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COVID-19 is highly contagious in nature. It marked a grievous impact on the world's social and economic status and disrupted the other related domains as well. Global mental health is debatable in present scenario. Travel and Tourism industry is one of the hardly hit sectors which clearly and badly influenced the aviation industry. In this article, we highlight some of the major global sectors which are highly affected by the pandemic, including social and financial turmoil caused by COVID-19, healthcare front, environment, education, and travel. A reasonable weightage is given to situation in India while discussing about the different impacts. Since the outbreak, researchers have been working feverishly to leverage a broad range of technologies to tackle the global threat. The Internet of Things (IoT) is one of the forerunners in this field. The Internet of Things (IoT) has gained popularity as a new research field in a variety of academic and industry fields in recent years, particularly in healthcare. This article investigates and highlights the overall applications of the well-proven IoT tools and technologies in all the COVID-19 impacted domains by providing a perspective roadmap to combat this global threat. Various myths or misconceptions regarding COVID-19 have also been discussed and explained logically. © 2023 Scrivener Publishing LLC.
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Maintaining the purported Social Separating is one of the essential and greatest ways to stop the new popular episode. Legislators are enacting restrictions on the standard of private distance between people in order to concur with this restriction. In light of this real-life occurrence, it is crucial to evaluate how consistent with realistic imperatives in our lives this is, in order to ascertain the causes of any prospective cracks in such distance obstacles and determine whether this portends an anticipated risk. In order to do this, we offer the Visual Social Removing (VSD) problem, which is defined as the automatic evaluation of the difference between the depiction of connected person aggregations and the private separation from an image.When this requirement is violated, it is vital for VSD to conduct painless research to determine whether people agree to the social distance restriction and to provide assessments of the degree of wellbeing of particular places. We first draw attention to the fact that measuring VSD involves more than simply math;it also suggests a deeper comprehension of the social behavior in the setting. The goal is to genuinely identify potentially dangerous circumstances while avoiding false alerts (such as a family with children or other family members, an elderly person with their guardians), all while adhering to current security protocols. Then, at that point, we discuss how VSD links to earlier research in social sign handling and demonstrate how to investigate fresh PC vision techniques that might be able to address this issue. Future issues about the viability of VSD systems, ethical repercussions, and potential application scenarios are the result. © 2023 IEEE.
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Anxiety disorders have seen an elevating number since the Covid-19 pandemic. This paper aims at identifying more about the various anxiety disorders using machine learning Techniques. Further, symptoms of the types of anxiety disorders: Generalized Anxiety Disorder, Panic Disorder, Post-Traumatic Stress Disorder, Obsessive-Compulsive Disorder and Social Anxiety Disorder are also discussed. The datasets used in the paper are collected by researchers from hospitals/organizations/educational institutions mainly through questionnaires and surveys. Some of the many Machine Learning techniques used for prediction of these anxiety disorders include Random Forest, Linear Regression, Support Vector Machine among others. Lastly, the performance metric for the techniques is presented here and henceforth, the result is drawn from this available data followed by the conclusion. © 2022 IEEE.
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Background: The widespread of novel coronavirus is a global concern. With each passing day, the novel coronavirus is making its new borders. Research has revealed that the virus produced in bats and was transmitted to humans firstly in Wuhan, Hubei province of China in December 2019. The contagion has been significantly acting as a vulnerable threat to the copious world. Objective(s): The purpose of this review is to provide the valuable and detailed information about the his-torical background, origin, classification, morphology, transmission pattern, symptoms, treatment pa-rameters, number of cases as well as the preventive measures of the corona-virus disease 2019 (COVID-19). Method(s): The disease is transmitted to people by inhalation or close contact of infected droplets. To split the sequential chain of this contagious disease, we need to know about its symptoms, ways of transmission, preventive measures and safety guidelines. Result(s): The widespread of COVID-19 is a global concern. The morbidity rate due to COVID-19 is in-creasing regularly. As for now, there is no specific cure, vaccine and treatment;the lives can be saved only by following the preventive measures and safety guidelines. Conclusion(s): Several clinical trials are in progress for the development of a suitable vaccine to combat this disease. This review shall be helpful in providing detailed information regarding the novel coronavi-rus so that the sequences of such infection can be reduced.Copyright © 2021 Bentham Science Publishers.
