Alexa Fluor 488-conjugated cholera toxin subunit B optimally labels neurons 3-7 days after injection into the rat gastrocnemius muscle

Neural tract tracing is used to study neural pathways and evaluate neuronal regeneration following nerve injuries.However,it is not always clear which tracer should be used to yield optimal results.In this study,we examined the use of Alexa Fluor 488-conjugated cholera toxin subunit B(AF488-CTB).This was injected into the gastrocnemius muscle of rats,and it was found that motor,sensory,and sympathetic neurons were labeled in the spinal ventral horn,dorsal root ganglia,and sympathetic chain,respectively.Similar results were obtained when we injected AF594-CTB into the tibialis anterior muscle.The morphology and number of neurons were evaluated at different time points following the AF488-CTB injection.It was found that labeled motor and sensory neurons could be observed 12 hours post-injection.The intensity was found to increase over time,and the morphology appeared clear and complete 3-7 days post-injection,with clearly distinguishable motor neuron axons and dendrites.However,14 days after the injection,the quality of the images decreased and the neurons appeared blurred and incomplete.Nissl and immunohistochemical staining showed that the AF488-CTB-labeled neurons retained normal neurochemical and morphological features,and the surrounding microglia were also found to be unaltered.Overall,these results imply that the cholera toxin subunit B,whether unconjugated or conjugated with Alexa Fluor,is effective for retrograde tracing in muscular tissues and that it would also be suitable for evaluating the regeneration or degeneration of injured nerves.

Lower-Limb Motion-Based Ankle-Foot Movement Classification Using 2D-CNN

Recently,the Muscle-Computer Interface(MCI)has been extensively popular for employing Electromyography(EMG)signals to help the development of various assistive devices.However,few studies have focused on ankle foot movement classification considering EMG signals at limb position.This work proposes a new framework considering two EMG signals at a lower-limb position to classify the ankle movement characteristics based on normal walking cycles.For this purpose,we introduce a human anklefoot movement classification method using a two-dimensional-convolutional neural network(2D-CNN)with low-cost EMG sensors based on lowerlimb motion.The time-domain signals of EMG obtained from two sensors belonging to Dorsiflexion,Neutral-position,and Plantarflexion are firstly converted into time-frequency spectrograms by short-time Fourier transform.Afterward,the spectrograms of the three ankle-foot movement types are used as input to the 2D-CNN such that the EMG foot movement types are finally classified.For the evaluation phase,the proposed method is investigated using the healthy volunteer for 5-fold cross-validation,and the accuracy is used as a standard evaluation.The results demonstrate that our approach provides an average accuracy of 99.34%.This exhibits the usefulness of 2D-CNN with low-cost EMG sensors in terms of ankle-foot movement classification at limb position,which offers feasibility for walking.However,the obtained EMG signal is not directly considered at the ankle position.