Background: The purpose of this study was to assess the efficacy of a lifestyle intervention on cardiorespiratory fitness in Latino youth with obesity and prediabetes. <br/>Methods: Participants (n=50) in this study were taken from a larger randomized controlled trial (n=180, BMI ≥ 95th percentile). Youth participated in a 6-month lifestyle intervention that included physical activity (60 minutes, 3x/week) and nutrition and wellness classes (60 minutes, 1x/week) delivered to families at the Lincoln Family YMCA in Downtown Phoenix. The primary outcome was cardiorespiratory fitness measured at baseline and post-intervention.<br/>Results: The mean BMI for the sample was 33.17 ± 4.54 kg/m2, which put the participants in the 98.4th percentile. At baseline, the mean VO2max was 2737.02 ± 488.89 mL/min. The mean relative VO2max was 30.65 ± 3.87 mL/kg/min. VO2max values significantly increased from baseline to post-intervention (2737.022 ± 483.977 mL/min vs 2932.654 ± 96.062 mL/min, p<0.001). <br/>Conclusion: Culturally-grounded, family-focused lifestyle interventions are a promising approach for improving cardiorespiratory fitness in high-risk youth at risk for diabetes.
The field of biomedical research relies on the knowledge of binding interactions between various proteins of interest to create novel molecular targets for therapeutic purposes. While many of these interactions remain a mystery, knowledge of these properties and interactions could have significant medical applications in terms of understanding cell signaling and immunological defenses. Furthermore, there is evidence that machine learning and peptide microarrays can be used to make reliable predictions of where proteins could interact with each other without the definitive knowledge of the interactions. In this case, a neural network was used to predict the unknown binding interactions of TNFR2 onto LT-ɑ and TRAF2, and PD-L1 onto CD80, based off of the binding data from a sampling of protein-peptide interactions on a microarray. The accuracy and reliability of these predictions would rely on future research to confirm the interactions of these proteins, but the knowledge from these methods and predictions could have a future impact with regards to rational and structure-based drug design.