Systematic Review on Acupuncture for Treatment of Dysphagia after Stroke

Objective. To assess the therapeutic efficacy of acupuncture for dysphagia after stroke. Methods. Seven electronic databases were searched from their inception until 31 September 2016. All randomized controlled trials (RCTs) incorporating acupuncture or acupuncture combined with other interventions for treatment of dysphagia after stroke were enrolled. Then they were extracted and assessed by two independent evaluators. Direct comparisons were conducted in RevMan 5.3.0 software. Results. 6010 patients of 71 papers were included. The pooled analysis of efficacy rate of 58 studies indicated that acupuncture group was superior to the control group with moderate heterogeneity (RR = 1.17, 95% CI: 1.13 1.21, Z=9.08, and P<0.00001); meta-analysis of the studies using blind method showed that the efficacy rate of acupuncture group was 3.01 times that of control group with no heterogeneity (RR = 3.01, 95% CI: 1.95 4.65, Z=4.97, and P<0.00001). Only 13 studies mentioned the safety evaluation. Conclusion. The result showed that the acupuncture group was better than control group in terms of efficacy rate of dysphagia after stroke. And the combining result of those researches using blind method was more strong in proof. Strict evaluation standard and high-quality RCT design are necessary for further exploration

Intelligent ZHENG Classification of Hypertension Depending on ML-kNN and Information Fusion

Hypertension is one of the major causes of heart cerebrovascular diseases. With a good accumulation of hypertension clinical data on hand, research on hypertension's ZHENG differentiation is an important and attractive topic, as Traditional Chinese Medicine (TCM) lies primarily in “treatment based on ZHENG differentiation.” From the view of data mining, ZHENG differentiation is modeled as a classification problem. In this paper, ML-kNN—a multilabel learning model—is used as the classification model for hypertension. Feature-level information fusion is also used for further utilization of all information. Experiment results show that ML-kNN can model the hypertension's ZHENG differentiation well. Information fusion helps improve models' performance

Further validation of the Health Scale of Traditional Chinese Medicine (HSTCM)

<p>Abstract</p> <p>Background</p> <p>Few health measurement scales are based on Chinese medicine theory. The Health Scale of Traditional Chinese Medicine (HSTCM) was developed to fill this gap. The aim of this study is to validate the HSTCM.</p> <p>Methods</p> <p>A convenience sample of 630 participants was recruited in 11 settings. All participants were asked to complete the HSTCM and World Health Organization Quality of Life Measure-Abbreviated Version (WHOQOL-BREF).</p> <p>Results</p> <p>Properties of the HSTCM were tested. Intra-class correlation coefficient representing the inter-interviewer reliability was 0.99 (95%CI) for the overall instrument. Spearman-Brown correlation coefficient and Cronbach's coefficient alpha were 0.81 and 0.94 respectively, indicating satisfactory internal reliability and inter-interviewer reliability. Spearman's rho correlation coefficient between the HSTCM and WHOQOL-BREFF was -0.67. A receiver operating characteristic (ROC) curve analysis was performed to test the discriminate validation. Areas under the ROC curve analysis for the HSTCM and its domains ranged 0.71–0.87 and all the lower levels of 95%CI were greater than 0.50.</p> <p>Conclusion</p> <p>The HSTCM was validated as a generic health scale and may complement existing health measurement scales in Chinese medicine health care.</p

The consistency of invasive and non-invasive arterial blood pressure for the assessment of dynamic cerebral autoregulation in NICU patients

BackgroundStudies of the clinical application of dynamic cerebral autoregulation show considerable variations, and differences in blood pressure devices may be one of the reasons for this variation. Few studies have examined the consistency of invasive and non-invasive arterial blood pressure for evaluating cerebral autoregulation. We attempted to investigate the agreement between invasive and non-invasive blood pressure methods in the assessment of dynamic cerebral autoregulation with transfer function analysis.MethodsContinuous cerebral blood flow velocity and continuous invasive and non-invasive arterial blood pressure were simultaneously recorded for 15 min. Transfer function analysis was applied to derive the phase shift, gain and coherence function at all frequency bands from the first 5, 10, and 15 min of the 15-min recordings. The consistency was assessed with Bland–Altman analysis and intraclass correlation coefficient.ResultsThe consistency of invasive and noninvasive blood pressure methods for the assessment of dynamic cerebral autoregulation was poor at 5 min, slightly improved at 10 min, and good at 15 min. The values of the phase shift at the low-frequency band measured by the non-invasive device were higher than those measured with invasive equipment. The coherence function values measured by the invasive technique were higher than the values derived from the non-invasive method.ConclusionBoth invasive and non-invasive arterial blood pressure methods have good agreement in evaluating dynamic cerebral autoregulation when the recording duration reaches 15 min. The phase shift values measured with non-invasive techniques are higher than those measured with invasive devices. We recommend selecting the most appropriate blood pressure device to measure cerebral autoregulation based on the disease, purpose, and design

