CORRELATION OF LOWER LIMB STRENGTH, POWER, WAIST-HIP RATIO AND BMI WITH A SITTING-RISING TEST IN 18-35 YEARS AGE GROUP

Purpose: To investigate the co-relation of sitting-rising test scores with measures of lower extremity strength, power, and body compositions (body mass index and waist-hip ratio). Furthermore, to find out the level of physical activity in the targeted population. Methods: Participants aged between 18-35 years (n=131) of both genders were recruited in this study. Along with performing sitting-rising test, anthropometric and demographic data were collected to calculate body mass index and waist-hip ratio. Lower extremity strength was assessed using a 30-second chair stand test, and power was assessed using a triple hop test for distance. Also, the Rapid Assessment Disuse Index questionnaire was given to dividing the population into two groups depending on whether they are involved more in physical activity or not. Results: Sitting-rising test scores showed a positive correlation with lower extremity strength and power, negative correlation with body mass index, and no co-relation with waist-hip ratio. Further, 76 participants out of 131 were less physical activity and had more sedentary behavior. Conclusion: Sitting-rising from the floor in young adults is influenced by the strength and power of lower extremities and body mass index except for the waist-hip ratio. Keywords: Sitting-rising test; Lower extremity strength and power; RADI score

CORRELATION OF LOWER LIMB STRENGTH, POWER, WAIST-HIP RATIO AND BMI WITH A SITTING-RISING TEST IN 18-35 YEARS AGE GROUP

Purpose: To investigate the co-relation of sitting-rising test scores with measures of lower extremity strength, power, and body compositions (body mass index and waist-hip ratio). Furthermore, to find out the level of physical activity in the targeted population. Methods: Participants aged between 18-35 years (n=131) of both genders were recruited in this study. Along with performing sitting-rising test, anthropometric and demographic data were collected to calculate body mass index and waist-hip ratio. Lower extremity strength was assessed using a 30-second chair stand test, and power was assessed using a triple hop test for distance. Also, the Rapid Assessment Disuse Index questionnaire was given to dividing the population into two groups depending on whether they are involved more in physical activity or not. Results: Sitting-rising test scores showed a positive correlation with lower extremity strength and power, negative correlation with body mass index, and no co-relation with waist-hip ratio. Further, 76 participants out of 131 were less physical activity and had more sedentary behavior. Conclusion: Sitting-rising from the floor in young adults is influenced by the strength and power of lower extremities and body mass index except for the waist-hip ratio. Keywords: Sitting-rising test; Lower extremity strength and power; RADI score

Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19

IMPORTANCE Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19. Objective To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19. DESIGN, SETTING, AND PARTICIPANTS In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 non–critically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022). INTERVENTIONS Patients were randomized to receive open-label initiation of an ACE inhibitor (n = 257), ARB (n = 248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; n = 10), or no RAS inhibitor (control; n = 264) for up to 10 days. MAIN OUTCOMES AND MEASURES The primary outcome was organ support–free days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes. RESULTS On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ support–free days among critically ill patients was 10 (–1 to 16) in the ACE inhibitor group (n = 231), 8 (–1 to 17) in the ARB group (n = 217), and 12 (0 to 17) in the control group (n = 231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ support–free days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively). CONCLUSIONS AND RELEVANCE In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT0273570

Analytics for Investigation of Disease Outbreaks (AIDO)

Objective: Analytics for the Investigation of Disease Outbreaks (AIDO) is a web-based tool designed to enhance a user’s understanding of unfolding infectious disease events. A representative library of over 650 outbreaks across a wide selection of diseases allows similar outbreaks to be matched to the conditions entered by the user. These historic outbreaks contain detailed information on how the disease progressed as well as what measures were implemented to control its spread, allowing for a better understanding within the context of other outbreaks.Introduction: Situational awareness, or the understanding of elemental components of an event with respect to both time and space, is critical for public health decision-makers during an infectious disease outbreak. AIDO is a web-based tool designed to contextualize incoming infectious disease information during an unfolding event for decision-making purposes.Methods: Public health analysts of the Biology Division at Los Alamos National Laboratory curated a diverse library of historic disease outbreaks from publicly available official reports and peer reviewed literature to serve as a representation of the range of potential outbreak scenarios for a given disease. Available outbreak metadata are used to identify properties that relate to the magnitude and/or duration of the outbreak. Properties vary by disease, as they are related to disease-specific characteristics like transmission, disease manifestation, risk factors related to disease severity, and environmental factors specific to the given location. These properties are then incorporated into a similarity algorithm (s in Figure 1) to identify outbreaks that are similar to user inputs.Results: AIDO currently includes libraries for 39 diseases that are diverse across pathogen type (viral, bacterial and parasitic) as well as transmission type (vectorborne (e.g., Dengue, Malaria), foodborne (e.g., Salmonella, Campylobacteriosis), waterborne (e.g., Cholera), and person-to-person transmitted (e.g., Measles)). In addition to providing a similarity score to the user’s outbreak, we provide aggregated comparisons to multiple historical outbreaks, descriptive statistics to show the distribution of property values for each disease, and extensive contextual information about each outbreak.Conclusions: The analytics provided by AIDO allow users to interact with a unique data set of historic outbreaks and the associated metadata to contextualize incoming information and generate hypotheses about appropriate decisions. The tool is continually updated with new functionalities and additional data

