Muscle fiber-type specific expression of UCP3-protein is reported here for the firts time, using immunofluorescence microscopy
Training-induced adaptations in muscle morphology, including their magnitude and individual variation, remain relatively unknown in elite athletes. We reported changes in rowing performance and muscle morphology during the general and competitive preparation phases in elite rowers. Nineteen female rowers completed 8 weeks of general preparation, including concurrent endurance and high-load resistance training (HLRT). Seven rowers were monitored during a subsequent 16 weeks of competitive preparation, including concurrent endurance and resistance training with additional plyometric loading (APL). Vastus lateralis muscle volume, physiological cross-sectional area (PCSA), fascicle length, and pennation angle were measured using 3D ultrasonography. Rowing ergometer power output was measured as mean power in the final 4 minutes of an incremental test. Rowing ergometer power output improved during general preparation [+2 ± 2%, effect size (ES) = 0.22, P = 0.004], while fascicle length decreased (−5 ± 8%, ES = −0.47, P = 0.020). Rowing power output further improved during competitive preparation (+5 ± 3%, ES = 0.52, P = 0.010). Here, morphological adaptations were not significant, but demonstrated large ESs for fascicle length (+13 ± 19%, ES = 0.93), medium for pennation angle (−9 ± 15%, ES = −0.71), and small for muscle volume (+8 ± 13%, ES = 0.32). Importantly, rowers showed large individual differences in their training-induced muscle adaptations. In conclusion, vastus lateralis muscles of elite female athletes are highly adaptive to specific training stimuli, and adaptations largely differ between individual athletes. Therefore, coaches are encouraged to closely monitor their athletes' individual (muscle) adaptations to better evaluate the effectiveness of their training programs and finetune them to the athlete's individual needs.
Background & aims: Accurate diagnosis of sarcopenia requires evaluation of muscle quality, which refers to the amount of fat infiltration in muscle tissue. In this study, we aim to investigate whether we can independently predict mortality risk in transcatheter aortic valve implantation (TAVI) patients, using automatic deep learning algorithms to assess muscle quality on procedural computed tomography (CT) scans. Methods: This study included 1199 patients with severe aortic stenosis who underwent transcatheter aortic valve implantation (TAVI) between January 2010 and January 2020. A procedural CT scan was performed as part of the preprocedural-TAVI evaluation, and the scans were analyzed using deep-learning-based software to automatically determine skeletal muscle density (SMD) and intermuscular adipose tissue (IMAT). The association of SMD and IMAT with all-cause mortality was analyzed using a Cox regression model, adjusted for other known mortality predictors, including muscle mass. Results: The mean age of the participants was 80 ± 7 years, 53% were female. The median observation time was 1084 days, and the overall mortality rate was 39%. We found that the lowest tertile of muscle quality, as determined by SMD, was associated with an increased risk of mortality (HR 1.40 [95%CI: 1.15–1.70], p < 0.01). Similarly, low muscle quality as defined by high IMAT in the lowest tertile was also associated with increased mortality risk (HR 1.24 [95%CI: 1.01–1.52], p = 0.04). Conclusions: Our findings suggest that deep learning-assessed low muscle quality, as indicated by fat infiltration in muscle tissue, is a practical, useful and independent predictor of mortality after TAVI.