Meta-analysis of genome-wide association studies in East Asian-ancestry populations identifies four new loci for body mass index

Recent genetic association studies have identified 55 genetic loci associated with obesity or body mass index (BMI). The vast majority, 51 loci, however, were identified in European-ancestry populations. We conducted a meta-analysis of associations between BMI and ∼2.5 million genotyped or imputed single nucleotide polymorphisms among 86 757 individuals of Asian ancestry, followed by in silico and de novo replication among 7488–47 352 additional Asian-ancestry individuals. We identified four novel BMI-associated loci near the KCNQ1 (rs2237892, P = 9.29 × 10−13), ALDH2/MYL2 (rs671, P = 3.40 × 10−11; rs12229654, P = 4.56 × 10−9), ITIH4 (rs2535633, P = 1.77 × 10−10) and NT5C2 (rs11191580, P = 3.83 × 10−8) genes. The association of BMI with rs2237892, rs671 and rs12229654 was significantly stronger among men than among women. Of the 51 BMI-associated loci initially identified in European-ancestry populations, we confirmed eight loci at the genome-wide significance level (P < 5.0 × 10−8) and an additional 14 at P < 1.0 × 10−3 with the same direction of effect as reported previously. Findings from this analysis expand our knowledge of the genetic basis of obesity

Multi-Label Symptom Analysis and Modeling of TCM Diagnosis of Hypertension

Traditional Chinese Medicine (TCM) has been used for diagnosis of hypertension and has significant advantages. Symptom analysis and modeling of TCM provides a way for the clinician to produce a service to users to accurately and efficiently diagnose hypertension. In this study, an ensemble learning framework based on network clustering analysis with information fusion is proposed. We first analyze the frequency distribution and cluster heat map of TCM hypertension clinical cases, and establish a network based on the syndrome and symptom of cases. Through the analysis of community networks, we get the dominant and subordinate syndrome and construct a sub-classifier to co-train and improve the performance of the classifier. Then we use ML-KNN and RAkEL-SVM multi-label classifiers to train and test the cases. Considering the result of 10-fold cross validation, we discover that ML-KNN and RAkEL-SVM with information fusion have better performance than traditional learning methods without information fusion. For all evaluation criteria, the average precision of ML-KNN is higher, and the F-Measure does not vary substantially. But the averaged recall of RAkEL-SVM is significantly higher

Deep learning architectures for multi-label classification of intelligent health risk prediction

Abstract Background Multi-label classification of data remains to be a challenging problem. Because of the complexity of the data, it is sometimes difficult to infer information about classes that are not mutually exclusive. For medical data, patients could have symptoms of multiple different diseases at the same time and it is important to develop tools that help to identify problems early. Intelligent health risk prediction models built with deep learning architectures offer a powerful tool for physicians to identify patterns in patient data that indicate risks associated with certain types of chronic diseases. Results Physical examination records of 110,300 anonymous patients were used to predict diabetes, hypertension, fatty liver, a combination of these three chronic diseases, and the absence of disease (8 classes in total). The dataset was split into training (90%) and testing (10%) sub-datasets. Ten-fold cross validation was used to evaluate prediction accuracy with metrics such as precision, recall, and F-score. Deep Learning (DL) architectures were compared with standard and state-of-the-art multi-label classification methods. Preliminary results suggest that Deep Neural Networks (DNN), a DL architecture, when applied to multi-label classification of chronic diseases, produced accuracy that was comparable to that of common methods such as Support Vector Machines. We have implemented DNNs to handle both problem transformation and algorithm adaption type multi-label methods and compare both to see which is preferable. Conclusions Deep Learning architectures have the potential of inferring more information about the patterns of physical examination data than common classification methods. The advanced techniques of Deep Learning can be used to identify the significance of different features from physical examination data as well as to learn the contributions of each feature that impact a patient’s risk for chronic diseases. However, accurate prediction of chronic disease risks remains a challenging problem that warrants further studies