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Background: The peroneal muscles are the most effective lateral stabilisers whose tension braces the ankle joint complex against excessive supination. The purpose of this study was to identify the morphological and biomechanical effects of two machine-based shank muscle training methods.
Methods: Twenty-two healthy male recreationally active sports students performed ten weeks of single-set high resistance strength training with 3 training sessions per week. The subjects conducted subtalar pronator/supinator muscle training (ST) with the right leg by using a custom-made apparatus; the left foot muscles were exercised with machine-based talocrural plantar and dorsiflexor training (TT). Muscle strength (MVIC), muscle volume and foot biomechanics (rearfoot motion, ground reaction forces, muscle reaction times) during a sudden ankle supination were recorded before and after the intervention.
Results: Compared to TT, ST resulted in significantly higher pronator (14% vs. 8%, P<0.01) and supinator MVIC (25% vs. 12%, P<0.01). During sudden foot inversions, both ST and TT resulted in reduced supination velocity (-12%; P<0.01). The muscle reaction onset time was faster after the training in peroneus longus (PL) (P<0.01). Muscle volume of PL (P<0.01) and TA (P<0.01) increased significantly after both ST and TT.
Conclusion: After both ST and TT, the ankle joint complex is mechanically more stabilised against sudden supinations due to the muscle volume increase of PL and TA. As the reduced supination velocities indicate, the strength training effects are already present during free-fall. According to a sudden ankle supination in standing position, both machine-based dorsiflexor and pronator strength training is recommended for enhancing the mechanical stability of the ankle.
Introduction: Previous studies have established graph theoretical analysis of functional network connectivity (FNC) as a potential tool to detect neurobiological underpinnings of psychiatric disorders. Despite the promising outcomes in studies that examined FNC aberrancies in bipolar disorder (BD) and major depressive disorder (MDD), there is still a lack of research comparing both mood disorders, especially in a nondepressed state. In this study, we used graph theoretical network analysis to compare brain network properties of euthymic BD, euthymic MDD and healthy controls (HC) to evaluate whether these groups showed distinct features in FNC.
Methods: We collected resting‐state functional magnetic resonance imaging (fMRI) data from 20 BD patients, 15 patients with recurrent MDD as well as 30 age‐ and gender‐matched HC. Graph theoretical analyses were then applied to investigate functional brain networks on a global and regional network level.
Results: Global network analysis revealed a significantly higher mean global clustering coefficient in BD compared to HC. We further detected frontal, temporal and subcortical nodes in emotion regulation areas such as the limbic system and associated regions exhibiting significant differences in network integration and segregation in BD compared to MDD patients and HC. Participants with MDD and HC only differed in frontal and insular network centrality.
Conclusion: In conclusion, our findings indicate that a significantly altered brain network topology in the limbic system might be a trait marker specific to BD. Brain network analysis in these regions may therefore be used to differentiate euthymic BD not only from HC but also from patients with MDD.
Due to an increasing awareness of the potential hazardousness of air pollutants, new laws, rules and guidelines have recently been implemented globally. In this respect, numerous studies have addressed traffic-related exposure to particulate matter using stationary technology so far. By contrast, only few studies used the advanced technology of mobile exposure analysis. The Mobile Air Quality Study (MAQS) addresses the issue of air pollutant exposure by combining advanced high-granularity spatial-temporal analysis with vehicle-mounted, person-mounted and roadside sensors. The MAQS-platform will be used by international collaborators in order 1) to assess air pollutant exposure in relation to road structure, 2) to assess air pollutant exposure in relation to traffic density, 3) to assess air pollutant exposure in relation to weather conditions, 4) to compare exposure within vehicles between front and back seat (children) positions, and 5) to evaluate "traffic zone"- exposure in relation to non-"traffic zone"-exposure. Primarily, the MAQS-platform will focus on particulate matter. With the establishment of advanced mobile analysis tools, it is planed to extend the analysis to other pollutants including including NO2, SO2, nanoparticles, and ozone.