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      The effects of facilitatory and inhibitory kinesiotaping of Vastus Medialis on the activation and fatigue of superficial quadriceps muscles

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          Abstract

          This study aimed to investigate how facilitatory and inhibitory KT of the Vastus Medialis affected the activation and the fatigue indices of VM, Vastus Lateralis (VL) and Rectus Femoris (RF) throughout a dynamic fatigue protocol. Seventeen collegiate athletes (Ten males, seven females, age: 24.76 ± 3.99 years, height: 1.73 ± 0.10 m, mass: 68.11 ± 8.54 kg) voluntarily participated in four dynamic fatigue protocol sessions in which no-tape (control condition), inhibitory, facilitatory and sham KTs were applied to the Vastus Medialis in each session. The protocol included 100 dynamic maximum concentric knee extensions at 90°/s using an isokinetic dynamometry device. The knee extensor muscle activities were recorded using wireless surface electromyography. The average muscle activity (Root mean square) during the first three repetitions and the repetitions number of 51–100, respectively, were used to calculate the before and after exhaustion muscle activity. Furthermore, median frequency slope during all repetitions was reported as the fatigue rate of muscles during different KT conditions and for the control condition (no-tape). The results showed neither muscle activation (significance for the main effect of KT; VM = 0.82, VL = 0.72, RF = 0.19) nor fatigue rate (significance for the main effect of KT; VM = 0.11 VL = 0.71, RF = 0.53) of the superficial knee extensor muscles were affected in all four conditions. These findings suggest that the direction of KT cannot reduce, enhance muscle activity or cause changes in muscle exhaustion. Future studies should investigate the generalizability of current findings to other populations.

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          Dynamic sensorimotor interactions in locomotion.

          Locomotion results from intricate dynamic interactions between a central program and feedback mechanisms. The central program relies fundamentally on a genetically determined spinal circuitry (central pattern generator) capable of generating the basic locomotor pattern and on various descending pathways that can trigger, stop, and steer locomotion. The feedback originates from muscles and skin afferents as well as from special senses (vision, audition, vestibular) and dynamically adapts the locomotor pattern to the requirements of the environment. The dynamic interactions are ensured by modulating transmission in locomotor pathways in a state- and phase-dependent manner. For instance, proprioceptive inputs from extensors can, during stance, adjust the timing and amplitude of muscle activities of the limbs to the speed of locomotion but be silenced during the opposite phase of the cycle. Similarly, skin afferents participate predominantly in the correction of limb and foot placement during stance on uneven terrain, but skin stimuli can evoke different types of responses depending on when they occur within the step cycle. Similarly, stimulation of descending pathways may affect the locomotor pattern in only certain phases of the step cycle. Section ii reviews dynamic sensorimotor interactions mainly through spinal pathways. Section iii describes how similar sensory inputs from the spinal or supraspinal levels can modify locomotion through descending pathways. The sensorimotor interactions occur obviously at several levels of the nervous system. Section iv summarizes presynaptic, interneuronal, and motoneuronal mechanisms that are common at these various levels. Together these mechanisms contribute to the continuous dynamic adjustment of sensorimotor interactions, ensuring that the central program and feedback mechanisms are congruous during locomotion.
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            How to determine leg dominance: The agreement between self-reported and observed performance in healthy adults

            Context Since decades leg dominance is suggested to be important in rehabilitation and return to play in athletes with anterior cruciate ligament injuries. However, an ideal method to determine leg dominance in relation to task performance is still lacking. Objective To test the agreement between self-reported and observed leg dominance in bilateral mobilizing and unilateral stabilizing tasks, and to assess whether the dominant leg switches between bilateral mobilizing tasks and unilateral stabilizing tasks. Design Cross-sectional study. Participants Forty-one healthy adults: 21 men aged 36 ± 17 years old and 20 women aged 36 ±15 years old. Measurement and analysis Participants self-reported leg dominance in the Waterloo Footedness Questionnaire-Revised (WFQ-R), and leg dominance was observed during performance of four bilateral mobilizing tasks and two unilateral stabilizing tasks. Descriptive statistics and crosstabs were used to report the percentages of agreement. Results The leg used to kick a ball had 100% agreement between the self-reported and observed dominant leg for both men and women. The dominant leg in kicking a ball and standing on one leg was the same in 66.7% of the men and 85.0% of the women. The agreement with jumping with one leg was lower: 47.6% for men and 70.0% for women. Conclusions It is appropriate to ask healthy adults: “If you would shoot a ball on a target, which leg would you use to shoot the ball?” to determine leg dominance in bilateral mobilizing tasks. However, a considerable number of the participants switched the dominant leg in a unilateral stabilizing task.
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              Decomposition of surface EMG signals.

              This report describes an early version of a technique for decomposing surface electromyographic (sEMG) signals into the constituent motor unit (MU) action potential trains. A surface sensor array is used to collect four channels of differentially amplified EMG signals. The decomposition is achieved by a set of algorithms that uses a specially developed knowledge-based Artificial Intelligence framework. In the automatic mode the accuracy ranges from 75 to 91%. An Interactive Editor is used to increase the accuracy to > 97% in signal epochs of about 30-s duration. The accuracy was verified by comparing the firings of action potentials from the EMG signals detected simultaneously by the surface sensor array and by a needle sensor. We have decomposed up to six MU action potential trains from the sEMG signal detected from the orbicularis oculi, platysma, and tibialis anterior muscles. However, the yield is generally low, with typically < or = 5 MUs per contraction. Both the accuracy and the yield should increase as the algorithms are developed further. With this technique it is possible to investigate the behavior of MUs in muscles that are not easily studied by needle sensors. We found that the inverse relationship between the recruitment threshold and the firing rate previously reported for muscles innervated by spinal nerves is also present in the orbicularis oculi and the platysma, which are innervated by cranial nerves. However, these two muscles were found to have greater and more widespread values of firing rates than those of large limb muscles.
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                Author and article information

                Contributors
                letafatkaramir@yahoo.com
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                4 August 2022
                4 August 2022
                2022
                : 12
                : 13451
                Affiliations
                [1 ]GRID grid.412265.6, ISNI 0000 0004 0406 5813, Department of Biomechanics and Sports Injuries, Faculty of Physical Education and Sports Sciences, , Kharazmi University, ; Tehran, Iran
                [2 ]GRID grid.411622.2, ISNI 0000 0000 9618 7703, Department of Sports Biomechanics and Motor Control, Faculty of Physical Education and Sports Sciences, , Mazandaran University, ; Babolsar, Iran
                [3 ]GRID grid.10979.36, ISNI 0000 0001 1245 3953, Faculty of Physical Culture, , Palacký University Olomouc, ; Olomouc, Czech Republic
                [4 ]GRID grid.5611.3, ISNI 0000 0004 1763 1124, School of Physiotherapy, , University of Verona, ; Via Bengasi 4, 37134 Verona, Italy
                [5 ]GRID grid.412265.6, ISNI 0000 0004 0406 5813, Biomechanics and Corrective Exercise Laboratory, Faculty of Physical Education and Sport Sciences, , Kharazmi University, ; Mirdamad Blvd., Hesari St, Tehran, Iran
                Article
                17849
                10.1038/s41598-022-17849-x
                9352761
                35927291
                1dcabfae-dfb0-4507-920e-f453612b5cc6
                © The Author(s) 2022

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 9 March 2022
                : 2 August 2022
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                © The Author(s) 2022

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                physiology,health care
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                physiology, health care

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