Improving Motor Competence of Children: The "Super Quinas" Intervention Program in Portuguese Primary Schools.

Objectives: The objective of this study was to describe the effects of an extra hour of a structured motor program on the motor competence (MC) of children 6-10 years old..

Design: The need for movement interventions to enhance MC among school-aged children has gained vital importance in the last years, given the negative secular trends reported.

Chemical Characterization and Antimicrobial Activity of Essential Oils and Nanoemulsions of and .

This study aimed to develop gel nanoemulsions (NEs) of Brazilian essential oils (EOs) from and , as well as to perform chemical characterization and investigate the antimicrobial activity of the EOs and NEs. : The main chemical compounds of EO were curzerene (34.80%) and germacrene B (11.

NF-κB-Specific Suppression in Cardiomyocytes Unveils Aging-Associated Responses in Cardiac Tissue.

: Aging is associated with structural and functional changes in the heart, including hypertrophy, fibrosis, and impaired contractility. Cellular mechanisms such as senescence, telomere shortening, and DNA damage contribute to these processes. Nuclear factor kappa B (NF-κB) has been implicated in mediating cellular responses in aging tissues, and increased NF-κB expression has been observed in the hearts of aging rodents.

Azole Resistance and Mutation in Clinical Isolates of .

We investigated the molecular mechanisms underlying azole resistance in seven isolates that caused candidemia and candiduria in Paraná, Brazil (2016-2022). Biofilm production, antifungal susceptibility testing, multilocus sequence typing, amplification and sequencing of , and quantification of , , and expression levels were performed. Notably, five isolates (71.

Synthetic data in generalizable, learning-based neuroimaging.

Synthetic data have emerged as an attractive option for developing machine-learning methods in human neuroimaging, particularly in magnetic resonance imaging (MRI)-a modality where image contrast depends enormously on acquisition hardware and parameters. This retrospective paper reviews a family of recently proposed methods, based on synthetic data, for generalizable machine learning in brain MRI analysis. Central to this framework is the concept of domain randomization, which involves training neural networks on a vastly diverse array of synthetically generated images with random contrast properties.