A comparison between electromyography-driven robot and passive motion device on wrist rehabilitation for chronic stroke.

BACKGROUND: The effect of using robots to improve motor recovery has received increased attention, even though the most effective protocol remains a topic of study. OBJECTIVE: . The objective was to compare the training effects of treatments on the wrist joint of subjects with chronic stroke with an interactive rehabilitation robot and a robot with continuous passive motion. METHODS: . This study was a single-blinded randomized controlled trial with a 3-month follow-up. Twenty-seven hemiplegic subjects with chronic stroke were randomly assigned to receive 20-session wrist training with a continuous electromyography (EMG)-driven robot (interactive group, n = 15) and a passive motion device (passive group, n = 12), completed within 7 consecutive weeks. Training effects were evaluated with clinical scores by pretraining and posttraining tests (Fugl-Meyer Assessment [FMA] and Modified Ashworth Score [MAS]) and with session-by-session EMG parameters (EMG activation level and co-contraction index). RESULTS: . Significant improvements in FMA scores (shoulder/elbow and wrist/hand) were found in the interactive group (P < .05). Significant decreases in the MAS were observed in the wrist and elbow joints for the interactive group and in the wrist joint for the passive group (P < .05). These MAS changes were associated with the decrease in EMG activation level of the flexor carpi radialis and the biceps brachii for the interactive group (P < .05). The muscle coordination on wrist and elbow joints was improved in the interactive groups in the EMG co-contraction indexes across the training sessions (P < .05). CONCLUSIONS: . The interactive treatment improved muscle coordination and reduced spasticity after the training for both the wrist and elbow joints, which persisted for 3 months. The passive mode training mainly reduced the spasticity in the wrist flexor.

Deep proteome profiling of sera from never-smoked lung cancer patients.

Previous studies on the serum proteome are hampered by the huge dynamic range of concentration of different protein species. The use of Equalizer Beads coupled with a combinatorial library of ligands has been shown to allow access to many low-abundance proteins or polypeptides undetectable by classical analytical methods. This study focused on never-smoked lung cancer, which is considered to be more homogeneous and distinct from smoking-related cases both clinically and biologically. Serum samples obtained from 42 never-smoked lung cancer patients (28 patients with active untreated disease and 14 patients with tumor resected) were compared with those from 30 normal control subjects using the pioneering Equalizer Beads technology followed by subsequent analysis by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS). Eighty-five biomarkers were significantly different between lung cancer and normal control. The application of classification algorithms based on significant biomarkers achieved good accuracy of 91.7%, 80% and 87.5% in class-prediction with respect to presence or absence of disease, subsequent development of metastasis and length of survival (longer or shorter than median) respectively. Support vector machine (SVM) performed best overall. We have proved the feasibility and convenience of using the Equalizer Beads technology to study the deep proteome of the sera of lung cancer patients in a rapid and high-throughput fashion, and which enables detection of low abundance polypeptides/proteins biomarkers. Coupling with classification algorithms, the technologies will be clinically useful for diagnosis and prediction of prognosis in lung cancer.