Re-emerging Infectious Disease (RED) Alert tool

Objective: Although relying on verbal definitions of "re-emergence", descriptions that classify a “re-emergence” event as any significant recurrence of a disease that had previously been under public health control, and subjective interpretations of these events is currently the conventional practice, this has the potential to hinder effective public health responses. Defining re-emergence in this manner offers limited ability for ad hoc analysis of prevention and control measures and facilitates non-reproducible assessments of public health events of potentially high consequence. Re-emerging infectious disease alert (RED Alert) is a decision-support tool designed to address this issue by enhancing situational awareness by providing spatiotemporal context through disease incidence pattern analysis following an event that may represent a local (country-level) re-emergence. The tool’s analytics also provide users with the associated causes (socioeconomic indicators) related to the event, and guide hypothesis-generation regarding the global scenario.Introduction: Definitions of “re-emerging infectious diseases” typically encompass any disease occurrence that was a historic public health threat, declined dramatically, and has since presented itself again as a significant health problem. Examples include antimicrobial resistance leading to resurgence of tuberculosis, or measles re-appearing in previously protected communities. While the language of this verbal definition of “re-emergence” is sensitive enough to capture most epidemiologically relevant resurgences, its qualitative nature obfuscates the ability to quantitatively classify disease re-emergence events as such.Methods: Our tool automatically computes historic disease incidence and performs trend analyses to help elucidate events which a user may considered a true re-emergence in a subset of pertinent infectious diseases (measles, cholera, yellow fever, and dengue). The tool outputs data visualizations that illustrate incidence trends in diverse and informative ways. Additionally, we categorize location and incidence-specific indicators for re-emergence to provide users with associated indicators as well as justifications and documentation to guide users’ next steps. Additionally, the tool also houses interactive maps to facilitate global hypothesis-generation.Results: These outputs provide historic trend pattern analyses as well as contextualization of the user’s situation with similar locations. The tool also broadens users' understanding of the given situation by providing related indicators of the likely re-emergence, as well as the ability to investigate re-emergence factors of global relevance through spatial analysis and data visualization.Conclusions: The inability to categorically name a re-emergence event as such is due to lack of standardization and/or availability of reproducible, data-based evidence, and hinders timely and effective public health response and planning. While the tool will not explicitly call out a user scenario as categorically re-emergent or not, by providing users with context in both time and space, RED Alert aims to empower users with data and analytics in order to substantially enhance their contextual awareness; thus, better enabling them to formulate plans of action regarding re-emerging infectious disease threats at both the country and global level

Spatial temporal cluster analysis to enhance awareness of disease re-emergence on a global scale

ObjectiveThe application of spatial analysis to improve the awareness and use of surveillance data.IntroductionThe re-emergence of an infectious disease is dependent on social, political, behavioral, and disease-specific factors. Global disease surveillance is a requisite of early detection that facilitates coordinated interventions to these events. Novel informatics tools developed from publicly available data are constantly evolving with the incorporation of new data streams. Re-emerging Infectious Disease (RED) Alert is an open-source tool designed to help analysts develop a contextual framework when planning for future events, given what has occurred in the past. Geospatial methods assist researchers in making informed decisions by incorporating the power of place to better explain the relationships between variables.MethodsDisease incidence and indicator data derived for the RED Alert project were analyzed for spatial associations. Using aggregate country-level data, spatial and spatiotemporal clusters were identified in ArcMap 10.5.1. The identified clusters were then used as the outcome for a series of binary logistic regression models to determine significant covariates that help explain global hotspots. These methods will continue to evolve and be incorporated into the RED Alert decision support ecosystem to provide analysts with a global perspective on potential re-emergence.ResultsHotspots of high disease incidence in relation to neighboring countries were identified for measles, cholera, dengue, and yellow fever between 2000 and 2014. Disease-specific predictors were identified using aggregate estimates from World Bank indicator dataset. Data was imputed where possible to enhance the validity of the Gi * statistic for clustering. In the future, as data streams become more readily available, hotspot modeling at a finer resolution will help to improve the precision of spatial epidemiology.ConclusionsSpatial methods enhance the capability of understanding complex population and disease relationships, which in turn improves surveillance and the ability to predict re-emergence. With tools like RED Alert, public health analysts can better prepare to respond rapidly to future re-emerging disease threats. 

Abstracts of Scientifica 2022

This book contains the abstracts of the papers presented at Scientifica 2022, Organized by the Sancheti Institute College of Physiotherapy, Pune, Maharashtra, India, held on 12–13 March 2022. This conference helps bring researchers together across the globe on one platform to help benefit the young researchers. There were six invited talks from different fields of Physiotherapy and seven panel discussions including over thirty speakers across the globe which made the conference interesting due to the diversity of topics covered during the conference. Conference Title:  Scientifica 2022Conference Date: 12–13 March 2022Conference Location: Sancheti Institute College of PhysiotherapyConference Organizer: Sancheti Institute College of Physiotherapy, Pune, Maharashtra, Indi

Effect of Antiplatelet Therapy on Survival and Organ Support–Free Days in Critically Ill Patients With COVID-19

International audienc