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1.
Malaysian Journal of Medical Sciences ; 29(5):83-92, 2022.
Article in English | EMBASE | ID: covidwho-2100663

ABSTRACT

Background: COVID-19 was declared a pandemic by the World Health Organization (WHO). COVID-19 is highly contagious, making it a threat to healthcare workers, including those working in mortuaries. Therefore, it is important to determine if the cause of death (COD) could be identified using limited autopsy, diagnostic tests and post-mortem imaging modalities instead of full autopsy. This study aims to examine the effectiveness of post-mortem imaging, specifically post-mortem computed tomography (PMCT) at determining the COD during a pandemic. Method(s): This cross-sectional study included 172 subjects with suspected or unknown COVID-19 status brought in dead to the institute's mortuary during the pandemic in Malaysia. PMCT images reported by forensic radiologists and their agreement with conventional autopsy findings by forensic pathologists regarding COD were analysed to look at the effectiveness of PMCT in determining COD during a pandemic. Result(s): Analysis showed that 78.7% (133) of cases reported by forensic radiologists concurred with the COD certified by forensic pathologists. Of these cases, 85 (63.9%) had undergone only external examination and real-time reverse transcriptase polymerase chain reaction (rRT-PCR) COVID-19 testing, meaning that imaging was the sole method used to determine the COD besides history from available medical records and the investigating police officer. Conclusion(s): PMCT can be used as a complement to medicolegal autopsies in pandemic contexts, as it provides significant information on the possible COD without jeopardising the safety of mortuary health care workers. Copyright © 2022, Penerbit Universiti Sains Malaysia. All rights reserved.

2.
23rd Annual Conference of the International Speech Communication Association, INTERSPEECH 2022 ; 2022-September:2863-2867, 2022.
Article in English | Scopus | ID: covidwho-2091310

ABSTRACT

In this paper, we describe an approach for representation learning of audio signals for the task of COVID-19 detection. The raw audio samples are processed with a bank of 1-D convolutional filters that are parameterized as cosine modulated Gaussian functions. The choice of these kernels allows the interpretation of the filterbanks as smooth band-pass filters. The filtered outputs are pooled, log-compressed and used in a self-attention based relevance weighting mechanism. The relevance weighting emphasizes the key regions of the time-frequency decomposition that are important for the downstream task. The subsequent layers of the model consist of a recurrent architecture and the models are trained for a COVID-19 detection task. In our experiments on the Coswara data set, we show that the proposed model achieves significant performance improvements over the baseline system as well as other representation learning approaches. Further, the approach proposed is shown to be uniformly applicable for speech and breathing signals and for transfer learning from a larger data set. Copyright © 2022 ISCA.

3.
Journal of the American Society of Cytopathology ; 11(6):S14-S15, 2022.
Article in English | EMBASE | ID: covidwho-2086369

ABSTRACT

Introduction: The telemedicine center of our hospital provides expert consultation services to two rural districts of Punjab. The aim of this study was to assess its utility in Fine needle aspiration Cytology (FNAC) diagnostic service. Material(s) and Method(s): A 2-year retrospective audit from April 2020-2022 covering the COVID pandemic time was carried out on all cases of telecytopathology consultation files of the E-sanjeevani platform. A total of 75 cases whose FNAC smear images clicked by android smartphones with 48MP cameras and sent by Whatsapp to the E-Sanjeevani administrator were included. The images along with brief case clinical details were e-mailed for expert opinion to the cytopathologist (RS). The image quality, ability to provide a diagnosis, site-wise differences and comparison of the referral and expert review diagnosis was made. Result(s): The ages of the patients ranged from 4-80 years (2 children, 73 adults), with 25 males and 50 females. The sites of FNA performed at the district hospital were lymph nodes (22), breast (21), thyroid (15), soft tissue (8), salivary gland (2), skin (4), lip (2) and glans penis(1). The number of Whatsapp images evaluated ranged from 3-20 with median of 11 per case. They were in JPEG file format with size ranging from 40-163kb. Image quality was rated visually as good, medium, and poor in 46 (61%), 21m (28%) and 8(11%) cases respectively. There was no distortion of images upon enlarging them for better visualization on a large monitor. Best accuracy was obtained in breast and lymph nodes FNA. Soft tissue FNA was difficult to interpret and was inconclusive in 3/8 cases. Conclusion(s): Telecytopathology by Whatsapp is simple, quick, feasible and very useful to provide expert opinion in FNAC of various sites thereby enabling the pathologist in the district hospital setting. [Formula presented] Copyright © 2022