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In the context of the nidovirales order, the coronavirus (Covid-19) is a virus family i.e. extracted from Ribonucleic Acid (RNA) viruses. The pandemic ensued due to it has already infected 9,716,060 people across the globe and is still causing problems with mutations of concern. Because of the immense number of infected patients, and the resulting deficiency of testing kits in hospitals;a rapid, reliable, and automatic detection system is in extreme need to curb the numbers. SARS-Cov-2 is an influenza kind of virus that can be detected using imaging techniques. It is important to distinguish between Covid-19 (caused by SARS-Cov-2) disease against pneumonia disease infected patients and healthy person's chest x-ray scans respectively. Advanced computational techniques like ML (machine learning) and DL (deep learning) had proven to be extremely useful in image processing, especially for the processing of medical images. In this work, 2906 images were taken from the publically available datasets. Various transfer learning-based DL models are applied to these images. Resulting that the ML-based classifiers effectively categorizing the input images (normal/Covid-19/pneumonia). The model achieves 96.3% accuracy with the VGG19 model and Logistic Regression (LR) classifier. This model proves to be highly convenient in treating this pandemic disease Covid-19. © 2022 IEEE.
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The audit profession has experienced a digital transformation over the last decade with a rapid shift towards remote auditing forced by the recent COVID pandemic. This shift has led to changes related to the way auditors work and perceive. Through a survey of external auditors, this study addresses how the changes from on-site audits to remote audits affect audit success (i.e., audit quality, audit efficiency, and auditors' job satisfaction);we also explore dispositional and situational factors that contribute to remote audit success. Our results show that working remotely leads to high audit efficiency. Auditors' flexibility management competency leads to high remote audit quality and efficiency;and a physical working environment conducive for concentrating on audit tasks is positively associated with audit efficiency and auditors' job satisfaction for remote audits. We also found that working remotely could enhance audit quality and efficiency when the audit firm provides sufficient support to auditors. Our study provides insights for audit firms, regulators, and other stakeholders as they evaluate the many challenges the audit profession faces in achieving remote audit success. © 2023 John Wiley & Sons Ltd.
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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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Objectives: This study aims to assess the magnitude of anxiety, stress, and depression among private practitioners during the COVID pandemic 2020. Materials and Methods: A descriptive, observational and cross-sectional study was done using a semi-structured questionnaire through electronic media in the form of Google Forms. Patient health questionnaire 4 (PHQ-4) and Perceived Stress Scale-10 were incorporated into the questionnaire to assess the psychological status in November 2020. Results: A total of 157 private practitioners participated in the study. Males constituted 60%. Participants have mean age (SD) of 44.2 (6.9) years with work experience and a mean (SD) of 16.3 (6.78) years. The anxiety and depression subscales of the PHQ-4 scale showed that 54.7% of the private practitioners have anxiety subscale ≥3 and 28% have depression subscale >3 needing further psychiatric evaluation. Significant predictors of anxiety and depression were female gender, younger and less experienced, and those providing inpatient services in COVID care isolation facilities. Conclusion: There is a high prevalence of anxiety, stress, and depression among private practitioners comparable with other countries. ©2022 Published by Scientific Scholar on behalf of Indian Journal of Physiology and Pharmacology.
