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For 1–3 piezoelectric composite high-power transducers applied to FUS surgery, avoiding the electro-acoustic output performance fluctuating with the temperature is an important task. In this work, 1–3 type piezocomposites were fabricated with dice and fill method. The PZT4 piezoceramic was used as active phase, and mica powder modified epoxy resin E51 was used as passive phase: the mass ratio of the epoxy and mica powder was 10:x. Those composites were evaluated for power transducer applications with air backing and without front layer. The effect of mica powder modified polymer filler on the electro-acoustic output behavior and thermal stability of the transducer were detailed investigated. The results show that mica power could increase the storage modulus and the toughness of the polymer. As a result, an elevated kt of 0.7 was observed when the adding amount was 50 wt%. The electro-acoustic conversion efficiency (ηea) of the transducer substantial increased to 81–83% and with good thermal stability when x = 1–5. The research results show that adding mica powder in the filler materials was one of the potential methods to prepare high-power piezocomposite transducer.

BACKGROUND Early intervention with self-management strategies can potentially reduce the risk of progression of breast cancer-related lymphedema (BCRL). OBJECTIVE To determine if The-Optimal-Lymph-Flow (TOLF) program focused on self-management strategies applied to patients with a subclinical or mild stage of BCRL can improve lymphedema-related behaviors, symptom experience, and limb circumference changes. METHODS A total of 41 women with subclinical or mild lymphedema were enrolled in TOLF program. Lymphedema-related behaviors and lymphedema-related symptom experiences were measured by the Breast Cancer and Lymphedema Symptom Experience Index, and limb circumference changes were measured by sequential circumferential limb measurements at baseline and 1, 3, 6, and 12 months after the intervention. Generalized estimating equations were used to estimate the effects of the intervention on outcomes. RESULTS Generalized estimating equations revealed that lymphedema-related behaviors and the number and severity of lymphedema-related symptoms were significantly improved at 4 postintervention test points compared with baseline (all P < .001). Reduced lymphedema-related symptom distress in functional, social, emotional, and psychological and self-perception (all P < .01) also resulted. The majority (77.5%) of patients maintained their preintervention lymphedema status; 17.5% of them reversed from mild lymphedema to subclinical lymphedema; 5.0% of them had lymphedema status progression. CONCLUSION Positive outcomes in terms of lymphedema-related behaviors, relieving lymphedema-related symptom experience, and halting the progression of lymphedema status were documented following TOLF. IMPLICATIONS FOR PRACTICE Nurses could educate patients to incorporate the self-management strategies of TOLF program into daily life to help patients prevent or reverse subclinical or mild stage of BCRL.

Due to the narrow time window for the treatment of acute ischemic stroke, the stroke lesion area in the patient must be identified as quickly and accurately as possible to evaluate the risks and get the most timely and effective treatment. Therefore, there is great clinical significance in the study of automatic identification and segmentation methods for stroke lesions. In this paper, we propose a partitioning-stacking prediction fusion (PSPF) method based on an improved attention U-net to solve the problems of 3D-CNN-based networks, including their high computational cost and insufficient training data, and to achieve accurate segmentation of 3D stroke lesions. Our proposed PSPF method includes three steps in the first part. In Step 1, partitioning, we partition the slices obtained in a certain plane direction by slicing a Magnetic Resonance Imaging (MRI) into subsets according to the 2D graph similarity, then use each partitioned subset to perform training and prediction separately. In Step 2, stacking, we stack the 2D slice results of all subsets according to the position order in MRI before slicing and partitioning to form a 3D lesion result. In Step 3, fusion, we use soft voting to fuse the three orthogonal planes' 3D results that were obtained voxel by voxel in Steps 1 and 2. In the second part, we propose an improved attention U-net, which uses the features from three different scales to generate the attention gating coefficients that further improve training efficiency and segmentation accuracy. We implement a 6-fold cross-validation on the Anatomical Tracings of Lesions After Stroke (ATLAS) dataset to validate our method and model using metrics such as Dice Coefficient (DC), F2 score, and Area under Precision-Recall curve (APR). The results show that compared to the existing methods, our proposed method can not only improve the segmentation precision on unbalanced data but also improve the detailed performance of lesion segmentation. Our proposed method and model are generalized and accurate, demonstrating the good potential for clinical routines. The source codes and models in our method have been made publicly available at [available.upon.acceptance].

