Browsing by Author "Yucesoy, Can A."

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Open Access
Development and preclinical testing of a novel neurodenervant in the rat : C3 transferase mitigates botulinum toxin’s adverse effects on muscle mechanics
(2025) Kaya Keles, Cemre Su; Akdeniz Dogan, Zeynep D.; Yucesoy, Can A.
Spasticity, characterized by elevated muscle tone, is commonly managed with botulinum toxin type A (BTX-A). However, BTX-A can paradoxically increase passive muscle forces, narrow muscles’ length range of force exertion (lrange), and elevate extracellular matrix (ECM) stiffness. C3 transferase, known to inhibit myofibroblast and fascial tissue contractility, may counteract ECM stiffening. This study investigated whether combining BTX-A with C3 transferase reduces active forces without altering passive forces or lrange. Additionally, we examined the isolated effects of C3 transferase on muscle levels. Male Wistar rats received injections into the tibialis anterior (TA): Control (n = 7, saline) and C3 + BTX-A (n = 7, 2.5 µg C3 + 0.1U BTX-A). TA forces were measured one month post-injection, and isolated C3 transferase effects were assessed in separate groups (Control and C3, n = 6 each). Active forces were 43.5% lower in the C3 + BTX-A group compared to the Control group. No differences between groups in passive forces (p = 0.33) or lrange (p = 0.19) were observed. C3 transferase alone had no significant effect on relative muscle mass (p = 0.298) or collagen content (p = 0.093). Supplementing BTX-A with C3 transferase eliminates BTX-A’s adverse effects at the muscle level. C3 transferase alone causes no atrophy or collagen increase, which are key factors in BTX-A-induced ECM stiffening. This novel neurodenervant formula shows promise for advancing spasticity management.
Open Access
The use of nonnormalized surface EMG and feature inputs for LSTM-based powered ankle prosthesis control algorithm development
(2023) Keleş, Ahmet Doğukan; Türksoy, Ramazan Tarık; Yucesoy, Can A.
Advancements in instrumentation support improved powered ankle prostheses hardware development. However, control algorithms have limitations regarding number and type of sensors utilized and achieving autonomous adaptation, which is key to a natural ambulation. Surface electromyogram (sEMG) sensors are promising. With a minimized number of sEMG inputs an economic control algorithm can be developed, whereas limiting the use of lower leg muscles will provide a practical algorithm for both ankle disarticulation and transtibial amputation. To determine appropriate sensor combinations, a systematic assessment of the predictive success of variations of multiple sEMG inputs in estimating ankle position and moment has to conducted. More importantly, tackling the use of nonnormalized sEMG data in such algorithm development to overcome processing complexities in real-time is essential, but lacking. We used healthy population level walking data to (1) develop sagittal ankle position and moment predicting algorithms using nonnormalized sEMG, and (2) rank all muscle combinations based on success to determine economic and practical algorithms. Eight lower extremity muscles were studied as sEMG inputs to a long-short-term memory (LSTM) neural network architecture: tibialis anterior (TA), soleus (SO), medial gastrocnemius (MG), peroneus longus (PL), rectus femoris (RF), vastus medialis (VM), biceps femoris (BF) and gluteus maximus (GMax). Five features extracted from nonnormalized sEMG amplitudes were used: integrated EMG (IEMG), mean absolute value (MAV), Willison amplitude (WAMP), root mean square (RMS) and waveform length (WL). Muscle and feature combination variations were ranked using Pearson’s correlation coefficient (r > 0.90 indicates successful correlations), the root-mean-square error and one-dimensional statistical parametric mapping between the original data and LSTM response. The results showed that IEMG+WL yields the best feature combination performance. The best performing variation was MG + RF + VM (rposition = 0.9099 and rmoment = 0.9707) whereas, PL (rposition = 0.9001, rmoment = 0.9703) and GMax+VM (rposition = 0.9010, rmoment = 0.9718) were distinguished as the economic and practical variations, respectively. The study established for the first time the use of nonnormalized sEMG in control algorithm development for level walking.