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Background: Complicating the pandemic are the healthcare disparities experienced by ethnic minorities, including Black and Hispanic Americans. This is further exacerbated in those ethnic subgroups, especially if they have comorbidities, including cancer. With the introduction of COVID-19 vaccines, the shift is now focused on promoting vaccination. However, vaccine hesitancy and motives of why ethnic minority cancer patients receive or do not receive vaccines have not been explored and are the focus of this study. Methods: A cross-sectional survey was administered among cancer patients to understand the knowledge and attitude towards COVID-19 vaccines at a single institution in a predominantly ethnic minority population between February 1-June 30, 2021. The participant's inclusion criteria were >18 years old and diagnosed with solid or hematologic malignancy. Descriptive statistics were used to summarize the patient characteristics, COVID-19 vaccine knowledge, and uptake motives. A composite score of COVID-19 and vaccine knowledge was derived and its role on vaccination status was assessed using a multivariable logistic regression model. Results: Of 52 patients surveyed, COVID-19 vaccination prevalence during the survey was 40.4% (95% CI: 27, 54.9). Participants' average (sd) age was 63.5 (13.6) years;42% were male, 36% were Black, and 30% Hispanic;65% were receiving active treatment for their cancer. Seventy-nine percent believed COVID-19 infection to be dangerous or harmful to them, 61% were concerned about the side effects of the COVID-19 vaccine, yet 65% considered vaccines safe. Those refusing the vaccine (n=7) cited side effects (71%) or believed that the vaccine was not needed (57%). Of those who were unvaccinated (n=31), 48.4% (n=15) got vaccinated post-survey. The odds of vaccination was 3.79 (1.63, 8.82) times higher with a 1 unit increase in COVID-19 knowledge score but was not significant in the multivariable model. The final model suggested that the odds of vaccination increased 2.9 times more for a 1 unit increase in vaccine knowledge score;Blacks were two times more likely to get vaccinated and those with more than high school education had a five-fold increase in vaccination. The model results are presented in Table. Conclusions: This exploratory study has demonstrated that there are multiple reasons why an ethnic minority cancer patient would be vaccinated and possible reasons why they would not. This information will become important in improving vaccine campaigns targeting these populations and ensuring their safety and protection against COVID-19.
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Background: COVID 19 infection has worse outcomes and is more severe among frail patients and with co-morbidities. Additionally, it was suggested by Mehta et al that gastrointestinal malignancies may have worse outcomes. Based off these findings, we have evaluated outcomes and potential predictors of these outcomes in patients with gastrointestinal malignancies and COVID 19 infection. Methods: We conducted a retrospective evaluation of 69 cases of patients between February 2020 to February 2021, that had both COVID 19 infection and a gastrointestinal malignancy, including: gastric, colon, pancreatic, biliary, and hepatic. We studied population general characteristics, most common tumors, oxygen requirements, management and death frequencies, at two urban safety net hospitals. Results: The median age of patients with gastrointestinal malignancies and COVID-19 infection was 68 (33 to 87), it was more frequently seen among males (N = 46, 67%);patients had on average 1-7 other comorbidities (N = 55, 80%), hypertension being the most common (N = 43, 62%). All patients in this study were minorities, the majority being Hispanics (N = 32, 46%), followed by African Americans (N = 21, 30%), the rest of patients were minorities such as Asians, Native Americans, and others (N = 16, 23%). We noted that more than half of patients were obese (N = 24, 35%), or overweight (N = 19, 28%), with a mean BMI of 26.24. Most patients were non-smokers (N = 39, 57%). The performance status among these patients was excellent in the large majority (ECOG 0-2;N = 62, 90%). The majority of malignant tumors were adenocarcinomas (N = 63, 91%);others included neuroendocrine tumors (N = 4, 6%) and gastrointestinal tumors (N = 2, 3%), the majority were advanced stage disease between stage III to stage IV disease (N = 38, 55%). The most common malignancy was colon cancer (N = 44, 64%), and also was the most reported deaths were among patients those patients with colon cancer (N = 13, 57%). Among patients that needed to be hospitalized (N = 43, 62%), needed some sort of oxygen supplementation (N = 36, 84%), a small number of patients required ICU admission (N = 8, 19%). When hospitalized, patients were mostly treated with hydroxychloroquine (N = 30, 70%) and steroids (N = 7, 16%). Inflammatory markers such as D-dimer, ferritin, C-RP were not reported in the majority of cases. Overall, the case fatality rate across all gastrointestinal malignancies was noted to be substantial (N = 22, 32%), 35% in males and 26% among females (p = 0.46). Conclusions: During the first year of the COVID-19 pandemic, patients with gastrointestinal malignancies who were infected, seemed to have a high chance to be admitted to the hospital (62%), once hospitalized the majority required some sort of oxygen supplementation (84%) and had a high case fatality rate from the COVID-19 infection (32%).