Accurate behavioral assessments of consciousness are crucial in guiding management as they provide diagnostic and prognostic information, but they are challenging since different clinical scales lead to varying results for the same patient. This paper aimed to investigate the phase synchrony index in differentiating the states of consciousness (wakefulness, somnolence, stupor, light coma, middle coma, and deep coma) in ischemic stroke. We evaluated whether the quantitative electroencephalography (EEG) measure, phase synchrony index of the left and right hemispheres, can facilitate the assessment of consciousness in stroke patients. This paper included 82 patients with ischemic stroke admitted for inpatient rehabilitation. The phase synchrony index of the left and right hemispheres was computed in the alpha band (8–12 Hz) and beta band (13–30 Hz), respectively. The associations between the phase synchrony index of the left and right hemispheres in two frequency bands with the clinical states, including the level of consciousness and the National Institutes of Health Stroke Scale score were analyzed. For further assessments of phase synchrony of the left and right hemispheres (PSI-LR), four local phase synchrony indexes in the beta band were also evaluated. The experiments result showed that PSI-LRs in the beta band correlated significantly with the level of consciousness of ischemic stroke patient and the National Institutes of Health Stroke Scale score, and they can identify the impaired consciousness in ischemic stroke patients with an accuracy of 84.15%. Compared with local phase synchrony, the phase synchrony index of channel FP1 and channel FP2 exhibited a more significant correlation with the level of consciousness than the other three local phase synchrony measures. These results suggest that the novel quantitative EEG measure, PSI-LR provides a new objective way to assess the level of consciousness in ischemic stroke patients.

Consciousness is frequently assessed in acutely brain-injured patients. However, a direct measure of consciousness based on neurological examinations and behavioral observations is time-consuming and costly in medical labors. This paper aims to explore effective quantitative electroencephalography (EEG) measures to facilitate the objective assessment of consciousness. We do two studies in this paper. In the first study, the EEG signals recorded from 27 cerebral hemorrhage patients are analyzed to explore whether the phase synchrony index is effective in distinguishing the states of consciousness in cerebral hemorrhage patients. In the second study, 14 EEG recordings from four ischemic stroke patients, who have experienced more than two types of conscious disturbance, are studied to complement the analysis of the longitudinal value of the phase synchrony index. In both studies, a weighted sum of the six quantitative EEG features is analyzed on its correlation with the level of consciousness, and a linear regression model is built for the correlation analysis. In the first study, the phase synchrony index of the left and the right hemispheres (PSI-LR) in the beta band has shown a significant correlation with the level of consciousness in cerebral hemorrhage patients (Pearson correlation coefficients ρ = 0.500 and Spearman correlation coefficients ρ = 0.400 ). In the second study, PSI-LR also shows a high correlation with the longitudinal changes of consciousness in ischemic stroke patients (Pearson correlation coefficients ρ = 0.884 and Spearman correlation coefficients ρ = 0.887 ). Furthermore, the linear regression model combining the six quantitative EEG (QEEG) features shows a determination coefficient of 44.2% in cerebral hemorrhage patients and 83.8% in longitudinal ischemic stroke investigations, respectively. The PSI-LR is an effective indicator in not only identifying the disturbance of consciousness in cerebral hemorrhage patients, but also monitoring the changes of consciousness in ischemic stroke patients. A linear combination of the six QEEG features investigated in this paper is a new measure to improve the assessment of consciousness.

Purpose Early detection and intervention of lymphedema is essential for improving the quality of life of breast cancer survivors. Previous studies have shown that patients have symptoms such as arm tightness and arm heaviness before experiencing obvious limb swelling. Thus, this study aimed to develop a symptom-warning model for the early detection of breast cancer-related lymphedema. Methods A cross-sectional study was conducted at a tertiary hospital in Beijing between April 2017 and December 2018. A total of 24 lymphedema-associated symptoms were identified as candidate predictors. Circumferential measurements were used to diagnose lymphedema. The data were randomly split into training and validation sets with a 7:3 ratio to derive and evaluate six machine learning models. Both the discrimination and calibration of each model were assessed on the validation set. Results A total of 533 patients were included in the study. The logistic regression model showed the best performance for early detection of lymphedema, with AUC = 0.889 (0.840–0.938), sensitivity = 0.771, specificity = 0.883, accuracy = 0.825, and Brier scores = 0.141. Calibration was also acceptable. It has been deployed as an open-access web application, allowing users to estimate the probability of lymphedema individually in real time. The application can be found at https://apredictiontoolforlymphedema.shinyapps.io/dynnomapp/ . Conclusion The symptom-warning model developed by logistic regression performed well in the early detection of lymphedema. Integrating this model into an open-access web application is beneficial to patients and healthcare providers to monitor lymphedema status in real-time.

