Risk factors related to the recurrence of endometrioma in patients with long-term postoperative medical therapy

Objectives: The purpose of this study was to identify clinical risk factors for the recurrence of ovarian endometrioma after ovarian cystectomy in Korean women with long-term postoperative medical therapy.Material and Methods: A total of 134 patients who were surgically treated for endometriotic cysts at Pusan National University Hospital were included in this retrospective study. All patients received long-term postoperative medical treatment for at least 12 months after the first-line conservative surgery. Several epidemiologic variables were analyzed as possible risk factors for recurrence. Endometrioma recurrence was considered when a cystic mass was observed on transvaginal or transrectal sonography. Statistical analysis was performed using independent t-tests for parametric continuous variables.Results: The mean follow-up period for the 134 patients was 56.5 ± 14.3 months (range, 36–120 months) and the mean duration of the medical therapy was 17.9 ± 17.3 months (range, 12–120 months). The overall recurrence rate was 35/134 (26.12%). Our univariate analysis showed statistically significant differences between the recurrent and non-recurrent groups in terms of weight (P = 0.013), body mass index (P = 0.007), age at the time of surgery (P = 0.013), the diameter of the largest cyst (P = 0.001), the presence of dysmenorrhea (P < 0.0001), and postoperative pregnancy (P = 0.016). Multivariate analysis showed that body mass index (OR 1.153, 95% CI 1.003–1.326, P = 0.046), age at the time of surgery (OR 0.924, 95% CI 0.860–0.992, P = 0.029), and presence of dysmenorrhea (OR 12.226, 95% CI 3.543–42.188, P < 0.0001) were significantly correlated with the recurrence of endometrioma.Conclusions: We found that patients with dysmenorrhea after surgery, and a younger age of the patient at the time of surgery were the highest risk factors associated with the recurrence of endometrioma, despite long-term postoperative medication

A Novel Synthetic Compound (E)-5-((4-oxo-4H-chromen-3-yl)methyleneamino)-1-phenyl-1H-pyrazole-4-carbonitrile Inhibits TNF alpha-Induced MMP9 Expression via EGR-1 Downregulation in MDA-MB-231 Human Breast Cancer Cells

Breast cancer is a common malignancy among women worldwide. Gelatinases such as matrix metallopeptidase 2 (MMP2) and MMP9 play crucial roles in cancer cell migration, invasion, and metastasis. To develop a novel platform compound, we synthesized a flavonoid derivative, (E)-5-((4-oxo-4H-chromen-3-yl)methyleneamino)-1-phenyl-1H-pyrazole-4-carbonitrile (named DK4023) and characterized its inhibitory effects on the motility andMMP2andMMP9expression of highly metastatic MDA-MB-231 breast cancer cells. We found that DK4023 inhibited tumor necrosis factor alpha (TNF alpha)-induced motility and F-actin formation of MDA-MB-231 cells. DK4023 also suppressed the TNF alpha-induced mRNA expression ofMMP9through the downregulation of the TNF alpha-extracellular signal-regulated kinase (ERK)/early growth response 1 (EGR-1) signaling axis. These results suggest that DK4023 could serve as a potential platform compound for the development of novel chemopreventive/chemotherapeutic agents against invasive breast cancer

A Multi-scale Model for Simulating Liquid-hair Interactions

© ACM, 2017. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Fei, Y. (Raymond), Maia, H. T., Batty, C., Zheng, C., & Grinspun, E. (2017). A Multi-scale Model for Simulating Liquid-hair Interactions. ACM Trans. Graph., 36(4), 56:1–56:17. https://doi.org/10.1145/3072959.3073630The diverse interactions between hair and liquid are complex and span multiple length scales, yet are central to the appearance of humans and animals in many situations. We therefore propose a novel multi-component simulation framework that treats many of the key physical mechanisms governing the dynamics of wet hair. The foundations of our approach are a discrete rod model for hair and a particle-in-cell model for fluids. To treat the thin layer of liquid that clings to the hair, we augment each hair strand with a height field representation. Our contribution is to develop the necessary physical and numerical models to evolve this new system and the interactions among its components. We develop a new reduced-dimensional liquid model to solve the motion of the liquid along the length of each hair, while accounting for its moving reference frame and influence on the hair dynamics. We derive a faithful model for surface tension-induced cohesion effects between adjacent hairs, based on the geometry of the liquid bridges that connect them. We adopt an empirically-validated drag model to treat the effects of coarse-scale interactions between hair and surrounding fluid, and propose new volume-conserving dripping and absorption strategies to transfer liquid between the reduced and particle-in-cell liquid representations. The synthesis of these techniques yields an effective wet hair simulator, which we use to animate hair flipping, an animal shaking itself dry, a spinning car wash roller brush dunked in liquid, and intricate hair coalescence effects, among several additional scenarios.Graduate Student Research FellowshipNational Science FoundationNatural Sciences and Engineering Research Council of Canad

