Efficacy of combined atorvastatin calcium, Salviae miltiorrhizae and ligustrazine hydrochloride injection in cerebral infarction patients

Purpose: To study the effect of a combination of atorvastatin calcium with Salviae miltiorrhizae and ligustrazine hydrochloride injection on serum levels of ferritin (SF), C-reactive protein (CRP) and hypoxia-inducible factor 1α(HIF-1α) in cerebral infarction patients.Methods: A total of 60 cerebral infarction patients (confirmed by CT or MRI scan) were randomly assigned to control group and observation group (30 patients/group). Both groups received routine treatments. All patients took atorvastatin calcium, but those in the observation group were treated with Salviae miltiorrhizae and ligustrazine hydrochloride injection, in addition to atorvastatin hydrochloride for 14 days, with 7 days as treatment course. The levels of SF, CRP and HIF-1α were determined before and after treatment, to assess clinical efficacy and safety.Results: In both groups, SF, CRP and HIF-1α levels were lower after treatment than before treatment (p < 0.05). NIHSS score and platelet activation indices were also significantly reduced, relative to control (p < 0.05).Conclusion: The combination of atorvastatin calcium with Salviae miltiorrhizae and ligustrazine hydrochloride injection can control vascular inflammatory reactions by decreasing the levels of SF, CRP and HIF-1α. Thus, it may be beneficial in the clinical management of cerebral infarction.Keyword: Cerebral infarction, Atorvastatin calcium, Salviae miltiorrhizae, Ligustrazine hydrochloride injectio

A Novel General Imaging Formation Algorithm for GNSS-Based Bistatic SAR.

Global Navigation Satellite System (GNSS)-based bistatic Synthetic Aperture Radar (SAR) recently plays a more and more significant role in remote sensing applications for its low-cost and real-time global coverage capability. In this paper, a general imaging formation algorithm was proposed for accurately and efficiently focusing GNSS-based bistatic SAR data, which avoids the interpolation processing in traditional back projection algorithms (BPAs). A two-dimensional point target spectrum model was firstly presented, and the bulk range cell migration correction (RCMC) was consequently derived for reducing range cell migration (RCM) and coarse focusing. As the bulk RCMC seriously changes the range history of the radar signal, a modified and much more efficient hybrid correlation operation was introduced for compensating residual phase errors. Simulation results were presented based on a general geometric topology with non-parallel trajectories and unequal velocities for both transmitter and receiver platforms, showing a satisfactory performance by the proposed method

Training Effectiveness at PT XYZ Using Kirkpatrick Model and Return on Investment of Training (ROI-Training)

The goal of the research was to evaluate the effectiveness of Kirkpatrick model and Return on Investment of Training at PT XYZ. Observation was applied to this research. The result has shown several facts such as trainee\u27s feedback score was 4,62 above 4,10 as required by the company in terms of reaction, the average final exam score was 3,66 above 3,00 as required by the company in terms of learning, the trainees\u27 superiors\u27 feedback score was 3,53 above 3,00 as required by the company and Return on Investment of Training (ROI-Training) was 58,88% above 15% as required by the company. With these results, the company can conclude that the program is effective in nurturing its supervisory leaders

Improving variational autoencoder with deep feature consistent and generative adversarial training

We present a new method for improving the performances of variational autoencoder (VAE). In addition to enforcing the deep feature consistent principle thus ensuring the VAE output and its corresponding input images to have similar deep features, we also implement a generative adversarial training mechanism to force the VAE to output realistic and natural images. We present experimental results to show that the VAE trained with our new method outperforms state of the art in generating face images with much clearer and more natural noses, eyes, teeth, hair textures as well as reasonable backgrounds. We also show that our method can learn powerful embeddings of input face images, which can be used to achieve facial attribute manipulation. Moreover we propose a multi-view feature extraction strategy to extract effective image representations, which can be used to achieve state of the art performance in facial attribute prediction

Rigidity theorems of spacelike entire self-shrinking graphs in the pseudo-Euclidean space

In this paper, we firstly establish a new volume growth estimate for spacelike entire graphs in the pseudo-Euclidean space $\mathbb{R}^{m+n}_n$. Then by using this volume growth estimate and the Co-Area formula, we prove various rigidity results for spacelike entire self-shrinking graphs