Effect of motor imagery and actual practice on learning professional medical skills

Case-based report of graded motor imagery experience in traumatic brachial plexus injury: The art of moving without moving

BACKGROUND

We reported a 24-year-old woman who sustained multiple upper limb injuries after a traffic accident in March 2017. She sustained a C7-T1 brachial plexus injury and radial nerve injury on the left side diagnosed in November 2017. The patient underwent radial nerve reconstruction. The patient began her comprehensive therapy program in January 2018.

PURPOSE

To describe the use of graded motor imagery (GMI) and outcomes after traumatic brachial plexus palsy. We presented changes in electromyographic (EMG) activity of target muscles during task execution and functional status following 10-session GMI therapy.

STUDY DESIGN

Case report.

METHODS

The program included 4 sessions of motor imagery and 6 sessions of a combination of motor imagery and mirror therapy.

RESULTS

The patient successfully participated in the program with reported improvements in EMG activity, functional status, emotional well-being, and body awareness.

CONCLUSIONS

GMI therapy appears to have peripheral motor effects, including altered surface EMG activity and contributes to a favorable outcome in the functional level of the affected arm. An improved emotional state and awareness of the affected hand could have a positive effect on function. Future long-term randomized controlled trials are needed to investigate the cumulative peripheral effects of treatment of graded motor imagery and the effects of variables mediating its effects on functional performance in patients with nerve injury.

Movement Representation Strategies as a Tool for Educational Innovation in Physiotherapy Students: A Randomized Single-Blind Controlled-Pilot Trial

Physiotherapy has a strictly theoretical body of knowledge, but for the most part, the physiotherapist's learning is practical. The practical part is fundamental to acquire clinical skills that the physiotherapist will later use in professional practice. The main aim of this study was to assess the effectiveness of movement representation strategies (MRS) in the improvement of manual skills of physiotherapy students as an educational innovation strategy. We randomly assigned 30 participants to an action observation practice (AOP), motor imagery practice (MIP), or sham observation (SO) group. A high velocity, low amplitude lumbar manipulation technique that is widely used in clinical physiotherapy practice was taught in one session. The primary outcomes were required time and test score. The secondary outcomes were perceived mental fatigue and perceived difficulty for learning. The outcomes were assessed preintervention and immediately after the intervention (postintervention). The main results showed that both AOP and MIP improved the total time required and the test score, as well as entailed less perceived difficulty for learning. However, both strategies showed a higher level of mental fatigue after the intervention, which was higher in the MIP group. Based on the results obtained, it seems that the application of MRS promotes greater learning of manual motor tasks in physiotherapy students and could be used as educational innovation strategies.

Synthetic peptides that form nanostructured micelles have potent antibiotic and antibiofilm activity against polymicrobial infections

The emergence of multidrug-resistant bacterial pathogens is a growing threat to global public health. Here, we report the development and characterization of a panel of nine-amino acid residue synthetic peptides that display potent antibacterial activity and the ability to disrupt preestablished microbial biofilms. The lead peptide (Peptide K6) showed bactericidal activity against Pseudomonas aeruginosa and Staphylococcus aureus in culture and in monocultures and mixed biofilms in vitro. Biophysical analysis revealed that Peptide K6 self-assembled into nanostructured micelles that correlated with its strong antibiofilm activity. When surface displayed on the outer membrane protein LamB, two copies of the Peptide K6 were highly bactericidal to Escherichia coli. Peptide K6 rapidly increased the permeability of bacterial cells, and resistance to this toxic peptide occurred less quickly than that to the potent antibiotic gentamicin. Furthermore, we found that Peptide K6 was safe and effective in clearing mixed P. aeruginosa-S. aureus biofilms in a mouse model of persistent infection. Taken together, the properties of Peptide K6 suggest that it is a promising antibiotic candidate and that design of additional short peptides that form micelles represents a worthwhile approach for the development of antimicrobial agents.

Deep and Wide Transfer Learning with Kernel Matching for Pooling Data from Electroencephalography and Psychological Questionnaires

Motor imagery (MI) promotes motor learning and encourages brain–computer interface systems that entail electroencephalogram (EEG) decoding. However, a long period of training is required to master brain rhythms’ self-regulation, resulting in users with MI inefficiency. We introduce a parameter-based approach of cross-subject transfer-learning to improve the performances of poor-performing individuals in MI-based BCI systems, pooling data from labeled EEG measurements and psychological questionnaires via kernel-embedding. To this end, a Deep and Wide neural network for MI classification is implemented to pre-train the network from the source domain. Then, the parameter layers are transferred to initialize the target network within a fine-tuning procedure to recompute the Multilayer Perceptron-based accuracy. To perform data-fusion combining categorical features with the real-valued features, we implement stepwise kernel-matching via Gaussian-embedding. Finally, the paired source–target sets are selected for evaluation purposes according to the inefficiency-based clustering by subjects to consider their influence on BCI motor skills, exploring two choosing strategies of the best-performing subjects (source space): single-subject and multiple-subjects. Validation results achieved for discriminant MI tasks demonstrate that the introduced Deep and Wide neural network presents competitive performance of accuracy even after the inclusion of questionnaire data.