Background Delirium is a common complication in hospitalized elderly patients, yet often remains unrecognized in the clinical care unit. The newly developed 3-Minute Diagnostic Interview for CAM-Defined Delirium (3D-CAM) has high sensitivity and specificity when administered by trained researchers. However, diagnostic characteristics of the 3D-CAM as performed in routine practice are unclear. Objective To determine the diagnostic characteristics of a Chinese version of the 3D-CAM administered to hospitalized elderly patients by bedside nurses. Design A prospective cohort study. Setting A tertiary geriatric hospital in China. Participants 323 patients and 49 bedside nurses. Methods This prospective cohort study was conducted at a tertiary geriatric hospital in China. A Chinese version of 3D-CAM was administered by trained bedside nurses and compared against the reference standard performed by experienced neurologists using the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition criteria. All assessors were blinded to each other's assessment results. Sensitivity and specificity of the 3D-CAM among bedside nurses were analysed by comparing with the reference standard to test the ability of 3D-CAM in detecting delirium. Results A total of 323 patients were included in the study, of whom 64 were classified as delirium positive by the neurologists. The 3D-CAM had a sensitivity of 92% (95% confidence interval, 82–97%), specificity of 89% (95% confidence interval, 84–92%), respectively. Conclusion The Chinese version of 3D-CAM demonstrated good performance as a bedside nurse-administered screening tool for delirium detection among hospitalized Chinese geriatric patients. Registration number: ChiCTR-IOR-17,010,368

Electroencephalogram (EEG) analysis has been widely used in the diagnosis of stroke diseases for its low cost and noninvasive characteristics. In order to classify the EEG signals of stroke patients with cerebral infarction and cerebral hemorrhage, this paper proposes a novel EEG stroke signal classification method. This method has two highlights. The first is that a multi-feature fusion method is given by combining wavelet packet energy, fuzzy entropy and hierarchical theory. The second highlight is that a suitable classification model based on ensemble classifier is constructed for perfectly classification stroke signals. Entropy is an accessible way to measure information and uncertainty of time series. Many entropy-based methods have been developed these years. By comparing with the performances of permutation entropy, sample entropy, approximate entropy in measuring the characteristic of stroke patient's EEG signals, it can be found that fuzzy entropy has best performance in characterization stroke EEG signal. By combining hierarchical theory, wavelet packet energy and fuzzy entropy, a multi-feature fusion method is proposed. The method first calculates wavelet packet energy of EEG stroke signal, then extracts hierarchical fuzzy entropy feature by combining hierarchical theory and fuzzy entropy. The experimental results show that, compared with the fuzzy entropy feature, the classification accuracy based on the fusion feature of wavelet packet energy and hierarchical fuzzy entropy is much higher than benchmark methods. It means that the proposed multi-feature fusion method based on stroke EEG signal is an efficient measure in classifying ischemic and hemorrhagic stroke. Support vector machine (SVM), decision tree and random forest are further used as the stroke signal classification models for classifying ischemic stroke and hemorrhagic stroke. Experimental results show that, based on the proposed multi-feature fusion method, the ensemble method of random forest can get the best classification performance in accuracy among three models.

Objective: We aimed to establish a scoring system to predict the risk of breast cancer-related lymphedema. Methods: From April 2017 to December 2018, 533 patients who previously underwent surgery for breast cancer were enrolled in this cross-sectional study. Univariate analysis was performed to explore and define the risk factors. A scoring system was then established on the basis of odds ratio values in the regression analysis. Results: The additive scoring system values ranged from 6 to 22. The receiver operating characteristic (ROC) curve of this scoring system showed a sensitivity and specificity of 83.3% and 57.3%, respectively, to predict the risk of lymphedema at a cut-off of 15.5 points; the area under the curve was 0.736 (95% confidence interval: 0.662–0.811), with χ2 = 5.134 (P = 0.274) for the Hosmer–Lemeshow test. Conclusions: The predictive efficiency and accuracy of the scoring system were acceptable, and the system could be used to predict and screen groups at high risk for breast cancer-related lymphedema. Keywords: Breast cancer lymphedema, Breast neoplasms, China, Cross-sectional studies, Lymphedema, Risk factors, ROC curve

