Relationship between motivational climate, anxiety and average mark in pre-service physical education teachers: a cross-sectional study based on structural equation modelling approach.

Background: Motivation is a variable that directly influences task orientation. Within the motivational sphere, the motivational climate determines whether a task is performed with an intrinsic or extrinsic.

Purpose: It has been observed that depending on motivational orientations, anxiety levels and task performance can be increased.

Biomarker discovery and development of prognostic prediction model using metabolomic panel in breast cancer patients: a hybrid methodology integrating machine learning and explainable artificial intelligence.

Background: Breast cancer (BC) is a significant cause of morbidity and mortality in women. Although the important role of metabolism in the molecular pathogenesis of BC is known, there is still a need for robust metabolomic biomarkers and predictive models that will enable the detection and prognosis of BC. This study aims to identify targeted metabolomic biomarker candidates based on explainable artificial intelligence (XAI) for the specific detection of BC.

Development of flexibility in primary school students.

Background: The flexibility development issue has not been studied well enough. Still, the problem can be partially solved in physical education lessons in a secondary school if a set of simple exercises for flexibility development is permanently added to the standard physical education (PE) program. The study aimed to increase flexibility in children aged 9-10 years in PE lessons.

Enhancing type 2 diabetes mellitus prediction by integrating metabolomics and tree-based boosting approaches.

Background: Type 2 diabetes mellitus (T2DM) is a global health problem characterized by insulin resistance and hyperglycemia. Early detection and accurate prediction of T2DM is crucial for effective management and prevention. This study explores the integration of machine learning (ML) and explainable artificial intelligence (XAI) approaches based on metabolomics panel data to identify biomarkers and develop predictive models for T2DM.

Evaluating bioenergetic pathway contributions from single to multiple sprints.

This study aims to investigate the changes in bioenergetic pathway contributions during repeated sprint exercises with an increasing number of repetitions. Twelve male amateur soccer players executed a single 20 m sprint and three repeated-sprint protocols (5 × 20 m, 10 × 20 m, 15 × 20 m with 15-second rest intervals), analyzing oxidative, glycolytic, and ATP-PCr energy pathways using the PCr-LA-O2 method. Findings revealed a significant decline in energy expenditure and performance outputs as the number of sprint repetitions increased.

A hybrid machine learning model combining association rule mining and classification algorithms to predict differentiated thyroid cancer recurrence.

Background: Differentiated thyroid cancer (DTC) is the most prevalent endocrine malignancy with a recurrence rate of about 20%, necessitating better predictive methods for patient management. This study aims to create a relational classification model to predict DTC recurrence by integrating clinical, pathological, and follow-up data.

Methods: The balanced dataset comprises 550 DTC samples collected over 15 years, featuring 13 clinicopathological variables.

Diurnal variation of psychomotor, cognitive and physical performances in schoolchildren: sex comparison.

Background: The present study investigated the effect of time of day (08h00 vs. 11h00 vs. 14h00 vs.

Highest oxygen consumption prediction: introducing variable theoretical proportional factors for different sports.

Purpose: The use of a fixed theoretical-proportional-factor (TPF) is one of the indirect highest-oxygen-consumptions (HOC) assessment methods, but it may not accurately reflect the physiological differences across various sports (cycling-triathlon-running-football-multisport). The aim of this study is to evaluate the variability of TPF across different sports, proposing a series of sport-specific new TPF values for more accurate HOC estimation.

Methods: A sample of 340 adults (26.

Metabolomics Biomarker Discovery to Optimize Hepatocellular Carcinoma Diagnosis: Methodology Integrating AutoML and Explainable Artificial Intelligence.

This study aims to assess the efficacy of combining automated machine learning (AutoML) and explainable artificial intelligence (XAI) in identifying metabolomic biomarkers that can differentiate between hepatocellular carcinoma (HCC) and liver cirrhosis in patients with hepatitis C virus (HCV) infection. We investigated publicly accessible data encompassing HCC patients and cirrhotic controls. The TPOT tool, which is an AutoML tool, was used to optimize the preparation of features and data, as well as to select the most suitable machine learning model.

Platelet Metabolites as Candidate Biomarkers in Sepsis Diagnosis and Management Using the Proposed Explainable Artificial Intelligence Approach.

: Sepsis is characterized by an atypical immune response to infection and is a dangerous health problem leading to significant mortality. Current diagnostic methods exhibit insufficient sensitivity and specificity and require the discovery of precise biomarkers for the early diagnosis and treatment of sepsis. Platelets, known for their hemostatic abilities, also play an important role in immunological responses.

A crossover study on attentional focus and gross motor performance in individuals with Down syndrome.

Little is known about the effect of using an attentional focus instruction on motor performance in people with intellectual disabilities. Therefore, this study explored the effects of different attentional focus instructions on gross motor skill performances in individuals with Down syndrome. Seven community-dwelling participants (age 25.

Moderate aerobic training counterbalances the deleterious effect of undernutrition on oxidative balance and mitochondrial markers.

