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Background: Mild cognitive impairment (MCI) is becoming an emerging problem for developing countries where there is an increase in expected age. There is no specific curative therapeutic treatment available for these patients. Objective: The objective of this study was to evaluate short and long-term changes in the electroencephalogram (EEG) parameters and cognition of MCI patients with aerobic exercises. Methods: A randomized controlled trial was conducted on 40 patients which were randomly divided into two groups, "aerobic exercise treatment group (n=21)" and "no-aerobic control group (n=19)". Short-term effects of exercise were measured after single session of exercise and long-term effects were measured after an 18 sessions (6 weeks) treatment. The outcomes which were measured were, electroenphelogram paramaters (slowness and complexity of the EEG) and cognitive functions (using mini-mental state examination (MMSE), Montreal cognitive assessment (MoCA), and trail making test (TMT) A and B). Results: After one session of aerobic exercise there were significant improvements in slowness (delta waves; 0.678+0.035 vs 0.791+0.033; p=0.015) and complexity (0.601+0.051 vs 0.470+0.042; p=0.027) of the EEG in aerobic exercise treated group as compared to no-aerobic exercise group. After six weeks there were significant improvements in slowness (delta waves; 0.581+0.036 vs 0.815+0.025; p=0.005) and complexity (0.751+0.045 vs 0.533+0.046; p=0.001) of the EEG in the aerobic group as compared to no-aerobic group. Moreover, significant improvements were observed in the MMSE (p=0.032), MoCA (p=0.036), TMT-A (p=0.005) and TMT-B (p=0.007) in aerobic exercise group as compared to no-aerobic group. A c c e p t e d M a n u s c r i p t 3 Conclusion: Aerobic exercise showed improvement in cognition after short and long-term treatment in MCI subjects and can be used as potential therapeutic candidate.

The objective of this study was to investigate whether a single session of chiropractic care could increase strength in weak plantar flexor muscles in chronic stroke patients. Maximum voluntary contractions (strength) of the plantar flexors, soleus evoked V-waves (cortical drive), and H-reflexes were recorded in 12 chronic stroke patients, with plantar flexor muscle weakness, using a randomized controlled crossover design. Outcomes were assessed pre and post a chiropractic care intervention and a passive movement control. Repeated measures ANOVA was used to asses within and between group differences. Significance was set at p < 0.05. Following the chiropractic care intervention there was a significant increase in strength (F (1,11) = 14.49, p = 0.002; avg 64.2 ± 77.7%) and V-wave/Mmax ratio (F(1,11) = 9.67, p = 0.009; avg 54.0 ± 65.2%) compared to the control intervention. There was a significant strength decrease of 26.4 ± 15.5% (p = 0.001) after the control intervention. There were no other significant differences. Plantar flexor muscle strength increased in chronic stroke patients after a single session of chiropractic care. An increase in V-wave amplitude combined with no significant changes in H-reflex parameters suggests this increased strength is likely modulated at a supraspinal level. Further research is required to investigate the longer term and potential functional effects of chiropractic care in stroke recovery.

Electromyography (EMG) is a measure of electrical activity generated by the contraction of muscles. Non-invasive surface EMG (sEMG)-based pattern recognition methods have shown the potential for upper limb prosthesis control. However, it is still insufficient for natural control. Recent advancements in deep learning have shown tremendous progress in biosignal processing. Multiple architectures have been proposed yielding high accuracies (>95%) for offline analysis, yet the delay caused due to optimization of the system remains a challenge for its real-time application. From this arises a need for optimized deep learning architecture based on fine-tuned hyper-parameters. Although the chance of achieving convergence is random, however, it is important to observe that the performance gain made is significant enough to justify extra computation. In this study, the convolutional neural network (CNN) was implemented to decode hand gestures from the sEMG data recorded from 18 subjects to investigate the effect of hyper-parameters on each hand gesture. Results showed that the learning rate set to either 0.0001 or 0.001 with 80-100 epochs significantly outperformed (p < 0.05) other considerations. In addition, it was observed that regardless of network configuration some motions (close hand, flex hand, extend the hand and fine grip) performed better (83.7% ± 13.5%, 71.2% ± 20.2%, 82.6% ± 13.9% and 74.6% ± 15%, respectively) throughout the course of study. So, a robust and stable myoelectric control can be designed on the basis of the best performing hand motions. With improved recognition and uniform gain in performance, the deep learning-based approach has the potential to be a more robust alternative to traditional machine learning algorithms.