4.
NeuroQuantology ; 20(10):9348-9359, 2022.
Article in English | EMBASE | ID: covidwho-2067325

ABSTRACT

Internet hoaxes like COVID-19 pose a danger to the population in southern India. Studying the relevance of COVID's traits has helped academics debunk the bogus news spread on social media. A plethora of concerns were voiced when a small number of unverified reports made headlines recently;such stories may have implications across a wide range of topics, including religion, politics, health, and beyond. More than half of all health-related bogus news is itself fraudulent. Vaccine side effects, drug interactions, outdated medical technology, new viruses, and other factors all contribute to people's declining health. The fake news includes text content, audio files, video files, and photos—most of the fake news is in the form of video content 55%. Social media websites like YouTube, WhatsApp, Twitter, and Facebook, produce false news content. The COVID pandemic is universal, so more than 65% of news is connected internationally. Finally, 75% are treated as fake news;it could be a genuine risk to public health—fake news understanding the social media during the present and future situation. Unfortunately, internet users complain about being shown a flood of similar content that is either misleading or entirely false when searching for relevant and dependable information. Several worries about what has been termed an "infodemic" of misleading material being spread online, including possibly bad advice on some subjects. In this paper, the main objective is to identify fake news in online media and classify the fake news.

5.
10th IEEE International Conference on Healthcare Informatics, ICHI 2022 ; : 502-504, 2022.
Article in English | Scopus | ID: covidwho-2063256

ABSTRACT

Since the start of the COVID-19 pandemic, hospitals have been overwhelmed with the high number of ill and critically ill patients. The surge in ICU demand led to ICU wards running at full capacity, with no signs of demand falling. As a result, resource management of ICU beds and ventilators has been a bottleneck in providing adequate healthcare to those in need. Short-term ICU demand forecasts have become a critical tool for hospital administrators. Therefore, using the existing COVID-19 patient data, we build models to predict if a patient's health will deteriorate below safe thresholds to deem admission into ICU in the next 24 to 96 hours. We identify the most important clinical features responsible for the prediction and narrow down the health indicators to focus on, thereby assisting the hospital staff in increasing responsiveness. These models can help the hospital staff better forecast ICU demand in near real-time and triage patients for ICU admissions as per the risk of deterioration. Using a retrospective study with a dataset of 1411 COVID-19 patients from an actual hospital in the USA, we run experiments and find XGBoost performs the best among the models tested when tuning parameters for sensitivity (recall). The most important feature for the four prediction tasks is the maximum respiratory rate, but subsequent features in order of importance vary between models predicting ICU transfer in the next 24 to 48 hours and those predicting ICU transfer in the next 72 to 96 hours. © 2022 IEEE.

6.
10th IEEE International Conference on Healthcare Informatics, ICHI 2022 ; : 201-210, 2022.
Article in English | Scopus | ID: covidwho-2063250

ABSTRACT

At the beginning of the breakout of a new disease, the healthcare community almost always has little experience in treating patients of this kind. Similarly, due to insufficient patient records at the early stage of a pandemic, it is difficult to train an in-hospital mortality prediction model specific to the new disease. We call this the 'cold start' problem of mortality prediction models. In this paper, we aim to study the cold start problem of 3-days ahead COVID-19 mortality prediction models by the following two steps: (i) Train XGBoost [1] and logistic regression 3-days ahead mortality prediction models on MIMIC3, a publicly available ICU patient dataset [2];(ii) Apply those MIMIC3 models to COVID-19 patients and then use the prediction scores as a new feature to train COVID-19 3-days ahead mortality prediction models. Retrospective experiments are conducted on a real-world COVID-19 patient dataset(n = 1,287) collected in US from June 2020 to February 2021 with a mixed cohort of both ICU and Non-ICU patients. Since the dataset is imbalanced(death rate = 7.8%), we primarily focus on the relative improvement of AUPR. We trained models with and without MIMIC3 scores on the first 200, 400,..., 1000 patients respectively and then tested on the next 200 incoming patients. The results show a diminishing positive transfer effect of AUPR from 5.36% for the first 200 patients(death rate = 5.5%) to 3.58% for all 1,287 patients. Meanwhile the AUROC scores largely remain unchanged, regardless of the number of patients in the training set. What's more, the p-value of t-test suggests that the cold start problem disappears for a dataset larger than 600 COVID-19 patients. To conclude, we demonstrate the possibility of mitigating the cold start problem via the proposed method. © 2022 IEEE.