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SARS-CoV-2 is highly infectious and has ability to mutate into newer, more contagious, and lethal strains. Moreover, presence of comorbidities and low immunity increases the COVID-19 susceptibility and severity. Thus, COVID-19 is challenging to treat and eradicate globally. This increase stress and anxiety among the patients, worsening their condition. Even health care workers (HCWs) are distressed and anxious while managing the COVID-19. Mental stress and depression increases risk of COVID-19. Yogic breathing techniques may be beneficial in improving immunity and reducing stress and anxiety. The present study investigated the effectiveness of short and controlled Yoga-based breathing protocols in COVID-positive, COVID-recovered and HCWs. Study subjects were recruited from Postgraduate Institute of Medical Education and Research, Chandigarh, India from 13th October, 2020 to 7th January 2021. Each group was randomly divided into intervention or yoga group and non-intervention or control group. COVID-positive practiced a 5-min routine and COVID-recovered and HCW practiced 5-min and 18-min routines for 15 days. Pre-post estimation of neuropsychological parameters and heart rate variability and baseline, 7th and 15th day estimation of biochemical parameters, 6-minute walk and 1-minute sit-stand tests were conducted. Based on Ayurveda, Prakriti-type was assessed. WBC count was elevated in COVID-positive intervention (p<0.001) and control groups (p=0.003). WBC count (p=0.002) and D-dimer (p=0.002) was decreased in COVID-recovered intervention. A non-significant reduction in perceived stress and tension was noted in COVID-positive intervention. Tension was reduced and quality of life improved in HCW intervention (p>0.05). The Kapha Prakriti (48.9 %) was dominant among COVID-19 infected (positive and recovered) subjects. Distance covered in 6-min increased after intervention in COVID-positive (p=0.01) and HCW (p=0.002). The covered distance was more after intervention in all groups than control sub-group. COVID-positive intervention group shows reduced heart rate (p>0.05) and high-frequency power (p=0.01). The interventions were capable of improving exercise capacity in patients and HCW and reduced cardiovascular risk in COVID-19. The studied breathing protocol can be integrated for the management of COVID-19 and is beneficial to HCWs.
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Online mode during COVID-19 has raised mental health issues among students along with the stigma around it that continues to exist. As a result of which, there are people who are not comfortable in disclosing their personal details to outsiders for help. This brings in the use of AI applications that can help in this critical issue requiring regular monitoring. In this paper, a survey is conducted to know about awareness about this topic. A review about many conversational AI chatbots is provided that are helpful for handling mental health issues such as stress, anxiety, and depression in a number of ways. These include voice and text based chatbots developed in the last decade. The strengths and limitations of these are also discussed. © 2022 IEEE.
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Despite the availability of GNSS on consumer devices enabled personal navigation for most of the World population in most of the outdoor conditions, the problem of precise pedestrian positioning is still quite challenging when indoors or, more in general, in GNSS-challenging working conditions. Furthermore, the covid-19 pandemic also raised of pedestrian tracking, in any environment, but in particular indoors, where GNSS typically does not ensure sufficient accuracy for checking people distance. Motivated by the mentioned needs, this paper investigates the potential of UWB and LiDAR for pedestrian positioning and tracking. The two methods are compared in an outdoor case study, nevertheless, both are usable indoors as well. The obtained results show that the positioning performance of the LiDAR-based approach overcomes the UWB one, when the pedestrians are not obstructed by other objects in the LiDAR view. Nevertheless, the presence of obstructions causes gaps in the LiDAR-based tracking: instead, the combination of LiDAR and UWB can be used in order to reduce outages in the LiDAR-based solution, whereas the latter, when available, usually improves the UWB-based results.
