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1.
Supportive Care in Cancer ; 30:S23, 2022.
Article in English | EMBASE | ID: covidwho-1935793

ABSTRACT

Introduction There is a paucity of literature reflecting how the initial phases of COVID-19 and the changes to hospital processes affected referrals to cancer physiatry and inpatient cancer rehabilitation admissions. Methods A retrospective cross-sectional descriptive study was performed to evaluate inpatient hospital admissions, referrals to physiatry, and the number of patients admitted to inpatient rehabilitation and subsequent discharge disposition. There were no active interventions. Results In 2019 vs 2020, there were 10,274 vs 7,051 inpatient hospital admissions, 387 vs 337 referrals to physiatry, and 108 vs 102 rehabilitation admissions. There was an increase in referrals in 2020 (3.8% vs 4.8%, p=0.001) with no significant change to rehabilitation admissions (27.9% vs 30.3%: p= 0.485). There was an increase in hematological services referrals and a decrease in neurosurgical services in 2020 (20.4% vs 31.4%;48.2% vs 26.5%;p = 0.01). There was an increased frequency of transfer back to primary acute care service in 2020 (7.4% vs 21.8%;p = 0.01). Conclusions During the COVID-19 pandemic, there was an increase in referrals to physiatry despite a decrease in hospital admissions, suggesting the importance of rehabilitation. There was an increase in the percentage of referrals by hematological services accompanied by a decrease in neurological services, likely due to decreased elective procedures. Finally, return to primary increased, which may be reflective of increased acuity of patients.

2.
American Journal of Respiratory and Critical Care Medicine ; 205:1, 2022.
Article in English | English Web of Science | ID: covidwho-1880562
3.
1st International Conference on Technologies for Smart Green Connected Society 2021, ICTSGS 2021 ; 107:4987-4998, 2022.
Article in English | Scopus | ID: covidwho-1874787

ABSTRACT

The speed with which the COVID-19 pandemic has spread is astounding, but the global response is based on lessons learned from earlier sickness outbreaks in recent years. In a human test immunization study, solid volunteers are given a test antibody and afterward intentionally presented to the life from making the sickness check whether the antibody works or not. Nonetheless, there are significant moral contemplations that should be tended to especially for another infection like COVID-19, which perhaps not yet completely comprehend as yet it tends to figure out how to treat. It could be hard for the clinical local area and expected volunteers to appropriately appraise the possible dangers of taking an interest in a COVID-19 human test study. The investigation is completed utilizing managed AI calculations where it has been attempted to foresee the yield with greatest exactness. © The Electrochemical Society

4.
Neurographics ; 12(1):17-20, 2022.
Article in English | Scopus | ID: covidwho-1841283

ABSTRACT

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been rarely associated with neurologic complications that are more vividly described in adults. Similar literature on the pediatric population is scarce. We report multisystem inflammatory syndrome–associated cerebral microhemorrhages in a child with COVID-19 infection. © 2022, American Society of Neuroradiology. All rights reserved.

5.
2021 International Conference on Computational Performance Evaluation, ComPE 2021 ; : 722-728, 2021.
Article in English | Scopus | ID: covidwho-1831750

ABSTRACT

In Dec 2019, Coronavirus has first shown in China and since then there is a big rise in the number of these cases. On March 28, 2020, WHO (World Health Organization) tweeted and proclaimed it's an epidemic. The test kits are relatively less likely to be checked by collecting blood samples because they are easily infectious, and collecting blood samples are very time taking. But it's important to get a quick and simpler way to validate the covid-19. Lungs are getting very badly affected by the Coronavirus and it increases in lungs gradually so we have to come up with the Convolutional Neural Network (CNN). This detects Corona virus utilising X-Rays of the Chest within about few seconds. To diagnose Coronavirus from X-ray Image dataset using different Convolutional Neural Network methodologies like Mobile Net, Inception, Exception, VGG. However, the findings obtained are based on the VGG16, VGG19 model. Apply the models to the X-ray dataset this was obtained from the Kaggle source. This dataset included 100 X-ray images of the lungs(chest) of the Patients with CORONA VIRUS, and 100 X-ray images of the lungs(chest) of People who are healthy. Python language is being used to execute the COVID19 dataset and Google Collaboratory is used for coding purposes. The focus of this research is to see how successful automatically detecting COVID-19 from chest X-rays using Convolutional Neural Networks. This study shows that to detecting COVID-19, VGG16 performs better than other method. The accuracy is 96.15% using VGG16 method. The excellent achievement of these models has the potential to rapidly better the COVID-19 diagnosis performance and speed. Although, A bigger dataset of chest X-ray pictures (COVID-19 positive) are necessary while using deep transfer learning to achieve consistent, accurate and better results to detecting COVID-19 diseases. © 2021 IEEE.