AMID: Accurate Magnetic Indoor Localization Using Deep Learning

Geomagnetic-based indoor positioning has drawn a great attention from academia and industry due to its advantage of being operable without infrastructure support and its reliable signal characteristics. However, it must overcome the problems of ambiguity that originate with the nature of geomagnetic data. Most studies manage this problem by incorporating particle filters along with inertial sensors. However, they cannot yield reliable positioning results because the inertial sensors in smartphones cannot precisely predict the movement of users. There have been attempts to recognize the magnetic sequence pattern, but these attempts are proven only in a one-dimensional space, because magnetic intensity fluctuates severely with even a slight change of locations. This paper proposes accurate magnetic indoor localization using deep learning (AMID), an indoor positioning system that recognizes magnetic sequence patterns using a deep neural network. Features are extracted from magnetic sequences, and then the deep neural network is used for classifying the sequences by patterns that are generated by nearby magnetic landmarks. Locations are estimated by detecting the landmarks. AMID manifested the proposed features and deep learning as an outstanding classifier, revealing the potential of accurate magnetic positioning with smartphone sensors alone. The landmark detection accuracy was over 80% in a two-dimensional environment

Piezoelectric characteristics of PVA/DL-alanine polycrystals in d33 mode

Summary: In this study, polyvinyl alcohol (PVA)-mixed DL-alanine (PVA/DL-alanine) polycrystals are fabricated, and their piezoelectric characteristics in the d33 mode are investigated. The d33 piezoelectric coefficients of the PVA/DL-alanine polycrystals are found to increase with an increase in the weight ratio of DL-alanine, and the PVA/DL-alanine polycrystal composed of PVA and DL-alanine in a weight ratio of 1:3 exhibits a d33 of ∼5 pC/N. The piezoelectric characteristics of the PVA/DL-alanine polycrystals are discussed in terms of the crystal structure by employing scanning electron microscopy and X-ray diffraction analyses. To confirm the piezoelectric performance of the polycrystals, the piezoelectric voltages of a piezoelectric device composed of a single layer of ZnO thin film and heterostructured devices consisting of a layer of PVA/DL-alanine polycrystal and a ZnO thin film layer are measured and compared. This study presents PVA/DL-alanine polycrystals as a potential piezoelectric material for bio-friendly piezoelectric-device applications

An Adaptive User Tracking Algorithm Using Irregular Data Frames for Passive Fingerprint Positioning

Wi-Fi fingerprinting is the most popular indoor positioning method today, representing received signal strength (RSS) values as vector-type fingerprints. Passive fingerprinting, unlike the active fingerprinting method, has the advantage of being able to track location without user participation by utilizing the signals that are naturally emitted from the user’s smartphone. However, since signals are generated depending on the user’s network usage patterns, there is a problem in that data are irregularly collected according to the patterns. Therefore, this paper proposes an adaptive algorithm that shows stable tracking performances for fingerprints generated at irregular time intervals. The accuracy and stability of the proposed tracking method were verified by experiments conducted in three scenarios. Through the proposed method, it is expected that the stability of indoor positioning and the quality of location-based services will improve

An adaptive hybrid filter for practical WiFi-based positioning systems

AbstractThis paper proposes an adaptive hybrid filter for WiFi-based indoor positioning systems. The hybrid filter adopts the notion of particle filters within the prediction framework of the basic Kalman filter. Restricting the predicts of a moving object to a small number of particles on a way network, and replacing the Kalman gain with a dynamic weighting scheme are the key features of the hybrid filter. The adaptive hybrid filter significantly outperformed the basic Kalman filter, and a particle filter in the performance evaluation at three test places: a Library and N5 building, KAIST, Daejeon, and an E-mart mall, Seoul