Preoperative supine pelvic incidence predicts standing pelvic incidence following S2AI fixation in patients with adult spinal deformity: a prospective study.

STUDY DESIGN: A prospective study. OBJECTIVE: The aim of this study was to investigate the PI change in different postures and before and after S2­alar­iliac (S2AI) screw fixation, and to investigate whether pre-op supine PI could predict post-op standing PI. Previous studies have reported PI may change with various positions. Some authors postulated that the unexpected PI change in ASD patients could be due to sacroiliac joint laxity, S2-alar-iliac (S2AI) screw placement, or aggressive sagittal cantilever technique. However, there was a lack of investigation on how to predict post-op standing PI when making surgical strategy. METHODS: A prospective case series of ASD patients undergoing surgical correction with S2AI screw placement was conducted. Full-spine X-ray films were obtained at pre-op standing, pre-op supine, pre-op prone, as well as post-op standing postures. Pelvic parameters were measured. Spearman correlation analysis was used to determine relationships between each parameter. RESULTS: A total of 83 patients (22 males, 61females) with a mean age of 58.4 ± 9.5 years were included in this study. Pre-op standing PI was significantly lower than post-op standing PI (p = 0.004). Pre-op prone PI was significantly lower than post-op standing PI (p = 0.001). By contrast, no significant difference was observed between pre-op supine and post-op standing PI (p = 0.359) with a mean absolute difference of 2.2° ± 1.9°. Correlation analysis showed supine PI was significantly correlated with post-op standing PI (r = 0.951, p < 0.001). CONCLUSION: This study revealed the PI changed after S2AI screw fixation. The pre-op supine PI can predict post-op standing PI precisely, which facilitates to provide correction surgery strategy with a good reference for ideal sagittal alignment postoperatively.

The 10th anniversary of the scientific description of the black snub-nosed monkey (Rhinopithecus strykeri): It is time to initiate a set of new management strategies to save this critically endangered primate from extinction.

Traditionally, the genus Rhinopithecus (Milne-Edwards, 1872, Primates, Colobinae) included four allopatric species, restricted in their distributions to China and Vietnam. In 2010, a fifth species, the black snub-nosed monkey (Rhinopithecus strykeri) was discovered in the Gaoligong Mountains located on the border between China and Myanmar. Despite the remoteness, complex mountainous terrain, dense fog, and armed conflict that characterizes this region, over this past decade Chinese and Myanmar scientists have begun to collect quantitative data on the ecology, behavior and conservation requirements of R. strykeri. In this article, we review the existing data and present new information on the life history, ecology, and population size of R. strykeri. We discuss these data in the context of past and current conservation challenges faced by R. strykeri, and propose a series of both short-term and long-term management actions to ensure the survival of this Critically Endangered primate species. Specifically, we recommend that the governments and stakeholders in China and Myanmar formulate a transboundary conservation agreement that includes a consensus on bilateral exchange mechanisms, scientific research and monitoring goals, local community development, cooperation to prevent the hunting of endangered species and cross-border forest fires. These actions will contribute to the long-term conservation and survival of this Critically Endangered species.

Effect of electroacupuncture on mice model of permenopausal depressive disorder.

OBJECTIVE: Study the effect of electroacupuncture on permenopausal depressive disorder (PDD) model through the peri-menopausal depression model mice. METHODS: KM female mice were selected. Except for the blank group (BG), the other groups of mice were removed by castration method. The mice of PDD was prepared by combining chronic unpredictable stimulation. Mice in the model group (MG) were not treated and fed normally. The western medicine group (WG) was given the corresponding drug for treatment. The electroacupuncture group (EAG) was given the electroacupuncture for treatment, and consecutive for 28 days. The levels of T, E2, FSH and LH in serum of mice were measured, and the brain tissue of 5-HT, DA and NE level were measured. Through the HE staining observed the morphological changes of mice hypothalamus. RESULTS: Compared with MG, EAG could increase the number of spontaneous activities of PDD model mice, the level of T, E2 in serum and the level of 5-HT, DA, NE in brain tissue was improved, and the level of FSH, LH in serum was reduced, and the hypothalamic lesions was improved. CONCLUSION: Electroacupuncture could improve the activity and memory of PDD mice, adjust the disorder of sex hormone, and increased the levels of monoamine transmitters (5-HT, NE, DA), and it could effectively improve the behavior and related biochemical indexes of PDD, and thus play an important therapeutic role.

Fisher Discrimination Regularized Robust Coding Based on a Local Center for Tumor Classification.

Tumor classification is crucial to the clinical diagnosis and proper treatment of cancers. In recent years, sparse representation-based classifier (SRC) has been proposed for tumor classification. The employed dictionary plays an important role in sparse representation-based or sparse coding-based classification. However, sparse representation-based tumor classification models have not used the employed dictionary, thereby limiting their performance. Furthermore, this sparse representation model assumes that the coding residual follows a Gaussian or Laplacian distribution, which may not effectively describe the coding residual in practical tumor classification. In the present study, we formulated a novel effective cancer classification technique, namely, Fisher discrimination regularized robust coding (FDRRC), by combining the Fisher discrimination dictionary learning method with the regularized robust coding (RRC) model, which searches for a maximum a posteriori solution to coding problems by assuming that the coding residual and representation coefficient are independent and identically distributed. The proposed FDRRC model is extensively evaluated on various tumor datasets and shows superior performance compared with various state-of-the-art tumor classification methods in a variety of classification tasks.

A Review of Computational Drug Repositioning Approaches.

Computational drug repositioning emerges as a new idea of drug discovery and development. Contrary to conventional routines, computational drug repositioning encompasses low risk and high safety. Some successful cases demonstrated its advantage. Therefore, a large number of computational drug repositioning approaches have been developed over the past decades. We summarized briefly these methods and classified them into target-based, gene-expression-based, phenome-based and multi-omics-based categories according to strategies of drug repositioning. We reviewed some representatives of computational drug repositioning methods in each category, with emphasis on detail of techniques and finally discussed developing trends of computational drug repositioning.

Maxdenominator Reweighted Sparse Representation for Tumor Classification.

The classification of tumors is crucial for the proper treatment of cancer. Sparse representation-based classifier (SRC) exhibits good classification performance and has been successfully used to classify tumors using gene expression profile data. In this study, we propose a three-step maxdenominator reweighted sparse representation classification (MRSRC) method to classify tumors. First, we extract a set of metagenes from the training samples. These metagenes can capture the structures inherent to the data and are more effective for classification than the original gene expression data. Second, we use a reweighted regularization method to obtain the sparse representation coefficients. Reweighted regularization can enhance sparsity and obtain better sparse representation coefficients. Third, we classify the data by utilizing a maxdenominator residual error function. Maxdenominator strategy can reduce the residual error and improve the accuracy of the final classification. Extensive experiments using publicly available gene expression profile data sets show that the performance of MRSRC is comparable with or better than many existing representative methods.