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Covid-19 has quickly emerged as a global threat, tipping the world into a new phase. The delay in medical care because of the quickly rising Covid-19 cases makes it necessary to overcome the manual and time taking technique such as RTPCR. This paper implements different pre-trained CNN feature extraction models using various Machine Learning (ML) classifiers on chest CT scans to analyze Covid-19 infected patients. It may be observed from the obtained results that accuracy of 96.4% was obtained using the VGG16 model and neural network classifier. The implementation of pre-trained models and classifiers reduce the time taken for manual detection of disease and helps doctors to prevent life of a patient. © 2021 IEEE.
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Background: Patients (pts) with cancer have increased morbidity and mortality associated with the development of SARS-Co-V2 infection. The mRNA vaccines BNT162b2 and mRNA-1273 have robust safety and efficacy with 95-97% prevention of severe COVID-19 disease and development of protective antibody titers in 92 - 100% of healthy individuals. By contrast, some pts with hematological malignancy fail to produce anti-spike antibodies (Ab) despite full courses of vaccination. This is particularly true for pts with non-Hodgkin lymphomas (NHL) and chronic lymphocytic leukemia (CLL) who are actively treated with or have received B cell-directed therapies (BCT). We recently demonstrated that NHL pts who received the COVID-19 vaccine within 9 months from BCT demonstrated markedly lower rates of seroconversion (11%) compared to healthy individuals (100%) or a cohort of older (age >65y) residents of a nursing home (91.5%). NHL pts who had received BCT more than 9 months before the vaccine responded more robustly (88%) (Ghione et al, Blood 2021). Here, we update the results of our earlier study and perform an analysis to identify factors that may help predict for adequate response to COVID-19 vaccines. We hypothesized that neutrophil (N) or lymphocyte (L) counts and/or N/L ratio (NLR) at baseline might predict for adequate Ab production in response to COVID-19 vaccines after receipt of BCT. Methods: This was an observational study performed at Roswell Park Comprehensive Cancer Center. Pts with NHL/CLL who had received COVID-19 mRNA vaccine were included, vaccine response was assessed as previously described (Ghione et al, Blood 2021). For NLR calculation, pts with CLL or NHL with blood involvement were excluded. Clinical variables were analyzed with the t test and Fisher's exact test;validity and cut-off for the L count was obtained with the ROC analysis using GraphPad9 and SPSS. Results: A total of 142 pts with various types of NHL and CLL receiving standard of care treatments were enrolled. Five pts with prior exposure to COVID-19 infection were excluded from the analysis, reaching a total n=137. Of 83 pts with NHL (n=57) and CLL(n=26) in our cohort who were vaccinated within 9 months of BCT, 14 (17%) seroconverted. Baseline N count (p= 0.5), sex (p= 0.2), age (p= 0.8), number of prior lines of treatment (p= 0.4), type of disease (p= 0.7), time from end of BCT to vaccine (p= 0.7) and type of vaccine (p= 0.08), did not affect the rate of seroconversion. For analysis of N and L counts, 37/137 pts with CLL/NHL involving peripheral blood were excluded. Among the remaining 100 pts, 76 had received/were receiving BCT and 24 were either on observation or were on a treatment not including BCT. Only 26/76 (34%) pts on treatment/previously treated with BCT mounted IgG Ab response, while 22/24 (91.6%) patients who were not on BCT mounted the IgG Ab response (p= 0.007). In these NHL pts (N=100), higher L counts and higher NLR were associated with an increased IgG response to the vaccine (p= 0.019). For pts on BCT (N=76) a higher L count (cut-off of 1455 