7.
NeuroQuantology ; 20(11):615-623, 2022.
Article in English | EMBASE | ID: covidwho-2044248

ABSTRACT

The Coronavirus (COVID-19) outbreak has wreaked havoc on people’s life, requiring citizens, businesses, and governments to adapt their responses. The crisis has highlighted the need for governments to respond swiftly, honestly, and effectively while ensuring accountability, retaining faith in public policies and actions, and engaging and partnering with communities and stakeholders in several ways.The crisis affected all states and Union Territories of India. District Pulwama is located in Jammu and Kashmir Union Territory. More than two hundred doctors are working in the district.According to the 2011 census, the Pulwama District has 327 census villages, with 08 of them being uninhabited, and the district’s total population is 5.60 lac. This paper used a hybrid approach, integrating quantitative and qualitative research techniques. The publicly accessible government figures were used to examine socioeconomic and demographic factors. The offices of the Directorate of Health Services Kashmir and the Pulwama-based Chief Medical Officer provided information about the district’s healthcare system. One hundred respondents participated in this research.A dynamic management approach required for the healthcare industry is presented. This approach is built on scientific, administrative, and geographic variables to providethe district’s population with immediate and efficient diagnoses at the nearest healthcare establishments in the future.

8.
Med Mycol ; 60(Suppl 1), 2022.
Article in English | PMC | ID: covidwho-2042619

ABSTRACT

 : Poster session 2, September 22, 2022, 12:30 PM - 1:30 PMObjectives:To study the prevalence of COVID-19-associated mucormycosis in a tertiary center.To analyze the factors responsible for the incidence of mucormycosis and outcome of the patients in relation to COVID 19 positive status. Methods: This is a retrospective observational study with (n = 433) samples. Nasopharyngeal and oropharyngeal swabs were collected from clinically suspected cases of mucormycosis. These were then subjected to Xpert® Xpress SARS-CoV-2 test. Those samples which tested positive were included in this study. A history sheet form was designed and filled up for 238 patients who were admitted and could be traced properly. Results: Total number of SARS CoV-2 positive samples was 296 out of 433 samples. Among the tested 66% (287/433) were male and 34% (146/433) were female. Relative positivity was higher among women 74% (107/144) than men 66% (189/285). Amongst 238 admitted patients, 83.1% patients (198/238) completed treatment and were discharged while 13.9% (33/238) succumbed during management. Diabetes was found to be most common risk factor at 88.65% (211/238) followed by hypertension 36.5% (87/238), and corticosteroid use 26.89% (64/238). Type 2 DM was the most frequent with 96% (203/211) and only 4% (8/211) patients having Type 1 DM. Amongst the diabetics, 74.8% (158/211) were on medication (oral hypoglycemic agent/insulin/both), and 16.6% (35/158) of them were non-compliant to treatment. Most common clinical presentation was facial swelling 66.8% (159/238) followed by facial pain 52.52% (125/238) and headache 34.45% (82/238). Microscopy findings either nasal or palatal scraping was positive in 69.75% (166/238) cases with aseptate hyphae in 95% (157/166) patients while septate hyphae were seen in 3% (5/166). Both aseptate and septate hyphae were present in 2% (4/166 patients).Amongst the deceased patients 91% (30/33) had diabetes mellitus exclusively of Type 2 and among them, 33% (10/30) were not on any anti-diabetic medication. It was also observed that among the deceased people who were on anti-diabetic medication, 45% (9/20) were non-compliant to the treatment. Combinedly 63.33% (19/30) diabetics were on inadequate blood sugar level control which might be strongly contributing to mortality. Conclusion: Percentage of clinically suspected mucor patients who were COVID-19 positive was 67%. Although male patients were twice more tested than females but females were proportionately more positive for SARS-CoV2. Type 2 diabetes mellitus was the lead risk factor, with facial swelling being the predominant presentation. Interestingly, fungal smear was positive in 166 out of 238 patients. Despite the best management, the mortality rate was 13.86% in such patients. It was also seen that untreated or non-compliance to anti-diabetic treatment resulted in increased mortality. Hence, it's necessary to review cases of COVID-19-associated mucormycosis for prompt management and to reduce morbidities and mortality.

