Claim Extraction and Dynamic Stance Detection in COVID-19 Tweets

The information ecosystem today is noisy, and rife with messages that contain a mix of objective claims and subjective remarks or reactions. Any automated system that intends to capture the social, cultural, or political zeitgeist, must be able to analyze the claims as well as the remarks. Due to the deluge of such messages on social media, and their tremendous power to shape our perceptions, there has never been a greater need to automate these analyses, which play a pivotal role in fact-checking, opinion mining, understanding opinion trends, and other such downstream tasks of social consequence. In this noisy ecosystem, not all claims are worth checking for veracity. Such a check-worthy claim, moreover, must be accurately distilled from subjective remarks surrounding it. Finally, and especially for understanding opinion trends, it is important to understand the stance of the remarks or reactions towards that specific claim. To this end, we introduce a COVID-19 Twitter dataset, and present a three-stage process to (i) determine whether a given Tweet is indeed check-worthy, and if so, (ii) which portion of the Tweet ought to be checked for veracity, and finally, (iii) determine the author's stance towards the claim in that Tweet, thus introducing the novel task of topic-agnostic stance detection. © 2023 ACM.

Association Between COVID-19 Pandemic and Serious Mental Illness: Systematic Review within Salutogenesis Model for Public Health Management

Background: The outbreak of the COVID-19 pandemic, the constant transformation of the SARS-COV-2 virus form, exposure to substantial psychosocial stress, environmental change, and isolation have led to the inference that the overall population's mental health could be affected, resulting in an increase in cases of psychosis. Objective(s): We initiated a systematic review to determine the impact of the SARS-COV-2 virus and its long-term effects-in both symptomatic and asymptomatic cases-on people with or without psychosis. We envisioned that this would give us an insight into effective clinical intervention methods for patients with psychosis during and after the pandemic. Method(s): We selected fifteen papers that met our inclusion criteria, i.e., those that considered participants with or without psychiatric illness and exposed to SARS-COV-2 infection, for this review and were retrieved via Google, Google Scholar, MEDLINE, PubMed, and PsychINFO Database. Key Gap: There is a dearth of research in understanding how COVID-19 affects people with or without a prior personal history of psychosis. Result(s): The systematic review summary provides insight into the state of knowledge. Insights from the systematic review have also been reviewed from the salutogenesis model's perspec-tive. There is moderate evidence of new-onset psychosis during the COVID-19 pandemic in which some antipsychotics treated the psychotic symptoms of patients while treating for COVID-19. Suggestions and recommendations are made for preventive and promotive public health strategies. Conclusion(s): The Salutogenesis model and Positive Psychology Interventions (PPI) provide another preventive and promotive public health management approach.Copyright © 2023 Bentham Science Publishers.

Fusion-Based Segmentation and Classification of Novel Coronavirus or Pneumonia from Chest X-ray Images Using Machine Learning Techniques

The deadfall widespread of coronavirus (SARS-Co V-2) disease has trembled every part of the earth and has significant disruption to health support systems in different countries. In spite of such existing difficulties and disagreements for testing the coronavirus disease, an advanced and low-cost technique is required to classify the disease. For the sense of reason, supervised machine learning (ML) along with image processing has turned out as a strong technique to detect coronavirus from human chest X-rays. In this work, the different methodologies to identify coronavirus (SARS-CoV-2) are discussed. It is essential to expand a fully automatic detection system to restrict the carrying of the virus load through contact. Various deep learning structures are present to detect the SARS-CoV-2 virus such as ResNet50, Inception-ResNet-v2, AlexNet, Vgg19, etc. A dataset of 10,040 samples has been used in which the count of SARS-CoV-2, pneumonia and normal images are 2143, 3674, and 4223 respectively. The model designed by fusion of neural network and HOG transform had an accuracy of 98.81% and a sensitivity of 98.65%. © 2022 IEEE.