lymphocytes per cubic millimeter of blood [µL] as noted in the ROC) was associated with a higher rate of COVID-19 vaccine response (p= 0.020, with a sensitivity of 84% and specificity 71.5%). Conclusion: In this study, we confirm that pts with NHL/CLL receiving COVID-19 vaccination while on active treatment or within 9 months of treatment with BCT respond poorly to COVID-19 mRNA vaccination (17% seroconversion). Although a higher lymphocyte count and NLR ratio were associated with improved seroconversion rates, these were not powerful predictors. Further study of specific lymphocyte sub-populations that contribute to effective vaccine induced immunity in the context of BCT is ongoing. These studies will help to define optimal strategies for immunization in patients receiving BCT. [Formula presented] Disclosures: Torka: TG Therapeutics: Membership on an entity's Board of Directors or advisory committees. Griffiths: Takeda Oncology: Consultancy, Honoraria;Alexion Pharmaceuticals: Consultanc , Research Funding;Abbvie: Consultancy, Honoraria;Novartis: Honoraria;Taiho Oncology: Consultancy, Honoraria;Celgene/Bristol-Myers Squibb: Consultancy, Honoraria, Research Funding;Boston Biomedical: Consultancy;Astex Pharmaceuticals: Honoraria, Research Funding;Genentech: Research Funding;Apellis Pharmaceuticals: Research Funding.
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OBJECTIVES: Countries throughout the world are experiencing COVID-19 viral load in their populations, leading to potential transmission and infectivity of asymptomatic COVID-19 cases. The current systematic review and meta-analysis aims to investigate the role of asymptomatic infection and transmission reported in family clusters, adults, children and health care workers, globally. STUDY DESIGN: Systematic review and meta-analysis. METHODS: An online literature search of PubMed, Google Scholar, medRixv and BioRixv was performed using standard Boolean operators and included studies published up to 17 August 2021. For the systematic review, case reports, short communications and retrospective studies were included to ensure sufficient asymptomatic COVID-19 transmission data were reported. For the quantitative synthesis (meta-analysis), participant data from a collection of cohort studies focusing on groups of familial clusters, adults, children and health care workers were included. Inconsistency among studies was assessed using I2 statistics. The data synthesis was computed using the STATA 16.0 software. RESULTS: This study showed asymptomatic transmission among familial clusters, adults, children and health care workers of 15.72%, 29.48%, 24.09% and 0%, respectively. Overall, asymptomatic transmission was 24.51% (95% confidence interval [CI]: 14.38, 36.02) among all studied population groups, with a heterogeneity of I2 = 95.30% (P < 0.001). No heterogeneity was seen in the population subgroups of children and health care workers. The risk of bias in all included studies was assessed using the Newcastle Ottawa Scale. CONCLUSIONS: For minimising the spread of COVID-19 within the community, this study found that following the screening of asymptomatic cases and their close contacts for chest CT scan (for symptomatic patients), even after negative nucleic acid testing, it is essential to perform a rigorous epidemiological history, early isolation, social distancing and an increased quarantine period (a minimum of 14-28 days). This systematic review and meta-analysis supports the notion of asymptomatic COVID-19 infection and person-to-person transmission and suggests that this is dependent on the varying viral incubation period among individuals. Children, especially those of school age (i.e. <18 years), need to be monitored carefully and follow mitigation strategies (e.g. social distancing, hand hygiene, wearing face masks) to prevent asymptomatic community transmission of COVID-19.