The state of Maternal Protein Malnutrition (MPM) is associated with several deleterious effects, including inflammatory processes and dysregulation in oxidative balance, which can promote neurodegeneration. On the other hand, it is known that aerobic exercise can promote systemic health benefits, combating numerous chronic diseases. Therefore, we evaluate the effect of aerobic exercise training (AET) on indicators of mitochondrial bioenergetics, oxidative balance, endoplasmic reticulum stress, and neurotrophic factor in the prefrontal cortex of malnourished juvenile Wistar rats.

Hybrid Explainable Artificial Intelligence Models for Targeted Metabolomics Analysis of Diabetic Retinopathy.

Background: Diabetic retinopathy (DR) is a prevalent microvascular complication of diabetes mellitus, and early detection is crucial for effective management. Metabolomics profiling has emerged as a promising approach for identifying potential biomarkers associated with DR progression. This study aimed to develop a hybrid explainable artificial intelligence (XAI) model for targeted metabolomics analysis of patients with DR, utilizing a focused approach to identify specific metabolites exhibiting varying concentrations among individuals without DR (NDR), those with non-proliferative DR (NPDR), and individuals with proliferative DR (PDR) who have type 2 diabetes mellitus (T2DM).

Combining the Strengths of the Explainable Boosting Machine and Metabolomics Approaches for Biomarker Discovery in Acute Myocardial Infarction.

Acute Myocardial Infarction (AMI), a common disease that can have serious consequences, occurs when myocardial blood flow stops due to occlusion of the coronary artery. Early and accurate prediction of AMI is critical for rapid prognosis and improved patient outcomes. Metabolomics, the study of small molecules within biological systems, is an effective tool used to discover biomarkers associated with many diseases.

Anthropometric Influence on Preschool Children's Physical Fitness and Motor Skills: A Systematic Review.

Early childhood is a critical period for physical and motor development with implications for long-term health. This systematic review examined the relationship between anthropometric characteristics and measures of physical fitness and motor skills in preschool-aged children (typically 2-6 years). The search strategy was applied in four databases (PubMed, ProQuest Central, Scopus, and Web of Science) to find articles published before 11 April 2024.

Warm-up music and low-dose caffeine enhance the activity profile and psychophysiological responses during simulated combat in female taekwondo athletes.

To assess the effects of warm-up music and low dose (3 mg·kg) of caffeine (CAF) on female taekwondo athlete's activity profile and psychophysiological responses during simulated combat. In a double-blinded, randomized, crossover study, 16 female athletes participated in simulated combats under one control and 5 experimental conditions [i.e.

Integrating proteomics and explainable artificial intelligence: a comprehensive analysis of protein biomarkers for endometrial cancer diagnosis and prognosis.

Endometrial cancer, which is the most common gynaecological cancer in women after breast, colorectal and lung cancer, can be diagnosed at an early stage. The first aim of this study is to classify age, tumor grade, myometrial invasion and tumor size, which play an important role in the diagnosis and prognosis of endometrial cancer, with machine learning methods combined with explainable artificial intelligence. 20 endometrial cancer patients proteomic data obtained from tumor biopsies taken from different regions of EC tissue were used.

The contribution of energy systems during 30-second lower body Wingate anaerobic test in combat sports athletes: Intermittent versus single forms and gender comparison.

Combat sports, encompassing a range of activities from striking and grappling to mixed and weapon-based disciplines, have witnessed a surge in popularity worldwide. These sports are demanding, requiring athletes to harness energy from different metabolic pathways to perform short, high-intensity activities interspersed with periods of lower intensity. While it is established that the anaerobic alactic (ATP-PC) and anaerobic lactic systems are pivotal for high-intensity training sessions typical in combat sports, the precise contribution of these systems, particularly in varied training modalities such as single (SMT) and intermittent (IST) forms of the 30-second Wingate test, remains inadequately explored.

Effects of short-term pre-competition weight loss on certain physiological parameters and strength change in elite boxers.

Background: Athletes in certain sports aim to gain an advantage by competing in a lower body mass class instead of competing in their own body mass class. This study aims to reveal certain physiologic and strength changes in elite male boxers who lost body mass rapidly before the competition.

Methods: 30 thirty boxers who were aged between 19-24 years and having a mean age of 7.

Emotional and Social Outcomes of the Teaching Personal and Social Responsibility Model in Physical Education: A Systematic Review and Meta-Analysis.

Context: In today's ever-changing world, fostering personal and social responsibility is essential for building strong and compassionate communities. This study aimed to provide a quantitative synthesis focusing on the emotional and social outcomes of Teaching Personal and Social Responsibility (TPSR) model-based Physical Education (PE) programs.

Methods: A comprehensive literature review covering the period from November 2022 to September 2023 identified 637 articles published between 2005 and 2023.

Personality and motivation of physical activity in adolescent girls: effects of perceived parental support and social physical anxiety.

In the present study, we investigated the relationship between personality and motivation for physical activity while introducing perceived parental support and social physical anxiety in adolescent girls (N = 318, M: 16.19 ± 0.51 years).