Does Electronic Banking Improve the Bank Performance of Indian Public Sector Banks: A Study of Post Covid Scenario

The research is to see how E-banking affects the performance of a bank. As a measure of the electronic method of transactions, we used credit cards, debit cards, National Electronic Fund Transfer (NEFT), Real Time Gross Settlement (RTGS), and Point of Sale (POS), while Return on Assets (ROA) was used as a metric of profitability. The research focuses on India's top ten public sector banks, as determined by market capitalization. The findings reveal that digital payment instruments considerably influence return on assets, indicating that internet banking might help banks increase their profitability. Furthermore, the study shows that electronic banking has a significant favorable influence on bank profitability. Financial institutions were able to reduce their banking expenses after the advent of e-banking services. Furthermore, technical progress in the banking industry provides additional potential for banks to improve their interaction with customers, easier access to banking facilities by clients, and banks' market reach with e-banking.

Seeing Should Probably Not Be Believing: The Role of Deceptive Support in COVID-19 Misinformation on Twitter

With the spread of the SARS-CoV-2, enormous amounts of information about the pandemic are disseminated through social media platforms such as Twitter. Social media posts often leverage the trust readers have in prestigious news agencies and cite news articles as a way of gaining credibility. Nevertheless, it is not always the case that the cited article supports the claim made in the social media post. We present a cross-genre ad hoc pipeline to identify whether the information in a Twitter post (i.e., a "Tweet") is indeed supported by the cited news article. Our approach is empirically based on a corpus of over 46.86 million Tweets and is divided into two tasks: (i) development of models to detect Tweets containing claim and worth to be fact-checked and (ii) verifying whether the claims made in a Tweet are supported by the newswire article it cites. Unlike previous studies that detect unsubstantiated information by post hoc analysis of the patterns of propagation, we seek to identify reliable support (or the lack of it) before the misinformation begins to spread. We discover that nearly half of the Tweets (43.4%) are not factual and hence not worth checking-a significant filter, given the sheer volume of social media posts on a platform such as Twitter. Moreover, we find that among the Tweets that contain a seemingly factual claim while citing a news article as supporting evidence, at least 1% are not actually supported by the cited news and are hence misleading.

Predictors of COVID-19 anxiety in UK university students

The current research aimed to evaluate UK student mental health during the first 4 months of the COVID-19 pandemic. Specifically, we were interested in exploring factors that contribute to students' anxiety levels about COVID-19. Demographics, mental health symptomatology and well-being-related variables were tested as predictors of COVID-19 anxiety. A cross-sectional online survey was completed by 1,164 UK university students (71.8% females). Measures included self-reported data on stress, social phobia, anxiety, depression, psychotic-like experiences, hopefulness, group membership, social identity, belonging, loneliness, COVID-19 related variables and demographics. The majority of participants were between 18 and 24 years old (92.2%), White British (57.9%) and in the first or second year of their course study (63.12%). A series of multiple linear regressions revealed that being female, experiencing more stress and anxiety, and greater worry about COVID-19 and its effects on social relationships were significant in explaining students' pandemic-related anxiety. Students' level of pandemic-related anxiety was independent of ethnicity, socioeconomic background and pre-existing mental health problems. Worrying about the future was the most common pandemic-related stressor, but it was not a significant predictor of COVID-19 anxiety. Our findings are in line with previous findings that females and students experiencing more stress have been disproportionately affected by the COVID-19 pandemic. Our findings could inform the delivery of targeted stress-management interventions that might prove beneficial for student wellbeing.

Ayurvedic and Other Herbal Remedies for Dengue: An Update

Dengue fever is a flu-like ailment propagated by female mosquitos of the Aedes aegypti species. It is also known as dandaka jwara in Ayurveda. It is most common in the world's subtropical and tropical climate zones. Vomiting, severe headache, nausea, rashes, joint pain, pain behind the eyes, muscle pain, and swollen glands are all common dengue symptoms. If not handled promptly, these symptoms can lead to more severe issues such as exhaustion, blood in the vomit, continuous vomiting, bleeding gums, restlessness, severe abdominal pain, and rapid bleeding. Because there is no specific medication for dengue fever, the disease is treated by eliminating and managing the symptoms. Fortunately, there are a variety of ayurvedic remedies (like Carica papaya L., Cissampelos pareira L., etc.) that can help to tackle the same by strengthening the immune system and controlling hyperthermia. This review article provides a comprehensive overview of dengue virus infections, clinical symptoms, diagnosis, mitigation, and treatments, focusing on ayurvedic and herbal remedies.Copyright © 2022 The Author(s)

