Hepatic portal venous gas : comparison of two cases

Peer reviewedPublisher PD

On the Role of Social Identity and Cohesion in Characterizing Online Social Communities

Two prevailing theories for explaining social group or community structure are cohesion and identity. The social cohesion approach posits that social groups arise out of an aggregation of individuals that have mutual interpersonal attraction as they share common characteristics. These characteristics can range from common interests to kinship ties and from social values to ethnic backgrounds. In contrast, the social identity approach posits that an individual is likely to join a group based on an intrinsic self-evaluation at a cognitive or perceptual level. In other words group members typically share an awareness of a common category membership. In this work we seek to understand the role of these two contrasting theories in explaining the behavior and stability of social communities in Twitter. A specific focal point of our work is to understand the role of these theories in disparate contexts ranging from disaster response to socio-political activism. We extract social identity and social cohesion features-of-interest for large scale datasets of five real-world events and examine the effectiveness of such features in capturing behavioral characteristics and the stability of groups. We also propose a novel measure of social group sustainability based on the divergence in group discussion. Our main findings are: 1) Sharing of social identities (especially physical location) among group members has a positive impact on group sustainability, 2) Structural cohesion (represented by high group density and low average shortest path length) is a strong indicator of group sustainability, and 3) Event characteristics play a role in shaping group sustainability, as social groups in transient events behave differently from groups in events that last longer

Serosurveillance among COVID-19 Cases in Ahmedabad Using SARS-COV2 IgG Antibodies

Background: Serosurveillance study focusing on antibodies against SARS-CoV2 among the Covid19 cases can add value in the scientific knowledge & help in formulating valid predictions regarding immunity status in the post-covid period. Objectives: To estimate seropositivity among covid19 cases and to identify various factors affecting seropositivity. Methods: During second half of October 2020, a population based serosurvey on Covid19 cases was carried out in Ahmedabad. Covid-Kavach test kits were used and estimated seroprevalence was compared with available demographic and covid19 case related parameters to identify factors affecting seropositivity in the post-covid period. Simple proportions and Z-test were used as appropriate. Results: As on October 2020, the sero-positivity among Covid19 cases in Ahmedabad was 54.51% [95% Confidence Interval (CI) 52.14-56.86%]. Females have higher positivity (54.78%) as compared to males (54.30%) but the difference was statistically not significant (Z=0.19, P=0.84). Among children and elderly, the positivity is high and from young adults to elderly the seropositivity has an increasing trend. Severity of clinical illness and longer duration of hospitalization are associated with higher seropositivity. Conclusion: With 54.51% seropositivity among covid19 cases, it is clear that all the covid19 cases may not have developed IgG antibodies, have undetectable level or might have disappeared during the post-covid period. Comparison of seropositivity with age group and clinical case details clearly suggest close correlation with the severity of clinical symptoms. The seronegative cases indicate the need for further in-depth scientific research to identify the factors affecting immunity and to uncover the reasons behind the same

Infected pancreatic necrosis: outcomes and clinical predictors of mortality. A post hoc analysis of the MANCTRA-1 international study

: The identification of high-risk patients in the early stages of infected pancreatic necrosis (IPN) is critical, because it could help the clinicians to adopt more effective management strategies. We conducted a post hoc analysis of the MANCTRA-1 international study to assess the association between clinical risk factors and mortality among adult patients with IPN. Univariable and multivariable logistic regression models were used to identify prognostic factors of mortality. We identified 247 consecutive patients with IPN hospitalised between January 2019 and December 2020. History of uncontrolled arterial hypertension (p = 0.032; 95% CI 1.135-15.882; aOR 4.245), qSOFA (p = 0.005; 95% CI 1.359-5.879; aOR 2.828), renal failure (p = 0.022; 95% CI 1.138-5.442; aOR 2.489), and haemodynamic failure (p = 0.018; 95% CI 1.184-5.978; aOR 2.661), were identified as independent predictors of mortality in IPN patients. Cholangitis (p = 0.003; 95% CI 1.598-9.930; aOR 3.983), abdominal compartment syndrome (p = 0.032; 95% CI 1.090-6.967; aOR 2.735), and gastrointestinal/intra-abdominal bleeding (p = 0.009; 95% CI 1.286-5.712; aOR 2.710) were independently associated with the risk of mortality. Upfront open surgical necrosectomy was strongly associated with the risk of mortality (p < 0.001; 95% CI 1.912-7.442; aOR 3.772), whereas endoscopic drainage of pancreatic necrosis (p = 0.018; 95% CI 0.138-0.834; aOR 0.339) and enteral nutrition (p = 0.003; 95% CI 0.143-0.716; aOR 0.320) were found as protective factors. Organ failure, acute cholangitis, and upfront open surgical necrosectomy were the most significant predictors of mortality. Our study confirmed that, even in a subgroup of particularly ill patients such as those with IPN, upfront open surgery should be avoided as much as possible. Study protocol registered in ClinicalTrials.Gov (I.D. Number NCT04747990)

A Qualitative Examination of Topical Tweet and Retweet Practices

This work contributes to the study of retweet behavior on Twitter surrounding real-world events. We analyze over a million tweets pertaining to three events, present general tweet properties in such topical datasets and qualitatively analyze the properties of the retweet behavior surrounding the most tweeted/viral content pieces. Findings include a clear relationship between sparse/dense retweet patterns and the content and type of a tweet itself; suggesting the need to study content properties in link-based diffusion models

Twitris v3: From Citizen Sensing to Analysis, Coordination and Action

What can citizen sensing with a billion-plus active users and a billion-plus posts per week, along with information in shared links to news and media objects, and use of contextually relevant Web of Data and background knowledge enable us in informing, understanding and managing a broad variety of activities and events on social media? Twitris, currently in version 3, is a scalable and interactive platform which continually collects, aggregates, integrates and analyzes above forms of data, information and knowledge to give deeper analysis and insights as well as facilitate research and development on coordination and targeted actions related to any event. In this demonstration, we will show Twitris comprehensive capabilities in spatio-temporal-thematic, people-content-network and sentiment-emotion-subjectivity analyses, with examples taken from business intelligence including brand tracking and advertising campaigns, politics and international affairs (e.g., Osama bin Ladin\u27s death, U.S. Election 2012), social/political unrests and movements (e.g., Occupy Wall Street), and disaster events (e.g., Mumbai terror attack, Hurricane Sandy). We will also demonstrate early examples of mining psycholinguistic cues which helps to correctly position our attention in the voluminous data for emergency response coordination