9.
Indian J Med Microbiol ; 40(4): 585-587, 2022.
Article in English | MEDLINE | ID: covidwho-2036080

ABSTRACT

This study (August-September 2021) estimated the seroprevalence of SARS-CoV-2 neutralizing antibodies in the general population of Delhi and correlated it with their anti-SARS-CoV-2 IgG levels. Samples were selected by simple random sampling method. The neutralizing capacity was estimated by performing a surrogate virus neutralization test (sVNT) (GenScript), Piscataway, NJ, USA. A total of 2233 (87.1%, 95% C.I. 85.7, 88.3) of the 2564 SARS-CoV-2 IgG seropositive samples had detectable SARS-CoV-2 neutralizing antibodies. In samples with S/CO â€‹≥ â€‹4.00, the neutralizing antibodies ranged from 94.5% to 100%. The SARS-CoV-2 neutralizing antibody seroprevalence strongly correlated with the S/CO range of IgG SARS-CoV-2 (r â€‹= â€‹0.62, p â€‹= â€‹0.002).


Subject(s)
COVID-19 , SARS-CoV-2 , Antibodies, Neutralizing , Antibodies, Viral , COVID-19/epidemiology , Humans , Immunoglobulin G , Seroepidemiologic Studies
10.
Kidney international reports ; 7(9):S477-S478, 2022.
Article in English | EuropePMC | ID: covidwho-2034051
11.
2022 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing, COM-IT-CON 2022 ; : 61-65, 2022.
Article in English | Scopus | ID: covidwho-2029201

ABSTRACT

This paper aims to forecast and visualize the confirmed cases, deaths, and recoveries of COVID-19 in India and also predict the end of the growth of COVID-19 cases in India. The methods used for the prediction of future COVID19 cases are machine learning techniques, improved logistic growth equation with a dynamic rate of infection, and automation of the calculations using Python programming language. The paper discusses the current models being used to predict the flattening of the curve, and the pros and cons of using these techniques. The paper then presents the solution and results achieved using our method. The average accuracy percentage of predictions of total confirmed cases was 85.6%, deaths were 84.5%, and recoveries were 83.8%. According to the predictions, the curve started to flatten in October and the curve will completely flatten in the 2nd week of January which confirms the situation that prevailed in India. © 2022 IEEE.

12.
Journal of Young Pharmacists ; 14(3):283-288, 2022.
Article in English | Web of Science | ID: covidwho-2025170

ABSTRACT

Background: The Severe Acute Respiratory Coronavirus (SARS-CoV-2) has emerged in a variety of forms since its first appearance in early December 2019. The Omicron variation (B.1.1.529) was recently confirmed as a relatively new Variant of Concern (VOC). There are several mutations in this S-protein, making it an exclusively lethal version of the protein. Omicron variants feature multiple mutations clustered in a region of S protein that is the principal target of antibodies, and these mutations may have an impact on the binding affinities of antibodies to the S protein, as demonstrated by structural analysis. Materials and Methods: Google, Sciencedirect, Web of science, and ResearchGate databases have been explored for potentially existing research to obtain the most emerging trends and up-to-date metadata on various perspectives of Omicron variants. Conclusion: There is evidence that the Omicron variant's mutations may interfere with antibody binding in people who have been exposed to the SARS-CoV-2 virus in the past. At the moment, there is very little information on the Omicron version. Therefore, mutation dispersion evaluations, evolutionary links to previous variants, and putative structural effects on antibody binding effects are all explored in this work. Results: In the current state of pandemic crises, the comprehension of Omicron will pave a path for healthcare professionals to treat infectious conditions very well.