Exponential Growth Bias in the Prediction of COVID-19 Spread and Economic Expectation

Exponential growth bias (EGB) is the pervasive tendency of people to perceive a growth process as linear when in fact it is exponential. We document that people exhibit EGB when asked to predict the number of COVID-19 positive cases in the future. Using four experimental interventions, we examine the effect of EGB on expectations about future macroeconomic conditions, and investment choices in risky assets. In the first intervention (Step), participants make predictions in several short steps;in the second and third treatments (Feedback-N and Feedback-G), participants are given feedback about their prediction errors in the form of either numbers or graphs;and in the fourth treatment (Forecast), participants are offered a forecast range of the future number of cases, based on a statistical model. We find that Feedback-N, Feedback-G and Forecast significantly reduce EGB relative to Step. A reduction in the bias, through the interventions, also decreases risky investment and helps to moderate future economic expectations. The results suggest that nudges, such as behaviourally informed communication strategies, that correct EGB can also help to rationalize economic expectations. © 2023 London School of Economics and Political Science.

Seeing Should Probably Not Be Believing: The Role of Deceptive Support in COVID-19 Misinformation on Twitter

With the spread of the SARS-CoV-2, enormous amounts of information about the pandemic are disseminated through social media platforms such as Twitter. Social media posts often leverage the trust readers have in prestigious news agencies and cite news articles as a way of gaining credibility. Nevertheless, it is not always the case that the cited article supports the claim made in the social media post. We present a cross-genre ad hoc pipeline to identify whether the information in a Twitter post (i.e., a "Tweet") is indeed supported by the cited news article. Our approach is empirically based on a corpus of over 46.86 million Tweets and is divided into two tasks: (i) development of models to detect Tweets containing claim and worth to be fact-checked and (ii) verifying whether the claims made in a Tweet are supported by the newswire article it cites. Unlike previous studies that detect unsubstantiated information by post hoc analysis of the patterns of propagation, we seek to identify reliable support (or the lack of it) before the misinformation begins to spread. We discover that nearly half of the Tweets (43.4%) are not factual and hence not worth checking - a significant filter, given the sheer volume of social media posts on a platform such as Twitter. Moreover, we find that among the Tweets that contain a seemingly factual claim while citing a news article as supporting evidence, at least 1% are not actually supported by the cited news and are hence misleading. © 2022 Association for Computing Machinery.

Effect of ceiling fan in mitigating exposure to airborne pathogens and COVID-19

Ceiling fans are the ubiquitously used electrical appliance in indoor spaces that affect the local airflow pattern and, consequently, transmission of airborne pathogens and respiratory droplets. This study numerically investigated the effect of airflow induced by the ceiling fan and ventilation rate on aerosol distribution to mitigate exposure to airborne pathogens and COVID-19. A full-scale room with a ceiling fan, natural ventilation and an occupant was modelled through transient computational fluid-particle dynamics (CFPD). To analyze the relationship between the ceiling fan rotation speed and the aerosol distribution, a ceiling fan was operated with 160, 265 and 365 revolutions per minute (RPM). The effect of the ceiling fan on particles was analyzed for particles of different sizes. The increasing ceiling fan rotation speed, the percentage deposition of the aerosol particles with diameters >40 μm was increased. The effect of different ventilation rates on aerosol distribution was evaluated. The increased ventilation rate, the percentage of the total aerosol particles flushed out was increased. The effectiveness of the mask in mitigating the exposure risk of airborne pathogens was also investigated. In combination with the natural ventilation and mask, the ceiling fan was demonstrated to have the potential to reduce airborne pathogen transmission in indoor spaces.

Does Canada's Express Entry System Meet the Challenges of the Labor Market?