Electroencephalogram (EEG) analysis has been widely used in the diagnosis of stroke diseases for its low cost and noninvasive characteristics. In order to classify the electroencephalogram (EEG) signals of stroke patients with cerebral infarction and cerebral hemorrhage, this paper proposed a novel EEG stroke signal feature extraction method by combining fuzzy entropy and hierarchical theory. Fuzzy entropy not only took the advantages of sample entropy, but also had less dependence on the length of time series and possessed better robustness to noise signals. It measured the similarity of two vectors based on Gaussian function instead of Heaviside function, avoiding discontinuity problems of sample entropy and approximate entropy. Hierarchical theory efficiently took advantages of the approximation information in low-frequency and the detail information in high-frequency. This was benefit for capturing a wealth of dynamic information and retaining redundant components. Support vector machine (SVM) was further used as the stroke signal classification model for classifying ischemic stroke and hemorrhagic stroke. The experimental results showed that, compared with other benchmarks, the classification accuracy based on the features of hierarchical fuzzy entropy is much higher than those benchmarks methods. Compared with the features of fuzzy entropy without using hierarchical theory, the classifier based on the features of hierarchical fuzzy entropy gave a much more improvement in classification performance by increasing accuracy from 68.03% to 96.72%. It meant that the proposed EEG stroke signal hierarchical fuzzy entropy feature extraction method was an efficient measure in classifying ischemic and hemorrhagic stroke.

As a typical cardiovascular disease, stroke is the world's second most deadly disease with the characteristics of high mortality, incidence, recurrence rate and disability rate. To improve the stroke predicting accuracy is a necessary measure for reducing the happening of stroke. As a non-invasive and portable method? Transcranial Doppler (TCD) can provide valuable information in diagnosis of cerebrovascular disease by measuring the blood flow speed in intracranial arteries. How to improve diagnosis accuracy performance based on TCD data set is a necessary demand for realizing cerebrovascular disease TCD intelligent diagnosis system. But TCD data set often presents an imbalanced phenomenon for less patients' data over all examiners. This imbalanced phenomenon easily makes a lower predicting accuracy of cerebrovascular patients. In this paper, an improved fuzzy support vector machine model is proposed by combining class center distance and information entropy. The model efficiently utilizes the advantages of class center distance and information entropy. Moreover, the imbalanced fuzzy support vector machine is used by combining an adaptive imbalanced regulator. The different influence of the imbalance regulating factor value on the classification effect were discussed in detail for finding a suitable regulating factor value. A practical clinical imbalanced TCD dataset is used by collecting clinical TCD data for identify the performance of the proposed method. The experimental results showed that the proposed method can get a better classification performance with higher accuracies and G-means over imbalanced TCD dataset than other benchmark methods.

Cervical cancer is the third most common gynecologic cancer in the world. Exploration of the molecular mechanism underlying cervical cancer pathogenesis will provide new insights into the development of novel therapies. In this study, we were aimed to characterize a novel miRNA in cervical cancer tumorigenesis. First, we measured the expressional change of miR-144-3p in clinical tissues and cancer cells. Second, we employed cell proliferation, cell migration, and invasion assays to understand its functional role in cervical cancer. Then, we confirmed in vitro findings in xenograft cancer model. Last, we mapped out a downstream target of miR-144-3p and validated its functional role in cancer cells. In the results, miR-144-3p was found significantly downregulated in cervical cancer cells and tissues. Over-expressing miR-144-3p suppressed cancer cells growth and metastasis. Consistent with in vitro results, over-expressing miR-144-3p led to tumor growth inhibition in vivo. Further on, MAPK6 was identified as an endogenous target of miR-144-3p in cervical cancer. Knocking down MAPK6 inhibited cervical cancer cells proliferation, migration, and invasion potential. Our investigation was the first time to report miR-144-3p as a tumor suppressive miRNA in cervical cancer. It inhibited tumor growth by targeting MAKP6. The newly identified signalling axis may serve as novel therapeutic targets to manage cervical cancer.