13.
3rd International Conference for Emerging Technology, INCET 2022 ; 2022.
Article in English | Scopus | ID: covidwho-2018882

ABSTRACT

As going to word using the 5G in some of the countries and many country are going to move to use this technology we want to know what this technology will give to the users and what will be the states of user experience while using this technology, during the last two years we have seen completely shift in education, work, shopping, gaming, streaming, meeting and many other activates it all shift into an online mode due to the covid-19 pandemic and that but high data rate low latency internet in the top list of the user requirement list this two service is the promised things of 5G and what will be the change and the effect in our daily life application with this technology something like IOT will be the main thing to have in every home, Industry, school, Hospital, City, country. How could this technology improve the use and the processing of all these applications that what we are going to discuss in this paper. © 2022 IEEE.

14.
Psychosomatic Medicine ; 84(5):A7, 2022.
Article in English | EMBASE | ID: covidwho-2002987

ABSTRACT

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.

15.
Benchmarking-an International Journal ; : 18, 2022.
Article in English | Web of Science | ID: covidwho-1985248

ABSTRACT

Purpose The unexpected outbreak of COVID-19 has expedited the trend toward online education. To facilitate undisruptive learning, EdTech companies are continuously working on providing solutions to restore teaching and learning practices. This has caused a significant behavioral shift of the investors in the EdTech market. This study aims to analyze the effects of Web Market Traffic on the increased number of investors funding an EdTech Company in the market. Design/methodology/approach By drawing on the multi-method web analytics approach, this study analyses the nexus between Web Market Traffic and Investor's Behavior in the US and India, proving the hypothesized relationship in the proposed Model using a data sample of 300 EdTech Players. Findings There is a significant difference between the investor's behavior in India and the US. This study shows that the investors in the US are more inclined towards investing in EdTech companies in comparison to India. The Results demonstrate that monthly visits of consumers and the number of acquisitions by players positively affect the investor's behavior, while bounce rates take a toll on the number of investors. Practical implications This Study suggests that EdTech investors in the US and India should harness Web Traffic to capture the EdTech market. Further, this study offers practical implications that EdTech players can use to attract potential investors and increase brand visibility by improving web market traffic parameters. Originality/value This paper's original contribution is to empirically shed light on the effects of web market traffic on the investor's behavior. The study emphasizes the quintessentiality of managing the bounce rates and monthly visits for an EdTech market to attract more investors and capital inflow that enhance brand visibility. The study found that the investors behave distinctly in the developed and emerging markets in the US and India.

17.
IEEE Frontiers in Education Conference (FIE) ; 2021.
Article in English | Web of Science | ID: covidwho-1978384

ABSTRACT

The Research to Practice Full Paper aims to determine the students' preferable learning mode for traditional classroom teaching under typical situations and forced virtual teaching in quarantine. Although many academic institutions have promoted online and distance education, face-to-face traditional classroom teaching has always been the dominant approach in the US. However, the sudden outbreak of the COVID-19 disease forced the academic institutions to convert the entire curriculum into a virtual method of teaching and learning. This drastic change in the educational system has dramatically impacted educators and students, and very little is known about the students' preferred teaching and learning mode. In this regard, the present study collected data from the civil engineering department at the University based on a questionnaire survey. A total of 337 students participated and responded to 18 multiple-choice questions. The questionnaire was divided into four parts: admission information, activity information, Fundamentals of Engineering Examination (FE exam) planning, and preferred teaching mode. The preferred teaching mode is further divided into 'Synchronous' that represents scheduled and live virtual engagements with students such as zoom meetings and ' Asynchronous', representing prerecorded lectures such as watching YouTube videos. This study performed two analyses to obtain precise results regarding: (1) online vs. traditional classroom learning, (2) synchronous vs. asynchronous learning. For this purpose, distinct tree-based methods, including bagging, boosting, and random forest were employed to investigate the significant factors that affect the students' choice of learning modes under different circumstances. The findings reveal that the activities such as participation in clubs or organizations, internships, or jobs, are the statistically significant factors that play a vital in the students' choice of teaching and learning mode under different situations. This study is expected to provide crucial insights for the academic professionals in adopting the teaching and learning modes that would substantially improve and enhance the quality of education.