While most immigrant-receiving countries have restricted immigration during the COVID-19 pandemic, Canada has taken a very different approach, increasing its immigration targets over the next three years. With this move, Canada has made it clear that immigrants are expected to play a lead role in the post-pandemic economic recovery. Most of these immigrants will be admitted through Express Entry, a hybrid system introduced in 2015 that combines elements of both supply- and demand-driven selection. In this article, we examine whether Canada's hybrid selection system meets its current and future labor market needs in a post-pandemic world. Using a mixed methods approach, we combine analysis of an administrative dataset with data from qualitative interviews with employers and experts in the immigration sector. We find that while Express Entry is a flexible and responsive tool for selecting immigrants, it narrows the occupational profiles of newcomers. The system also does not fully address the labor shortages and skills gaps described by Canadian employers. We argue that Canada's post-pandemic labor market will require a wider range of skills, and that Express Entry's adaptability should be carefully monitored and adjusted to broaden the skill mix of immigrants and facilitate skills match between immigrants and employers. © 2023 The Author(s). Published with license by Taylor & Francis Group, LLC.

Designerly Tele-Experiences: A New Approach to Remote Yet Still Situated Co-Design

The COVID-19 pandemic disrupted processes interaction designers took for granted, challenging some of our most commonplace design practices. Participatory and situated approaches have been impacted the most: where we engaged stakeholders in-person and in-context, during this time we must co-design remotely and in virtual environments. Such a dramatic change calls for new co-design methods. In this article, we present a novel remote strategy for involving stakeholders to co-design interactive technology: Designerly Tele-Experiences (DTE). Our methodological proposal enables participants to experience early design concepts in-the-wild as a provocation to contribute new ideas that resonate with their experiential preferences. Here we describe the rationale for DTE, unpack how it builds on and extends existing methods, and provide actionable guidelines from our experience of using it in our work. Our contribution will empower interaction designers to embrace participatory and situated approaches even when engaging stakeholders in person is not possible or desirable.

Safety, Tolerability, and Viral Pharmacodynamics of the IgG Monoclonal Antibody Sotrovimab Administered via Intramuscular Injection for the Treatment of Early Mild-to-Moderate COVID-19

Background. There is a continued need for therapeutics for the treatment of COVID-19, including intramuscular (IM) agents, which will enable broader use across a variety of healthcare delivery settings. Methods. COMET-PEAK (NCT04779879) is a 3-part study evaluating the safety, tolerability, pharmacokinetics (Part A), and viral pharmacodynamics (PD) of sotrovimab as treatment in adults >= 18 years with early mild/moderate COVID-19. In Parts B and C, the safety, tolerability and viral PD of sotrovimab administered as a 500 mg intravenous (IV) infusion or as a 500 mg or 250 mg IM injection, respectively, was evaluated. The primary objective for Parts B and C was to compare the virologic response of sotrovimab IM to IV, with an endpoint of mean area under the curve (AUC) of SARS-CoV-2 viral load as measured by qRT-PCR from Day 1 to Day 8 (AUCD1-8) in nasopharyngeal swabs and predefined 90% confidence interval (CI) limits of 0.5-2.0 indicating equivalence. Results. A total of 167 and 157 participants were enrolled in Part B and C, respectively, from February-July 2021. The median age of participants was 47 and 42 years in Part B and C, respectively, and ~50% had >= 1 risk factor for progression to severe disease. The viral load at baseline and through Day 29 of follow-up for each arm is shown in Table 1 and Figure 1. The primary objective was met for both study parts: the ratio of the least square geometric mean viral load AUC(D1-8) of sotrovimab IM vs IV was 1.04 (90% CI, 0.98, 1.09) and 1.02 (90% CI, 0.94, 1.11), for Part B and C, respectively. Through Day 29 of follow-up, the most common adverse event was injection site reactions (ISRs) in the IM arms. A total of 10 (12%) participants in the 500 mg IM group and 4 (5%) participants in the 250 mg IM group experienced an ISR, all Grade 1. Serious adverse events were uncommon, and related to COVID-19 progression, including one death in the 250 mg IM arm (Table 2). ISRs aside, there were few treatment-related AEs (2/84 IV, 1/82 IM) in Part B, none serious. Conclusion. IM administration of sotrovimab 500 mg and 250 mg each demonstrated equivalence to 500 mg sotrovimab IV in viral load assessments. Overall, there were no treatment-related serious AEs and sotrovimab was well tolerated. An 500 mg IM formulation will allow for expanded treatment potential with sotrovimab.