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COVID-19 , Adult , Asymptomatic Infections , Child , Humans , Quarantine , Retrospective Studies , SARS-CoV-2ABSTRACT
The COVID-19 pandemic that emerged around December 2019 claimed millions of lives. For vaccine development, S protein on viral envelope that binds to ACE2 receptor on cells for entry was identified as vaccine candidate. S protein consists of Receptor Binding Motif (RBM) in the S1 subunit followed by the S2 subunit with an intermediate furin cleavage site. A stabilized version of S protein with 2 proline residues was used as antigen. Overall, most vaccines exhibited efficacy between 80 and 95%. However, being a RNA virus that is prone to mutations along with selection pressure on S protein and frequent use of convalescent plasma led to evolution of variants. These variants are responsible for multiple waves of infection observed globally. In our review, we discuss current data on vaccines and its efficacy in neutralizing SARS-CoV-2 from Wuhan and its variants. Further, our docked mutations observed in variants on the ACE2-S complex cryo-EM structure show that mostly the S1 domain is under selection pressure where major mutations occur in the N terminal domain (NTD), RBM and junction near S1-S2 subunit. Therefore, this review would be a reference for development of new candidate antigen(s) with better efficacy against variants.
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Background: Increased rates of TE have been reported in patients (pts) with coronavirus disease (COVID-19), even without prior predisposition to thrombosis. Patients with cancer are already predisposed to a hypercoagulable state. We aimed to assess whether COVID-19 further increased the risk of TE in pts with active cancer at Montefiore Medical Center, Bronx, NY. Methods: The EMR of 90 cancer pts diagnosed with COVID-19 from March 15 to April 10 , 2020 were reviewed. COVID-19 testing was performed by PCR of nasal swab samples. Active cancer was defined as disease treated <1 year. Reports of imaging studies performed <30 days of the COVID-19+ test, either for new symptoms or for other reasons, were reviewed for new arterial (ATE) and/or venous thromboses (VTE). Patient were followed for 30 days from the date of COVID-19+ test for development of TE, hospital length of stay (LOS) and mortality. Results: Of 90 pts, 11 (12.2%) were found to have 13 new TE within 30 days of COVID-19+ test, 8 (8.9%) arterial and 5 (5.6%) venous. Of the 8 ATE, 7 were new strokes and/or microvascular cerebral disease (MCD) and 1 was a spleen infarct (SI). Of the 5 VTE, 3 were deep venous thrombosis, 1 pulmonary embolism (PE) and 1 patient presented with a superficial VTE. Two patients had 2 new TE each;stroke/PE and MCD/SI, respectively. Peak Ddimer (DD) value was higher in the TE group;mean DD (SD), TE vs no TE, 7.1 (3.4) vs 6.4 (7) ug/mL, p=0.03. Pts on either prophylactic or therapeutic anticoagulation (AC) had less TE;AC vs no AC, 9.1% vs 90.9%, p=0.0003. Only 1 pt on Enoxaparin prophylaxis developed TE. Of the 20 pts on therapeutic AC, 25% were newly started due to concern for thrombosis;the rest were already receiving AC for other reasons. Mortality was higher in the TE group;HR, TE vs no TE, 2.6, 95% CI (1.2 - 5.6), p=0.009. There was no correlation of cancer type, disease stage (metastatic or not), administration of prior chemotherapy or immunotherapy, common comorbidities, patient setting (inpatient, ICU, outpatient, ED visit), LOS or ventilation status with increased incidence of TE. Conclusions: Pts with COVID-19 have high rates of TE, and this is true for our pts with cancer. A high incidence of ATE was noted. TE was associated with increased mortality.
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We have demonstrated the use of an iteratively severed model of deep learning which associates for diagnosing Covid-19 pulmonary demonstration of using chest X-rays. In this paper, a customized convolutional neural network model is trained and analyzed on publicly available chest X-rays to grasp modality-strict feature demonstrations. Since the best performing models learn iteratively to make the model memory efficient, this model also learns and tries to improve the results with each step and classify the chest X-rays in their categories accurately. Then another model which predicts the length of stay of a patient at the hospital is created using multi-layered data processing approach. This model will empower hospitals for on time interference to prevent confusions and better management of hospital resources. We propose a method that uses catboost model which generally classifies the data in multiple classes. As a result, this study provides modality strict iterative and knowledge reusable model which influences Covid-19 detection and length of stay prediction. © 2021 IEEE.