18.
International Journal of Production Research ; : 20, 2022.
Article in English | Web of Science | ID: covidwho-1978076

ABSTRACT

The COVID-19 pandemic has caused critical challenges for e-commerce warehouses that strive to fulfill surging customer demand while facing a high virus infection risk. Current literature on picking optimization overlooks warehouse safety under pandemic conditions. Meanwhile, scattered storage and zone-wave-batch picking have been used in parallel by many large e-commerce warehouses, these two operational policies have not been considered together in picking optimization studies. This paper fills these gaps by solving an order batching problem considering scattered storage, zone-wave-batch picking, and pickers' proximity simultaneously. We formulate and solve the mathematical model of the discussed problem and propose the Aisle-Based Constructive Batching Algorithm (ABCBA) to help warehouses pick more efficiently and safely. Experiments with extensive datasets from a major third-party logistics (3PL) company show that, compared to the current picking strategy, ABCBA can reduce the total picking time and the virus infection risk due to pickers' proximity by 46% and 72%, respectively. Compared to other heuristics like tabu + nLSA3 (Yang, Zhao, and Guo 2020), ABCBA gets better results using less computation time.

19.
Managing Complexity and COVID-19: Life, Liberty, or the Pursuit of Happiness ; : 130-144, 2022.
Article in English | Scopus | ID: covidwho-1975137
20.
Gastroenterology ; 162(7):S-1247, 2022.
Article in English | EMBASE | ID: covidwho-1967428

ABSTRACT

Introduction Vaccines have emerged as our primary line of defence against the scourge of COVID-19. Patients with cirrhosis have a higher risk of severe COVID-19 and mortality and are thus high priority patients for vaccination. However, cirrhotics were excluded from the phase 2 and 3 trials of COVID-19 vaccines. Hence, we aimed to assess the seroconversion rate and safety of currently available COVID-19 vaccines in India, namely COVISHIELD (ChAdOx1 nCoV-19) and COVAXIN (BBV 152), in patients with cirrhosis. Methods All patients who had attended tele-hepatology services at our institute from March 2020 to June 2021 and diagnosed with cirrhosis as per their medical records were telephonically interviewed in July 2021 using a pre-specified questionnaire. Patients who had completed full course of vaccine (with the 2nd dose being administered at least 2 weeks back) and without history of documented COVID-19 infection (pre or post vaccination) were tested for SARS-CoV-2 IgG antibodies using an automated chemiluminescent assay (Orthoclinical Diagnostics). Our primary outcome was seroconversion in patients with cirrhosis who had received complete COVID-19 vaccination. Secondary outcomes included vaccine acceptance, documented COVID-19 infection post-vaccination and adverse effects of COVID-19 vaccines in cirrhosis. Results We identified and interviewed 784 patients with cirrhosis [compensated: 213 (27.2%), decompensated 561 (72.8%)] with a mean age of 51.07 ± 8.53 years. Two eighty-three (36.1%) patients had received at least 1 dose of COVID-19 vaccine [COVISHIELD: 231 (29.5%), COVAXIN: 52 (6.6%)] and 159 (20.3%) patients had completed full course of vaccination with 2 doses [COVISHIELD: 134 (17.1%), COVAXIN: 25 (3.2%)]. Documented COVID-19 (on RT-PCR) was reported in 3.2% (9/283) patients who had received at least one dose of COVID-19 vaccine while breakthrough COVID-19 (at-least 2 weeks after administration of 2nd dose) was reported in 3.1% (5/159). Adverse events were reported by 19.8% (56/283) patients with the most common being fever (13.1%), myalgia (5.6%) and sore throat (1.1%). No grade III/IV adverse events were reported. So far, 100 fully vaccinated patients (COVISHIELD: 88, COVAXIN: 12) have been tested for seroconversion. Seroconversion rate with COVISHIELD and COVAXIN were 92% (81/88) and 91.7% (11/ 12), respectively. Seropositive patients were divided into high, moderate, and low antibody responses based on the observed signal/cut-off response and no differences were observed between patients with compensated and decompensated cirrhosis (Table 1). There was no correlation between antibody signal/cut-off ratios and CTP (tau: 0.07, p=0.32) or MELD (tau: 0.08, p=0.29) scores. Conclusion Our preliminary data suggests that currently available COVID-19 vaccines in India are safe with high seroconversion rates in patients with cirrhosis. (Table Presented)

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