Antibiotic Overuse Among Children with COVID-19 Hospitalized in a Pediatric Intensive Care Unit

Background. Antibiotic overuse has been well described among hospitalized adults with COVID-19 but similar evaluations in children are lacking. We sought to quantify bacterial infection rates and antibiotic utilization among critically ill children hospitalized with COVID-19 to identify opportunities to optimize care. Methods. We performed a single center retrospective cohort study of all children hospitalized with symptomatic COVID-19 in a pediatric intensive care unit between May 16, 2020 and February 11, 2022 at a tertiary care children's hospital in the Southeastern U.S. We performed medical record review to demographic and clinical characteristics. This study was approved by the institutional IRB with a waiver of consent. Results. During the study period there were 92 subjects hospitalized in the intensive care unit with COVID-19. Demographic and clinical characteristics of the cohort are summarized in the Table. Median age was 12.4 years, median length of stay was 6 days, 32% of subjects required mechanical ventilation and 5% died. The vast majority of children had one or more comorbidities and only 1 subject was fully vaccinated against SARS-CoV-2. Thirteen (14%) subjects had bacterial growth from any clinical specimen. Eight subjects had respiratory cultures that may have represented airway colonization;when these were excluded, 5 (5%) subjects had either urinary tract or bloodstream infections. Two of the bloodstream infections were caused by drug-resistant organisms and were hospital-acquired. Despite the low number of subjects with bacterial infections, 45% received antibiotics for >3 days. The antibiotic days of therapy per subject varied widely and ranged from 0 to 61 days. Conclusion. In this cohort of nearly 100 critically ill children with COVID-19, the rate of culture-confirmed bacterial infection ranged from 5-14% yet nearly half of patients received antibiotics. Limitations include the single center and retrospective study design and the fact that bacterial pneumonia may not be cultureconfirmed. Despite these limitations, this work suggests that children with COVID-19 rarely have bacterial co-infections and are often prescribed unnecessary antibiotics.

Cardiac impairment in Long Covid 1-year post SARS-CoV-2 infection

Background: Long Covid is associated with multiple symptoms and impairment in multiple organs [1]. Cardiac impairment has been reported to varying degrees by varying methodologies in cross-sectional studies. Using cardiovascular magnetic resonance (CMR), we investigated the 12-month trajectory of cardiac impairment in individuals with Long Covid. Purpose: We conducted a prospective, longitudinal, 1-year study in individuals with Long Covid to investigate 1) the characteristics and trajectory of cardiac impairment;2) the impact of acute hospitalisation for COVID-19 on cardiac impairment;and 3) pathways for designing and improving clinical management for individuals at risk of cardiac impairment. Methods: 534 individuals with Long Covid underwent baseline CMR (quantitative T1 and T2 mapping, cardiac mass, volumes, function, and strain) and multi-organ MRI at 6 months (IQR 4.3,7.3) since first post-COVID-19 symptoms. If abnormal findings were reported at baseline, individuals were rescanned at 12.6 months (IQR 11.4, 14.2) (n=330). Symptoms, standardised questionnaires, and blood samples were collected at both timepoints (Figure 1). Cardiac impairment was defined as one or more of: low left or right ventricular ejection fraction (LVEF and RVEF), high left or right ventricular end diastolic volume (LVEDV and RVEDV), impaired left ventricular global longitudinal strain (GLS), or elevated native T1 in ≥3 cardiac independent AHA segments. A significant change over time was reported by comparison with 92 healthy controls. Results: The technical success of this multiorgan assessment in a non-acute setting was 99.1% at baseline, and 98.3% at follow-up, with 99.6% and 98.8% for CMR, respectively. Of individuals with Long Covid, 19% had cardiac impairment at baseline;70% had complete paired data at 12 months. Of those with paired data, 58% presented with ongoing cardiac impairment at 12 months. High sensitivity cardiac troponin I and B-type natriuretic peptide were not predictive of CMR findings, symptoms, or clinical outcomes. At baseline, low LVEF, high RVEDV and reduced GLS were associated with cardiac impairment;however, while elevated T1 was associated with less symptom severity at 12 months, individuals with low LVEF at baseline were associated with ongoing cardiac impairment 1 year post-infection (Figure 2). Conclusion: Cardiac impairment, other than myocarditis, is present in 1 in 5 individuals with Long Covid at 6 months, persisting in over half of those at 12 months. Cardiac-related blood biomarkers are unable to identify cardiac impairment in Long COVID. Subtypes of disease (based on symptoms, examination, and investigations) and predictive biomarkers are yet to be established. Interventional trials with pre-specified subgroup analyses are required to inform therapeutic options. Funding Acknowledgement: Type of funding sources: Public grant(s) – EU funding. Main funding source(s): Radical (EU) and Innovative UK (UK national) (and others)Figure 1Figure 2