6.
Embase;
Preprint in English | EMBASE | ID: ppcovidwho-327038

ABSTRACT

Background We conducted a repeat serosurvey in Delhi, India to estimate the seroprevalence of SARSCoV-2 in the general population and compare the antibody prevalence in the vaccinated and non-vaccinated groups. Methods This cross-sectional study was conducted from September 24 to October 14 2021 in 280 wards of Delhi among 27811 participants selected through a multistage sampling technique with housing settlement based stratification. The SARS-CoV-2 immunoglobulin (IgG) antibodies were screened with the VITROS® (Ortho Clinical Diagnostics, Raritan, NJ, USA) assay (90% sensitivity, 100% specificity). Results A total of 24895 (89.5%) samples were seropositive. The crude seroprevalence was 87.99% (95% CI 89.1, 89.8), weighted for age and sex was 88% (95% CI 87.6, 88.4), and after adjustment of assay performance was estimated as 97.5% (95% CI 97.0, 98.0). The weighted seroprevalence in the 11 districts ranged from 84.9% (South-West district) to 90.8% (East district) Females in all the age-groups (<18, 18-49 and ≥50) had significantly higher odds of seropositivity (p<0.001). On adjusted analysis, the odds of seroconversion in the participants vaccinated with at-least one dose of either Covid-19 vaccine (Covishield/Covaxin) was more than four times compared to the unvaccinated (aRR 4.2 (3.8, 4.6)). The seroprevalence was also comparable among the complete and partially vaccinated subgroups for both vaccines (Table 4). Most (86.8%) seropositive individuals had a SARS-CoV-2 signal/cut-off ≥4.0 except in children Conclusions We observed IgG antibodies against SARS-CoV-2 in most of the general population of Delhi with likely higher antibody titres in the vaccinated compared to the unvaccinated groups.

7.
Hepatology ; 74(SUPPL 1):335A-336A, 2021.
Article in English | EMBASE | ID: covidwho-1508754

ABSTRACT

Background: There is a prolonged RT-PCR positivity seen in COVID-19 infected patients up to 2-3 months.It is assumed that this virus is usually non-infective but there are hardly any study on the reactivation of this virus within the respiratory tract. We aim to investigate the presence of viral particles inside Extracellular vesicles (EV) and its role in underlying liver disease patients. Methods: SARS CoV2 nasal and throat swab RT-PCR positive n=64 {n=12(18.7%) chronic liver disease (CLD);n=52 (81.3%) non-liver disease} n=5 RT PCR negative subjects (HC) were studied. SARS CoV2 patients were also followed up for day(d) 7 and 14. Nasal swab [collected in viral transport media (VTM)] and plasma samples were investigated at each time point. Extracellular vesicles were isolated using differential ultracentrifugation. SARS CoV2 RNA was measured using qRT-PCR by Altona Real Star kit. Cellular origin of EV was confirmed using epithelial cells (Epcam+ CK19+ CDh1+), endothelial cells (CD31+CD45-), hepatocytes (ASGPR+) surface markers by Flow cytometry. Results: The COVID19 patients {Mean age 54±23 years;41 males} were having severity between moderate to severe. In patients with cirrhosis, the most common aetiology of liver disease was alcohol (MELD 22±8). In baseline RT-PCR positive patients, SARS-CoV2 RNA inside the EV was present in 53/64 (82%) patients with comparable viral load between VTM and EV (mean 1CT - 0.033±0.005 vs. 1CT- 0.029±0.014, p=ns). On follow-up at day 7, of the 24 patients negative for COVID19, 10 (41%) had persistence of virus in the EV (1CT - 0.028±0.004) and on day 14, 14 of 40 (35%) negative RT-PCR had EVs with SARS CoV2 RNA (1CT - 0.028±0.06). The mean viral load decreased at day7 and day14 in EV from baseline (p=0.008;0.002 respectively). The probability of detecting SARS-CoV2 in EVs in the VTM negative patients was significantly (p=0.001) greater { relative risk ratio 2.25 (95% of CI 1.08 to 4.67;p=0.02, odds ratio 28.1(95% of CI -1.27 to 619.9;p=0.03)}.SARS-CoV2 RNA otherwise undetectable in plasma, was found to be positive in EV in 12.5% of COVID19 positive patients. Interestingly, significantly prolonged and high viral load was found in EV at day 14 in CLD-COVID19 patients compared to COVID19 alone (p=0.002). The high cellular injury was seen in CLDCOVID19 infected patients with significant high levels of EV associated with epithelial cells and hepatocytes than COVID19 alone (p=0.004;0.001). Conclusion: Identification of SARS-CoV2 RNA in EV, in RT-PCR negative patients indicates persistence of infection for and likely recurrence of the infection. It is suggestive of another route of transmission as EV harbour SARS CoV2 RNA. EV associated RNA may determine the ongoing inflammation and clinical course of subjects with undetectable SARS-CoV2 virus and this may also have relevance in management of chronic liver disease patients.

