Effects of motor imagery training on lower limb motor function of patients with chronic stroke: A pilot single-blind randomized controlled trial.

AIMS: This pilot study aimed to evaluate the effects of motor imagery training on lower limb motor function of stroke patients. BACKGROUND: Motor imagery training has played an important role in rehabilitation outcomes of stroke patients. METHODS: In this pilot randomized controlled trial 32 stroke patients were randomly divided into experimental and control groups from January to June 2017. Patients in both groups received conventional neuro-rehabilitation five times a week in 3-h segments for 6 weeks. Patients in the experimental group underwent an additional 20 min of motor imagery training. Measures were evaluated by motor function of the lower extremity, activities of daily living and balance ability. RESULTS: The outcomes significantly improved by motor imagery training were the Fugl-Meyer Assessment of the lower extremity, the Functional Independence Measure dealing with transfers and locomotion, and the Berg Balance Scale. CONCLUSION: Motor imagery training could be used as a complement to physical rehabilitation of stroke patients. Our findings may be helpful to develop nursing strategies aimed at improving functional ability of stroke patients and thus enhancing their quality of life.

A novel cervix carcinoma biomarker: Pathological-epigenomics, integrated analysis of MethylMix algorithm and pathology for predicting response to cancer immunotherapy.

Herein, A non-invasive pathomics approach was developed to reveal the methylation status in patients with cervical squamous cell carcinoma and predict clinical outcomes and treatment response. Using the MethylMix algorithm, 14 methylation-driven genes were selected for further analysis. We confirmed that methylation-driven genes were differentially expressed in immune, stromal, and tumor cells. In addition, we constructed a methylation-driven model and explored the alterations in immunocyte infiltration between the different models. The methylation-driven subtypes identified in our investigation could effectively predict the clinical outcomes of cervical cancer. To further evaluate the level of methylation-driven patterns, we constructed a risk model with four genes. Significant correlations were observed between the score and immune response markers, including PD1 and CTLA4. Multiple immune infiltration algorithms evaluated the level of immunocyte infiltration between the high- and low-risk groups, while the components of anti-tumor immunocytes in the low-risk group were significantly increased. Subsequently, a total of 205 acquired whole-slide imaging (WSI) images were processed to capture image signatures, and the pathological algorithm was employed to construct an image prediction model based on the risk score classification. The model achieved an area under the curve (AUC) of 0.737 and 0.582 for the training and test datasets, respectively. Moreover, we conducted vitro assays for validation of hub risk gene. The proposed prediction model is a non-invasive method that combines pathomics features and genomic profiles and shows satisfactory performance in predicting patient survival and treatment response. More interdisciplinary fields combining medicine and electronics should be explored in the future.