Booster Dose for Tackling Emerging Variant of SARS-CoV-2

A novel species of coronavirus has engulfed the entire world. Its severity and rate at which it transmits have left no country untouched. Massive replication has brought mutation in the genomic sequence of the virus. Due to this, many newer variants of SARS-COV-2 have come into play. Many therapies are available for covid 19, such as Remdesivir, Baricitinib, Molnupiravir, etc., but none are effective at preventing SARS-CoV-2 infection. Even most of the efficacious vaccines against the earlier variants are now inefficacious against, the newer variants. So, the people already vaccinated with the primary course of vaccination are at risk of reinfection and symptomatic COVID 19 illness. Furthermore, the initial immune response produced by these vaccines may have diminished with time, paving the pathway for discussion on the absolute need for time off and booster doses for vaccinated people. Some developed countries like the U.K and Israel favor the booster dose strategy, while some defy it, claiming it is necessary to vaccinate unvaccinated people first rather than giving vaccines multiple times. In this article, we have explained the necessity of booster doses in tackling newer variants. However, for the time being, devising a variant-specific vaccine seems promising to hiatus this transmission.

DISTINCT PHENOTYPES OF MULTI-ORGAN ABNORMALITIES IN LONG COVID AND TYPE 2 DIABETES IDENTIFIED BY MULTIPARAMETRIC MAGNETIC RESONANCE IMAGING

Background and Aims: Long COVID is a syndrome affecting patients infected with SARSCoV- 2 who show a broad spectrum of symptoms, such as fatigue and dyspnoea, persisting for several months after the initial infection. Patients with type 2 diabetes (T2D) have frequent co-morbidities affecting multiple organs. Both long COVID and T2D are multi-system conditions associated with abnormalities in organ structure and function. Therefore, multi-organ assessment is critical for monitoring organ health and early detection of co-morbidities in both conditions. This study aimed to define the prevalence of multi-organ abnormalities in patients with long COVID and patients with T2D with a rapid, non-contrast, magnetic resonance imaging (MRI) scan. Methods: Overall, 135 long COVID patients without diabetes were recruited in the COVERSCAN study (NCT04369807) at a median of 183 days since first COVID-19 symptoms. 135 patients with established T2D were recruited in the MODIFY study (NCT04114682). MRI data were acquired to derive abdominal organ-specific measures of size, fat deposition and fibroinflammation (CoverScan®, Perspectum Ltd.). Reference values of MRI metrics were based on 92 healthy volunteers and published literature. The prevalence of abnormalities for each measure was assessed using Fisher's exact tests in the whole cohort and in a subset of patients with obesity (BMI≥30 kg/m2). Results: The demographics of the three cohorts were as follows: long COVID: median age 54 yrs [interquartile interval 46–60], 56% male, BMI 27 kg/m2 [26–31];T2D: 62 yrs [54–70], 59% male, BMI 32 kg/m2 [28–35], median T2D duration 11 yrs;healthy volunteers: 44 yrs [32–53], 66% male, BMI 23 kg/m2 [21–25]). There was a high prevalence of abdominal organ abnormality in both long COVID and T2D patient groups (Figure, left), including increased fat deposition (steatosis) in the liver, pancreas, and kidney (Figure, right). 35% of patients with T2D had clustering of abnormalities involving at least 2 organs, compared to 23% in long COVID. Abnormalities affecting the liver (steatosis, fibroinflammation and hepatomegaly) and renomegaly were more common in T2D than in long COVID (p<0.001). Considering only patients with obesity, liver fibroinflammation, hepatomegaly, and renomegaly remained significantly more prevalent in T2D than in long COVID (p<0.05). Conclusion: Long COVID and established T2D have distinct profiles of multi-organ impairment, more prevalent with obesity but not fully explained by it. Multi-organ MRI assessment can enrich the current blunt assessment of multi-system/multi-organ abnormalities in diverse disease states to inform earlier intervention and treatments. (Figure Presented) Left: Prevalence (%) of organ abnormalities in long COVID patients (black numbers) and T2D patients (grey numbers) (liver, pink;pancreas, yellow;kidney, green;spleen, blue). Right: Prevalence of organ steatosis, fibroinflammation and increased organ size by patient group. Colour per organ as in left figure.

