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The Application of Olive-Derived Polyphenols on Exercise-Induced Inflammation: A Scoping Review.
Nutrients
January 2025
Cambridge Centre for Sport and Exercise Sciences (CCSES), Faculty of Science and Engineering, Anglia Ruskin University, Cambridge CB1 1PT, UK.
Background/objectives: There is current scientific interest pertaining to the therapeutic effects of olive-derived polyphenols (ODPs), in particular their associated anti-inflammatory properties, following the wealth of research surrounding the physiological impact of the Mediterranean Diet (MD). Despite this association, the majority of the current literature investigates ODPs in conjunction with metabolic diseases. There is limited research focusing on ODPs and acute inflammation following exercise, regardless of the knowledge surrounding the elevated inflammatory response during this time.
View Article and Find Full Text PDFInvisible Monitoring for Athlete Health and Performance: A Call for a Better Conceptualization and Practical Recommendations.
Int J Sports Physiol Perform
January 2025
Department of Sport and Physical Activity, Faculty of Arts and Sciences, Edge Hill University, Ormskirk, United Kingdom.
Background: Practices to routinely monitor athletes are rapidly changing. With the concurrent exponential rise in wearable technologies and advanced data analysis, tracking training exposures and responses is widespread and more frequent in the athlete-coach decision-making process. Within this scenario, the concept of invisible monitoring emerged, which was initially vaguely defined as testing athletes without testing them.
View Article and Find Full Text PDFUnraveling the Invisible: Topological Data Analysis as the New Frontier in Radiology's Diagnostic Arsenal.
Tomography
January 2025
Department of Radiology, University of Padova, 35127 Padova, Italy.
This commentary examines Topological Data Analysis (TDA) in radiology imaging, highlighting its revolutionary potential in medical image interpretation. TDA, which is grounded in mathematical topology, provides novel insights into complex, high-dimensional radiological data through persistent homology and topological features. We explore TDA's applications across medical imaging domains, including tumor characterization, cardiovascular imaging, and COVID-19 detection, where it demonstrates 15-20% improvements over traditional methods.
View Article and Find Full Text PDFRecent Status on Lactate Monitoring in Sweat Using Biosensors: Can This Approach Be an Alternative to Blood Detection?
Biosensors (Basel)
December 2024
Microsis srl, Via degli Olmetti, 8a, 00060 Formello, Italy.
Recent studies have shown that lactate is a molecule that plays an indispensable role in various physiological cellular processes, such as energy metabolism and signal transductions related to immune and inflammatory processes. For these reasons, interest in its detection using biosensors for non-invasive analyses of sweat during sports activity and in clinical reasons assessments has increased. In this minireview, an in-depth study was carried out on biosensors that exploited using electrochemical methods and innovative nanomaterials for lactate detection in sweat.
View Article and Find Full Text PDFDirected Acyclic Graphs in Decision-Analytic Modeling: Bridging Causal Inference and Effective Model Design in Medical Decision Making.
Med Decis Making
January 2025
Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands.
Our commentary proposes the application of directed acyclic graphs (DAGs) in the design of decision-analytic models, offering researchers a valuable and structured tool to enhance transparency and accuracy by bridging the gap between causal inference and model design in medical decision making.The practical examples in this article showcase the transformative effect DAGs can have on model structure, parameter selection, and the resulting conclusions on effectiveness and cost-effectiveness.This methodological article invites a broader conversation on decision-modeling choices grounded in causal assumptions.
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