to sustainable development. The case studies in this chapter would cover the reasons for the disaster and corrective actions taken for some repeated disasters such as cyclones, earthquakes and the COVID-19 pandemic. Copyright © 2022 Wolters Kluwer Medknow Publications. All rights reserved.
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As the COVID-19 outbreak spread from early 2020 on, synchronous and asynchronous online learning became the predominant delivery method in the education system. This is the inaugural time that educational programs have indeed been totally given online across the state. So, this research aims to study the Indian student's perception of synchronous and asynchronous online courses amid COVID-19. This study involved 655 responses from UG students of various Indian educational institutions. In this study, we utilized basic random sampling to gather data, and SPSS was used to analyse the data. To narrow down the enormous dimensionality, the acquired data were subjected to a factor analysis utilizing a principal component analytical method. The results of the study demonstrate that synchronous can be challenging at times and puts more responsibility on the students. Asynchronous learning also gives the learners the chance to independently investigate and explore the subjects that have been given to them. Another reason why asynchronous exercises were perceived as burdensome by students was the large number of handwritten tasks that had to be turned in quickly. The COVID-19 outbreak has indeed been difficult for both students and teachers nationwide. Yet, teachers have supported students' use of digital learning tools. Therefore, asynchronous and synchronous online courses together have produced balanced learning. Copyright © 2022 Wolters Kluwer Medknow Publications. All rights reserved.
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At the end of 2019, the world saw a storm of a fatal virus that spread across the globe infecting people, blocking their progress as well as lives. Covid-19, coronavirus, had knocked out as the world's strongest pandemic that snuffed everything and everyone. With due research done on this infection, we came up with some cautions to be adopted, like social distancing, going out lesser or whenever needed, and the usage of masks which sounded to be a highly efficient way to secure oneself from inhaling this virus. Thus, with this production of masks reached its peaks, but the key challenge that persists is choosing the best one. Finding the right mask causing least problems today and tomorrow meanwhile protecting one’s health could be termed as the best here. We surveyed around 5000+ mask users of different age groups in northwestern part of an Asian country i.e. India, marking different parameters of their masks usage like its type, period of use, and general problems while or after using the mask, etc. Upon analysis, we found the most prominent masks used are cotton masks and surgical masks irrespective of the age group. Our findings showcased that with cotton mask one may leads to lesser after hassles of masks. Each mask types carries some after effect of its usage, wherein the most common disease found acne followed with breathlessness keeping these two-mask type as parameter. © 2022 American Institute of Physics Inc.. All rights reserved.
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For medical image processing analysis, deep learning is one of the most popular research subjects. It is subset of machine learning comprising of one or more neural network layers to simulate human behavior of learning and predicting. The purpose of this work is to investigate the application of deep learning models in image processing for disease analysis and medical innovations. The work showcased a generic deep learning model based on convolutional neural networks to classify diseases upon image analysis. To demonstrate the extensive medical use case of proposed model, the results demonstrated classifying pneumonia x-ray images alongside normal chest x-ray images, wrist r-ray pictures able to distinguish between normal and fractured wrists, etc. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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SESSION TITLE: COVID-19 Infections: Issues During and After Hospitalization SESSION TYPE: Original Investigations PRESENTED ON: 10/17/2022 01:30 pm - 02:30 pm PURPOSE: To characterize the health care utilization (HCU) of patients after discharge from a hospitalization due to Coronavirus Disease 2019 (COVID-19). METHODS: Retrospective analysis from a national cohort using the Optum Clinformatics Data Mart. Included all adults hospitalized with a primary diagnosis of COVID-19 between April 2020 and March 2021, with prior 12 months of continuous enrollment. HCU of patients discharged to a home setting was evaluated in three periods (0-90 days;91-180 days;181-275 days post-discharge). HCU was defined as emergency department (ED) visits, inpatient (IP) admissions, rehabilitation/skilled nursing facility (SNF) admissions, outpatient (OP) and telemedicine visits and was expressed as the number of visits per 10,000 person-days to adjust for time from discharge. We also examined the distribution of office visits by provider specialty RESULTS: We identified 91,374 unique patients who were discharged alive after a hospitalization due to COVID-19. A greater percentage of patients was discharged to a home setting (n=63,674 or 65.6%: home 41.54%;home with home health services 14.65%: home with outpatient services 4.42%) than to a non-home setting (26.23%: i.e., SNF, hospice, rehabilitation facility, etc.). The patients discharged to a home setting were mostly white (58.8%), females (53.4%), whose mean age was 72.4 (SD± 12). The percentage of office visits to Primary care provider (57.8%;48.3%, 47.7%), Cardiology (7.7%;8.0%;7.4%) Pulmonary medicine (4.7%;3.9%;3.1%) varied in the 3 time periods evaluated. Additionally, the outpatient visits to endocrinology (1.3%, 1.6%, 1.7%), Neurology (1.1%, 1.5%, 1.5%), Physical Medicine & Rehabilitation (0.7%, 1.0%, 1.2%), Psychiatry (0.7%, 0.9%, 1.1%) and other mental health professionals (0.4%, 0.5%, 0.5%) increased over time. CONCLUSIONS: In our nationally representative study, health care utilization remains high among patients discharged to a home setting after a hospitalization due to COVID-19. Additionally, the use of mental health services increased overtime among survivors. CLINICAL IMPLICATIONS: Understanding post-discharge health care utilization of patients after an index hospitalization due to COVID-19 will help health systems prepare and allocate resources for the most likely to be used services. DISCLOSURES: No relevant relationships by Alexander Duarte No relevant relationships by Yong-Fang Kuo No relevant relationships by Shawn Nishi, value=Consulting fee Removed 04/03/2022 by Shawn Nishi No relevant relationships by Efstathia Polychronopoulou No relevant relationships by Daniel Puebla Neira No relevant relationships by Gulshan Sharma No relevant relationships by Mohammed Zaidan