ECTOPIC FAT DEPOSITION IN THE LIVER AND PANCREAS AND THE PRESENCE OF EXCESS VISCERAL ADIPOSE TISSUE SIGNIFICANTLY INCREASE HOSPITALISATION RISK FOLLOWING COVID-19

Background and Aims: In patients with COVID-19, obesity may increase risk of hospitalisation, use of mechanical ventilation and patient mortality. High liver fat, body mass index (BMI) and male sex are significant predictors of hospitalisation risk following COVID-19. However, BMI is a poor indicator of body fat distribution. Here, we studied ectopic fat accumulation within the liver and pancreas and body composition through multiparametric magnetic resonance (mpMR) and compared participants with and without hospitalisation for COVID-19. Method: Participants with laboratory-confirmed or clinically suspected SARSCoV- 2 infection were recruited to the COVERSCAN study (NCT04369807;median time from initial symptoms = 177 days) and underwent a multi-organ mpMR scan (CoverScan®, Perspectum Ltd). Measures of liver and pancreatic fat (PDFF), liver fibroinflammation (cT1) and body composition [visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), skeletal muscle index (SMI)] were analysed. Differences between participants hospitalised (n = 59) and not hospitalised (n = 348) for COVID-19 were assessed using Wilcoxon signedrank tests. Univariate and multivariate analyses were performed on all biomarkers to assess the hospitalisation risk. Data presented are median values. Results: Approximately 6-months after initial symptoms, participants hospitalised following COVID-19 had significantly elevated pancreatic fat (3.8 % vs 2.8 %, p < 0.01), liver fat (3.8 % vs 2.4 %, p < 0.01) and liver cT1 (735ms vs 706ms, p < 0.01) compared to those who convalesced at home. Though hospitalised participants had a significantly elevated BMI (27 kg/m2 vs 25 kg/m2, p = 0.014), it was VAT, but not SAT, that was significantly elevated (132 cm2 vs 86 cm2, p < 0.01). Univariate analysis revealed that male sex, advanced age and elevated BMI, VAT, pancreatic fat, liver fat, and liver cT1 were all significantly predictive of hospitalisation following COVID- 19. In multivariate analysis, only age remained significantly predictive of hospitalisation. In hospitalised people with obesity (³ 30 kg/m2), VAT, liver cT1 and liver fat, but not BMI nor pancreatic fat, remained significantly elevated [VAT: 220 cm2 vs 152cm2, p = 0.01 (Figure 1);liver fat: 9.9 % vs 4.2 %, p = 0.003;liver cT1: 782ms vs 742ms, p = 0.012]. Conclusion: mpMR revealed significantly elevated visceral and ectopic fat deposition within the liver and pancreas in hospitalised participants following COVID-19. In obese participants, BMI was not significantly different in hospitalised, and non-hospitalised patients, whereas visceral fat, liver fibroinflammation and liver fat were significantly elevated. Our work highlights body fat distribution an important consideration for COVID-19 risk profiling, which cannot be sufficiently evaluated based on BMI alone. (Figure Presented) Figure 1. Comparison of liver fat (left), pancreatic fat (middle) and visceral adipose tissue (right) between participants hospitalised and not hospitalised following COVID-19.