ABSTRACT
The aim of this study was to determine if specific physical tests are sufficiently discriminant to differentiate players of similar anthropometric characteristics, but of different playing levels. Physical tests were conducted analyzing specific strength, throwing velocity, and running speed tests. Thirty-six male junior handball players (n = 36; age 19.7 ± 1.8 years; 185.6 ± 6.9 cm; 83.1 ± 10.3 kg; 10.6 ± 3.2 years of experience) from two different levels of competition participated in the study: NT = 18 were world top-level elite players, belonging to the Spanish junior men's national team (National Team = NT) and A = 18 players of the same age and anthropometric conditions, who were selected from Spanish third league men's teams (Amateur = A). The results showed significant differences (p < 0.05) between the two groups in all physical tests, except for two-step-test velocity and shoulder internal rotation. We conclude that a battery combining the Specific Performance Test and the Force Development Standing Test is useful in identifying talent and differentiating between elite and sub-elite players. The current findings suggest that running speed tests and throwing tests are essential in selecting players, regardless of age, sex, or type of competition. The results shed light on the factors that differentiate players of different levels and can help coaches in selecting players.
Subject(s)
Athletic Performance , Running , Male , Humans , Adolescent , Young Adult , Adult , Athletes , Exercise Test/methods , AnthropometryABSTRACT
Age and age-dependent inflammation are two main risk factors for cardiovascular diseases. Aging can also affect clock gene-related impairments such as chronodisruption and has been linked to a decline in melatonin synthesis and aggravation of the NF-κB/NLRP3 innate immune response known as inflammaging. The molecular drivers of these mechanisms remain unknown. This study investigated the impact of aging and NLRP3 expression on the cardiac circadian system, and the actions of melatonin as a potential therapy to restore daily rhythms by mitigating inflammaging. We analyzed the circadian expression and rhythmicity of clock genes in heart tissue of wild-type and NLRP3-knockout mice at 3, 12, and 24 months of age, with and without melatonin treatment. Our results support that aging, NLRP3 inflammasome, and melatonin affected the cardiac clock genes expression, except for Rev-erbα, which was not influenced by genotype. Aging caused small phase changes in Clock, loss of rhythmicity in Per2 and Rorα, and mesor dampening of Clock, Bmal1, and Per2. NLRP3 inflammasome influenced the acrophase of Clock, Per2, and Rorα. Melatonin restored the acrophase and the rhythm of clock genes affected by age or NLRP3 activation. The administration of melatonin re-established murine cardiac homeostasis by reversing age-associated chronodisruption. Altogether, these results highlight new findings about the effects aging and NLRP3 inflammasome have on clock genes in cardiac tissue, pointing to continuous melatonin as a promising therapy to placate inflammaging and restore circadian rhythm in heart muscle. Additionally, light microscopy analysis showed age-related morphological impairments in cardiomyocytes, which were less severe in mice lacking NLRP3. Melatonin supplementation preserved the structure of cardiac muscle fibers in all experimental groups.
Subject(s)
Inflammasomes , Melatonin , Animals , Circadian Rhythm/physiology , Inflammasomes/genetics , Inflammasomes/metabolism , Melatonin/metabolism , Melatonin/pharmacology , Melatonin/therapeutic use , Mice , Mice, Knockout , Myocytes, Cardiac/metabolism , NLR Family, Pyrin Domain-Containing 3 Protein/genetics , NLR Family, Pyrin Domain-Containing 3 Protein/metabolismABSTRACT
The increasing use of high-throughput gene expression quantification technologies over the last two decades and the fact that most of the published studies are stored in public databases has triggered an explosion of studies available through public repositories. All this information offers an invaluable resource for reuse to generate new knowledge and scientific findings. In this context, great interest has been focused on meta-analysis methods to integrate and jointly analyze different gene expression datasets. In this work, we describe the main steps in the gene expression meta-analysis, from data preparation to the state-of-the art statistical methods. We also analyze the main types of applications and problems that can be approached in gene expression meta-analysis studies and provide a comparative overview of the available software and bioinformatics tools. Moreover, a practical guide for choosing the most appropriate method in each case is also provided.
