High-intensity focused ultrasound of uterine fibroids and adenomyosis: maneuver technique for bowel loops located inside the treatment window IntroductionUterine fibroids and adenomyosis are two commonly gynecological benign tumors negatively affected women's health [1,2]. High-intensity focused ultrasound (HIFU) is a promisingly alternative treatment to conventional surgery and is increasingly worldwide used because of its completely non-invasive method [3][4][5][6]. During the ablation procedure, appearance of bowel loops inside the treatment window was generally problematic because hard elements and air bubbles inside bowel loops absorb and reflect ultrasound energy caused unpredictable thermal injury even bowel perforation [7,8]. Thus, in this concise communication, we aimed to introduce the filling bladder, filling rectum, and emptying bladder (BRB) technique for manipulating bowel loops out of the treatment Appearance of bowel loops in the sonication beam path during high-intensity focused ultrasound (HIFU) ablation therapy is a problematic condition. Filling bladder, filling rectum and, emptying bladder (BRB) maneuver technique might be helpful in producing a non-bowel treatment window for HIFU ablation of uterine fibroids and adenomyosis and ensuring the safety profile for patients.

The identification of potential predictors for motor outcome after rehabilitation helps underscore the factors that may affect treatment outcomes and target individuals who benefit the most from the therapy. In this study, we addressed and utilized a classifier to identify the potential predictors for motor performance outcome for patients with stroke after rehabilitation. The potential predictors selected and used by different assessments in this study were age, sex, time since stroke, education, neurologic status, and the movement performance of the upper extremity. This study aimed to identify predictors of motor performance outcomes after rehabilitation for stroke patients. The PSO-SVM was chosen in this study to find the predictor of motor function for clients with stroke. The potential predictors for motor outcome after rehabilitation were motor ability assessment of the Fugl-Meyer Assessment (FMA) and the Functional Independence Measure (FIM). This study is to investigate the potential demographic and clinical characteristics of stroke that can serve to predict rehabilitation outcomes in motor performance.

The aim of this study was to develop a prediction model that integrated various image features and neuropsychological scores to yield a single estimate reflecting the probability of dementia. Method:A total of 130 subjects belong to Normal control group, AD group, and MCI group, were recruited in this study. For these subjects, the multiple features obtained from different modalities, including structural MRI morphometry (volume / shape), rs-fMRI, and neuropsychological assessment measures (NPA) were used to explore an optimal set of predictors of conversion from MCI to AD. Unlike previous studies using logistic regression analysis, a new method based on learning vector quantization (LVQ) and probabilistic neural network (PNN) is proposed to establish a prediction model. Results:We test the baseline, 1-year follow-up, and 2-year follow-up scans of 17 AD subjects (M/ F=5/12), 22 normal controls (NC; 13/9), 16 subjects that remain stable MCI (MCI-s; 11/5), and 4 subjects convert to AD within a given timeframe (MCI-c; 2/2). This study found that the proposed quantitative indicator provides well-behaving AD state estimates, corresponding well with the actual diagnosis. Conclusion:According to the results, all of the test data have the trend that decreased over time. It has Neuropsychiatry (London) (2016) 6(6) 377 Research Jiann-Der Lee detection and diagnosis of .Brain atrophy typically starts in the medial temporal and limbic areas, subsequently extending to parietal association areas and finally to frontal and primary cortices. Early changes in hippocampus, amygdala, and entorhinal cortex have been demonstrated with the help of MRI and these changes are consistent with the underlying pathology of MCI and AD. Methods based on volumetric measurements [14-16], or on visual rating scales [17] have largely been used to assess cortex atrophy. Hippocampal volumes and entorhinal cortex measures have been found to be equally accurate in distinguishing between AD and normal cognitive elderly subjects [18]. However, the segmentation and identification of hippocampus or entorhinal cortex are usually time-consuming and prone to interrater and intra-rater variability. In addition, the enlargement of ventricles is also a significant characteristic of AD due to neuronal loss. Ventricles are filled with cerebro-spinal fluid (CSF) and surrounded by gray matter (GM) and white matter (WM). As a result, by measuring the ventricular enlargement, hemispheric atrophy rate shows higher correlation with the disease progression.In addition to the atrophy of brain regions, neuropsychological assessment (NPA) has featured prominently over the past 30 years in the characterization of dementia associated with Alzheimer disease (AD) [19,20]. As research has increasingly focused on earlier stages of illness, it has become clear that biological markers of AD can precede cognitive and behavioral symptoms by years, such as Mini Mental State Examination (MMSE) [21] and Clinical Dementia Rating scale (CDR) [22], the Cognitive Abilities Screening In...

In this study, a classification scheme, using the features from resting-state functional MRI (rs-fMRI) and voxel-based morphometry (VBM), was proposed to discriminate two subtypes of mild cognitive impairment (MCI): amnestic MCI (aMCI) subtypes and dys executive MCI (dMCI) subtypes. More specifically, this scheme employed random forests (RF) algorithm to classify three study groups i.e., healthy controls (NC), aMCI, and dMCI. With the hybrid framework, the classification accuracy achieves 77.42% (AUC=0.8101) between aMCI and NC, and 82.14% (AUC=0.8473) between dMCI and NC. If comparing two MCI subtypes against each other, the accuracy can reach 79.57% (AUC=0.8410). The preliminary results suggest that pattern matching using the features from multiple modalities can achieve a clinically relevant accuracy for the a priori diagnosis in MCI subtypes.