8.
American Journal of the Medical Sciences ; 361(6):725-730, 2021.
Article in English | Web of Science | ID: covidwho-1323584

ABSTRACT

Background: Coronavirus disease-19 (COVID-19) infection is associated with an uncontrolled systemic inflammatory response. Statins, given their anti-inflammatory properties, may reduce the associated morbidity and mortality. This study aimed to determine the association between statin use prior to hospitalization and in-hospital mortality in COVID-19 patients. Methods: In this retrospective study, clinical data were collected from the electronic medical records of patients admitted to the hospital with confirmed COVID-19 infection from March 1, 2020 to April 24, 2020. A multivariate regression analysis was performed to study the association of pre-admission statin use with in-hospital mortality. Results: Of 255 patients, 116 (45.5%) patients were on statins prior to admission and 139 (54.5%) were not. The statin group had a higher proportion of end stage renal disease (ESRD) (13.8% vs. 2.9%, p = 0.001), diabetes mellitus (63.8% vs. 35.2%, p<0.001), hypertension (87.9% vs. 61.1%, p < 0.001) and coronary artery disease (CAD) (33.6% vs. 5%, p < 0.001). On multivariate analysis, we found a statistically significant decrease in the odds of in-hospital mortality in patients on statins before admission (OR 0.14, 95% CI 0.03-0.61, p = 0.008). In the subgroup analysis, statins were associated with a decrease in mortality in those with CAD (OR 0.02, 95% CI 0.0003-;0.92 p = 0.045) and those without CAD (OR 0.05, 95% CI 0.005-0.43, p = 0.007). Conclusions: Our study suggests that statins are associated with reduced in-hospital mortality among patients with COVID-19, regardless of CAD status. More comprehensive epidemiological and molecular studies are needed to establish the role of statins in COVID-19.

9.
American Journal of Infectious Diseases ; 16(4):135-170, 2020.
Article in English | EMBASE | ID: covidwho-958286

ABSTRACT

Background: In India, the novel Coronavirus Disease (COVID-19) epidemic has grown to 17,00000 cases and around 38,000 deaths up to 30th July, 2020. The impacts of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) pandemic in India were studied using modified age-structured stochastic and deterministic mathematical models. Methods: A compartmental susceptible (S)-infected (symptomatic) (IS) infected (asymptomatic) (IN) recovered (R) i.e., SISINR model is developed, in which the flow of individuals through compartments is modeled using a set of differential equations. The outbreak of the novel COVID-19 pandemic is critically evaluated from all major angles using base, education, vaccination and education and vaccination models based on the modified SISINR transmission network model for their simulations in MATLAB and peak of infected cases (both symptomatic and asymptomatic cases) as well as end of pandemic is predicted in each case. Result: The numerical investigations are done for both stochastic and deterministic studies using the modified SISINR transmission network model for effective prediction about the transmission of SARS-CoV-2 pandemic in India. The progress of the novel COVID-19 pandemic in India is estimated for various scenarios by varying the basic reproduction numbers from mean to extremes (general assumptions and strategies are inculcated through contact tracing based on the values of contact ratio operated for the basic reproduction numbers, 'R0'). The efficacy and potential of the education programs and vaccination programs were established with the published datasets through a validation studies. Furthermore, the outbreak of the novel COVID-19 pandemic is predicted for majorly affected cities in India on the basis of different reproduction numbers 'R0'. Conclusion: The report presented herewith could be referred to revise the government policies to (a) implement mitigatory measures such as the practice of social distancing, partial city lockdown across the nation, etc., (b) implement a 100% daily number of tests to the susceptible population of the nation, (c) improve hospital facilities and the novel COVID-19 wards, (d) improve the recovery rate with the effective implementation of base, education, vaccination and education and vaccination model to attain the equilibrium stage of pandemic at the earliest and (e) meet the objective of preventing the outbreak of the novel COVID-19 pandemic through the effective implementation of control and prevention strategies across the nation.

