Bariatric surgery on the morbidity and mortality of SARS-CoV-2 infection

Background: The COVID-19 pandemic has had a big impact around the world and as obesity is an important risk factor for the severe form of the SARS-CoV-2 disease, bariatric surgery will have a great clinical impact. Performing this surgery in people with obesity implies weight loss, which has multiple beneficial effects such as reducing the inflammatory response, increasing immunity and improving cardiopulmonary and kidney function. This review is intended to provide updated information on bariatric surgery in the morbidity and mortality of SARS-CoV-2 infection. Expecting that it provides support to the reader in order to understand the pathophysiological mechanisms involved in the better prognosis and lower probability of hospitalization and mortality from COVID-19, pending comparison of obese patients who have not undergone bariatric surgery. © 2021 Medical Body of the Almanzor Aguinaga Asenjo National Hospital. All rights reserved.

Multilingual Sentiment Analysis on Short Text Document Using Semi-Supervised Machine Learning

Sentiment analysis is a task of identifying the sentiments in text which is often applied to analyzing text in social media, customer feedbacks, and product reviews. Various studies have explored how sentiment analysis can automatically done by using machine learning techniques. However, there has been few attempts in implementing sentiment analysis on multilingual text. Furthermore, most of the existing works uses labelled data to train and develop machine learning models for sentiment analysis. Using labelled data are often expensive and time consuming. In this study, a sentiment analysis model for multilingual text using semi-supervised machine learning was explored. The data used is composed of 50,788 tweets about COVID-19, these are cleaned by removing unnecessary characters, stop words, and emojis. After cleaning, the language of each tweet was identified, all tweets that are not written in Filipino or English were removed from the dataset. Afterwards, the tweets were all translated in English in preparation for the annotation phase. This study used an open-source tool, TextBlob, in annotating the tweets. TextBlob outputs the polarity of the text in vector representation. The TextBlob annotation were then validated by human experts through an inter-rater agreement. The level of agreement between the human annotations and TextBlob annotations have a substantial agreement with 0.78 Fleiss' Kappa value. Classifier models were developed using various machine learning algorithms. Based on the results of the experiment, SVC is the best performing model with count vectorizer as feature with an accuracy, precision, recall, and F1-score of 95%. For future work, fine tuning hyperparameters to optimize the models can be considered. © 2021 ACM.

Cross-Textual Analysis of COVID-19 Tweets: On Themes and Trends Over Time

In the context of worldwide events such as natural calamities and pandemics, understanding the sentiments of the people, in general, can pave way for intervention initiatives in public health, service, and security. Likewise, Twitter provides a platform where people can publicly share their thoughts and sentiments in the form of tweets. In this paper, we realize the importance of public data found on Twitter and focus on understanding how Filipinos react as the timeline of events occur in the Philippines during the COVID-19 pandemic. We perform analyses in two main aspects: identification of prominent themes and its trend of usage over time. Results show that Filipinos (a) express positive emotions such as love, hope, and longing, (b) raise concerns over health and testing, and (c) share their experience as main themes of discussion on Twitter. Cross-referencing our results with previous work dealing with thematic analysis on other natural disasters such as earthquakes and typhoons shows that the positive and hopeful mindset is consistent amongst Filipinos. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

THE USE OF TOCILIZUMAB AT A COMMUNITY HOSPITAL DURING THE COVID-19 PANDEMIC IN THE EPICENTER OF NORTHERN NEW JERSEY

SESSION TITLE: Late-breaking Abstract Posters SESSION TYPE: Original Investigation Posters PRESENTED ON: October 18-21, 2020 PURPOSE: To evaluate the use of Tocilizumab during the COVID-19 pandemic at our community hospital, Hackensack Meridian Palisades Medical Center, and the outcome of these patients. METHODS: We conducted a retrospective analysis of records of 42 patients who received Tocilizumab 400 mg, one dose between March and May 2020, during the height of the pandemic in our area. The criteria for giving Tocilizumab included respiratory failure necessitating supplemental oxygen and elevated inflammatory markers (Ferritin, D-dimer, CRP) indicating cytokine storm as well as a positive covid PCR swab. RESULTS: Review of the 42 patients who received Tocilizumab, 13 women, 29 males;mean age: 59 years, range 31-82 years) revealed that the average BMI was 30.5 (range 20-49 BMI), 20 (48%) of patients had hypertension, 13 (31%) had Diabetes and 11 (26%) had both comorbidities. Renal failure requiring hemodialysis was present in 3 (12%) patients, 1 patient had cancer, 1 patient had leukemia, 1 patient had copd, 1 patient had cirrhosis and 1 patient developed a PE during hospitalization. The mean Charlson comorbidity index was 2.166 (range 2-8). The average days with symptoms due to the virus prior to admission was 11.5 days (range 2-25). The median hospitalization day of receiving the drug was 14.5 days (range 1-28). There were 29 (69%) patients on 100% fio2 NRB, 3 (5%) patients on 40 to 80% fio2 and 11 (26%) patients intubated upon receiving Tocilizumab. The average length of stay was 21.57 days (range 1-67 days). Tocilizumab was given twice to 6 patients between days 6 through 17 after the first dose. There were 21 (50%) patients that died and 21 (50%) patients that survived. Only one intubated patient survived. The cohort of patients that received Tocilizumab twice, only one survived. Of the 11 (26%) patients that had both diabetes and hypertension 7 (63%) expired. CONCLUSIONS: In our analysis it is apparent that Tocilizumab did not have an impact on mortality in intubated patients as only 1 patient survived. Overall the mortality was 50% in our study so in this retrospective study a significant change in outcome was not seen. Of interest, is that in our cohort of patients only one had COPD and none had asthma. The most common comorbidity seen was hypertension followed by diabetes. This may be something to further study and evaluate as a risk factor for serious COVID infection leading to death. CLINICAL IMPLICATIONS: Further and ongoing trials are needed in order to conclude whether or not Tocilizumab will have an impact on mortality during a COVID infection. DISCLOSURES: No relevant relationships by Aida Capo, source=Web Response No relevant relationships by Aida de la Cruz, source=Web Response No relevant relationships by Swati Govil, source=Web Response No relevant relationships by Mounir Ibrahim, source=Web Response No relevant relationships by Harshana Patel, source=Web Response No relevant relationships by Nishan Rajaratnam, source=Web Response