Niclosamide (NCS) is an oral anthelminthic drug having low solubility and hence low bioavailability. Current investigation shows an approach to fabricate solid lipid nanoparticles (SLNs) of NCS and evaluated for pharmaceutical, in vitro and in vivo characterization. NFM-3 showed particle size 204.2 ± 2.2 nm, polydispersity index 0.328 ± 0.02 and zeta potential -33.16 ± 2 mV. Entrapment efficiency and drug loading capacity were 84.4 ± 0.02% and 5.27 ± 0.03%, respectively. Scanning electron microscopy image indicated that particles were nanoranged. DSC and P-XRD results showed change in physicochemical properties of NCS. FT-IR spectra confirmed compatibility between NCS and excipients. The drug release profile showed sustained release (93.21%) of NCS in 12 h. Different kinetic models showed zero-order kinetics and Case-II transport mechanism. Study showed maximum stability at refrigerated temperature. In vivo pharmacokinetic study showed 2.15-fold increase in NCS peak plasma concentration as solid lipid nanoparticle formulation (NFM-3) compared to commercial product while relative bioavailability was 11.08. Results including in vitro and in vivo release studies of NCS confirmed that SLNs system is suitable to improve oral delivery of NCS with increased aqueous solubility, permeability and finally bioavailability.

Objectives: As for stroke rehabilitation, brain-computer interfaces could potentially be used for inducing neural plasticity in patients with cerebral palsy by pairing movement intentions with relevant somatosensory feedback. Therefore, the aim of this study was to investigate if movement intentions from children with cerebral palsy can be detected from single-trial EEG. Moreover, different feature types and electrode setups were evaluated. Approach: Eight adolescents with cerebral palsy performed self-paced dorsiflexions of the ankle while nine channels of EEG were recorded. The EEG was divided into movement intention epochs and idle epochs. The data were pre-processed and temporal, spectral and template matching features were extracted and classified using a random forest classifier. The classification accuracy of the 2-class problem was used as an estimation of the detection performance. This analysis was repeated using a single EEG channel, a Large Laplacian filtered channel and nine channels. Results: A classification accuracy of ~70% was obtained using only a single channel. This increased to ~80% for the Laplacian filtered data, while ~75% of the data were correctly classified when using nine channels. In general, the highest accuracies were obtained using temporal features or using all of them combined. Significance: The results indicate that it is possible to detect movement intentions in patients with cerebral palsy; this may be used in the development of a brain-computer interface for motor rehabilitation of patients with cerebral palsy.

While several studies have demonstrated the short-term performance of pattern recognition systems, long-term investigations are very limited. In this study, we investigated changes in classification performance over time. Ten able-bodied individuals and six amputees took part in this study. EMG signals were recorded concurrently from surface and intramuscular electrodes, with intramuscular electrodes kept in the muscles for seven days. Seven hand motions were evaluated daily using linear discriminant analysis and the classification error quantified within (WCE) and between (BCE) days. BCE was computed for all possible combinations between the days. For all subjects, surface sEMG (7.2 ± 7.6%), iEMG (11.9 ± 9.1%) and cEMG (4.6 ± 4.8%) were significantly different (P < 0.001) from each other. A regression between WCE and days (1-7) was on average not significant implying that performance may be considered similar within each day. Regression between BCE and time difference (Df) in days was significant. The slope between BCE and Df (0-6) was significantly different from zero for sEMG (R = 89%) and iEMG (R = 95%) in amputees. Results indicate that performance continuously degrades as the time difference between training and testing day increases. Furthermore, for iEMG, performance in amputees was directly proportional to the size of the residual limb.

Seven patients with penile rupture were treated surgically within a 9-month period. All were operated on as emergencies, with successful results, except in 1 case which was associated with urethral rupture. Sexual performance was satisfactory in 6 of 7 cases.