10.
Chest ; 158(4):A1092, 2020.
Article in English | EMBASE | ID: covidwho-860877

ABSTRACT

SESSION TITLE: Fellows Diffuse Lung Disease Posters SESSION TYPE: Fellow Case Report Posters PRESENTED ON: October 18-21, 2020 INTRODUCTION: Nonspecific interstitial pneumonia (NSIP) is the second most common cause of idiopathic interstitial pneumonias. Even though ground glass capacities (GGOs) are thought to be the predominant feature of NSIP with high sensitivity, this disease has a varied presentation. Hence, reliance on imaging solely for its diagnosis can be erroneous. Here we present a case of cellular NSIP with atypical radiological findings. CASE PRESENTATION: A 61-year-old female with history of chronic kidney disease presented with cough with productive sputum and dyspnea. She had a similar admission two months ago with hypoxic respiratory failure requiring oxygen with high flow nasal cannula. CT chest showed multifocal airspace consolidations bilaterally. She was initially treated with antibiotics with no significant improvement following which steroids were added for severe pneumonia resulting in rapid improvement of her symptoms. Hence, she was thought to have a steroid responsive lung disease such as acute eosinophilic pneumonia or organizing pneumonia and was discharged on a tapering dose of steroids with a plan for outpatient follow-up. However, patient did not follow up and presented with recurring symptoms two weeks after discontinuing the steroids. CT of her chest again showed multifocal new confluent airspace consolidations in bilateral lung fields with improvement of old consolidation. She underwent bronchoscopy with transbronchial cryobiopsy showing cellular NSIP. She was started on high dose steroids with subsequent improvement and was discharged on prednisone 40 mg daily. No clear etiology for NSIP could be identified. She was then started on mycophenolate as an outpatient for steroid sparing effect. Unfortunately, she returned to the hospital with a diagnosis of COVID-19 pneumonia and subsequently passed away. DISCUSSION: Lower lobe predominant GGOs with reticular abnormality and traction bronchiectasis are most common CT findings of NSIP. Patchy airspace consolidations are characteristic for eosinophilic lung disease or organizing pneumonia but is an uncommon finding in NSIP. It is essential to make a definitive diagnosis as each of these have different prognoses and treatment durations. Our case demonstrates that NSIP should be considered in the differential for patchy consolidative lung disease especially when steroid responsive and recurrent. Cryobiopsy in this case was instrumental in making this diagnosis without subjecting the patient to an an open lung biopsy. CONCLUSIONS: NSIP has variable clinical, pathological and radiological manifestations and is usually hard to differentiate from other form of diffuse lung diseases such as organizing pneumonia or HP. Reference #1: Kligerman, Seth et al (2009). Nonspecific Interstitial Pneumonia: Radiologic, Clinical, and Pathologic Considerations. Radiographics;Inc. 29. 73-87. 10.1148/rg.291085096. Reference #2: Saraya, T., Takata, S., Fujiwara, M., & Takei, H. (2013). Cellular non-specific interstitial pneumonia masquerading as congestive heart failure. BMJ case reports, 2013, bcr2013010502. https://doi.org/10.1136/bcr-2013-010502 DISCLOSURES: No relevant relationships by Sadia Benzaquen, source=Web Response No relevant relationships by Ena Gupta, source=Web Response No relevant relationships by ATUL MATTA, source=Web Response No relevant relationships by Corrado Minimo, source=Web Response

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