promor: a comprehensive R package for label-free proteomics data analysis and predictive modeling.

Summary: We present promor, a comprehensive, user-friendly R package that streamlines label-free quantification proteomics data analysis and building machine learning-based predictive models with top protein candidates. Availability and implementation: promor is freely available as an open source R package on the Comprehensive R Archive Network (CRAN) (https://CRAN.R-project.org/package=promor) and distributed under the Lesser General Public License (version 2.1 or later). Development version of promor is maintained on GitHub (https://github.com/caranathunge/promor) and additional documentation and tutorials are provided on the package website (https://caranathunge.github.io/promor/). Supplementary information: Supplementary data are available at Bioinformatics Advances online.

Five Influential Factors for Clinical Team Performance in Urgent, Emergency Care Contexts.

INTRODUCTION: In deployed contexts, military medical care is provided through the coordinated efforts of multiple interdisciplinary teams that work across and between a continuum of widely distributed role theaters. The forms these teams take, and functional demands, vary by roles of care, location, and mission requirements. Understanding the requirements for optimal performance of these teams to provide emergency, urgent, and trauma care for multiple patients simultaneously is critical. A team's collective ability to function is dependent on the clinical expertise (knowledge and skills), authority, experience, and affective management capabilities of the team members. Identifying the relative impacts of multiple performance factors on the accuracy of care provided by interdisciplinary clinical teams will inform targeted development requirements. MATERIALS AND METHODS: A regression study design determined the extent to which factors known to influence team performance impacted the effectiveness of small, six to eight people, interdisciplinary teams tasked with concurrently caring for multiple patients with urgent, emergency care needs. Linear regression analysis was used to distinguish which of the 11 identified predictors individually and collectively contributed to the clinical accuracy of team performance in simulated emergency care contexts. RESULTS: All data met the assumptions for regression analyses. Stepwise linear regression analysis of the 11 predictors on team performance yielded a model of five predictors accounting for 82.30% of the variance. The five predictors of team performance include (1) clinical skills, (2) team size, (3) authority profile, (4) clinical knowledge, and (5) familiarity with team members. The analysis of variance confirmed a significant linear relationship between team performance and the five predictors, F(5, 240) = 218.34, P < .001. CONCLUSIONS: The outcomes of this study demonstrate that the collective knowledge, skills, and abilities within an urgent, emergency care team must be developed to the extent that each team member is able to competently perform their role functions and that smaller teams benefit by being composed of clinical authorities who are familiar with each other. Ideally, smaller, forward-deployed military teams will be an expert team of individual experts, with the collective expertise and abilities required for their patients. This expertise and familiarity are advantageous for collective consideration of significant clinical details, potential alternatives for treatment, decision-making, and effective implementation of clinical skills during patient care. Identifying the most influential team performance factors narrows the focus of team development strategies to precisely what is needed for a team to optimally perform.

Toward Live Virtual Constructive Simulations in Healthcare Learning.

STATEMENT: This article explores the combination of live, virtual, and constructive (LVC) simulations in healthcare. Live, virtual, and constructive simulations have long existed in the military, but their consideration (and deployment) in medical and healthcare domains is relatively new. We conducted a review on LVC- its current application in the military domain -and highlight an approach, challenges, and present suggestions for its implementation in healthcare learning. Furthermore, based on the state of the art in simulation in healthcare, we suggest that a combination of two simulation types (LV, VC, LC) at the time may be a simpler approach to the community at large.