Preeclampsia is a multisystem disorder associated with pregnancy and is a common cause of perinatal morbidity. The aim of this study was to determine whether elevated serum uric acid levels, alone or in combination with other laboratory factors could predict preeclampsia in women with adverse perinatal outcomes. We conducted a prospective observational study of women who were admitted to Soonchunhyang University Cheonan Hospital from January 2016 to December 2016. Demographic, clinical and laboratory data were collected for each pregnancy at the time of delivery. Women were grouped according to status (preeclampsia or normotensive), and a logistic regression analysis was used to determine the relationship between serum uric acid levels and adverse outcomes. The mean age of the study participants was 31.3 ± 5.0 years. In patients with preeclampsia, serum uric acid level was associated with the severity of preeclampsia, including blood pressure ( R = 0.321, P = .014), serum creatinine levels ( R = 0.505, P < .001), and proteinuria ( P = .014), as well as adverse fetal outcomes, including preterm labor ( P = .027) and low birth weight delivery ( P = .001). The optimal maternal serum uric acid threshold that predicted low birth weight at delivery was 6.35 mg/dL (sensitivity, 0.58; specificity, 0.95). The multivariable logistic regression model that was used to predict low birth weight at delivery displayed an area under the receiver-operating characteristic curve of 0.902 (95% confidence interval, 0.817–0.986). In women with preeclampsia, maternal serum uric acid level is an important parameter for predicting low birth weight. Additionally, the combination of uric acid, hemoglobin, and bilirubin levels appear to be optimal for predicting low birth weight in women with preeclampsia.

Cornus kousa the Korean dogwood has been traditionally used in East Asia as therapeutic traditional medicine however biological activities of Cornus kousa have not been investigated previously. The aim of the present study was to evaluate anti-obesity activities coupled with anti-angiogenic activities of anthocyanins rich fraction of ethanolic leaf extract of Cornus kousa (ELECk) in HUVECs and 3T3- L1 cells. Dried plants leaves were extracted with 70% ethanol and anthocyanin fraction (AnT Fr) was obtained by eluting the ethanolic extract through non-polar macroporous resin and further purification by HPLC. Antiangiogenic activities were determined by antiproliferative effect of AnT Fr on HUVECs. In the presence of various concentrations of AnT Fr, 3T3-L1 preadipocytes were induced to differentiate. Lipid accumulation in differentiated adipocytes were quantified by Oil-Red O staining. AnT Fr significantly suppressed angiogenesis by inhibiting proliferation and tube formation of HUVECs via downregulating VEGRF 2, PI3K, β‐catenin, NF‐kB, and Akt1 in a dose dependent manner. AnT Fr inhibited lipid accumulation by down-regulating adipogenesis and lipogenesis promoting signaling proteins, PPARγ, CCAAT, C/EBPα, aP2, FAS, and LPL, however enhanced AMPK activation to p-AMPK in 3T3 cells quantified and expressed by western blotting. AnT Fr inhibit lipid accumulation by regulating adipogenesis and lipogenesis related genes and signaling proteins. The anti-obesity activities exerted by Cornus kousa are associated with antiangiogenic activities of anthocyanins rich fraction of Cornus kousa. Hence the presence of bioactive anthocyanins, Cornus kosa, is a good candidate for nutraceutical and pharmaceutical formulation for treating or controlling obesity.

Capillary hemangioma of the tracheobronchial tree is an extremely rare benign tumor in adults, especially those located in the bronchus. Characteristics and treatment of capillary hemangiomas of adult tracheobronchial trees have not been well known. We present a 61-year-old man with hemoptysis, which was caused by a small tiny nodule in the left lingular segmental bronchus. The nodule was removed by a forcep biopsy, via flexible bronchoscopy, and it was revealed to be capillary hemangioma. A small isolated endobronchial capillary hemangioma can be treated with excisional forcep biopsy, but a risk of massive bleeding should not be overlooked.

We propose a framework based on imitation learning and self-learning to enable robots to learn, improve, and generalize motor skills. The peg-in-hole task is important in manufacturing assembly work. Two motor skills for the peg-in-hole task are targeted: "hole search" and "peg insertion". The robots learn initial motor skills from human demonstrations and then improve and/or generalize them through reinforcement learning (RL). An initial motor skill is represented as a concatenation of the parameters of a hidden Markov model (HMM) and a dynamic movement primitive (DMP) to classify input signals and generate motion trajectories. Reactions are classified as familiar or unfamiliar (i.e., modeled or not modeled), and initial motor skills are improved to solve familiar reactions and generalized to solve unfamiliar reactions. The proposed framework includes processes, algorithms, and reward functions that can be used for various motor skill types. To evaluate our framework, the motor skills were performed using an actual robotic arm and two reward functions for RL. To verify the learning and improving/generalizing processes, we successfully applied our framework to different shapes of pegs and holes. Moreover, the execution time steps and path optimization of RL were evaluated experimentally. of motor skills. However, for acquiring complete motor skills, it has one evident limitation: it does not ensure that robots acquire motor skills that are optimized for their goals (that is, it generally provides near-optimal solutions) [5]. Furthermore, it is not easy for human performers to provide a demonstration dataset that can cover all situations arising during the execution of a motor skill [6]. Nonetheless, these human demonstrations can be used as a solid starting point for robots to acquire motor skills [7]. To obtain optimal motor skills, robots must be able to improve motor skills through self-learning. However, this self-learning is a time-consuming and expensive process in the absence of references. We attempt to obtain these optimal solutions with fewer trials-and-errors by providing near-optimal solutions learned from human demonstrations. In this paper, robots improve motor skills to optimize them-referred to as improvement-and generalize them so that they are widely applicable-known as generalization-through self-learning.The peg-in-hole task has also been addressed through several imitation learning studies [8,9]. However, the peg-in-hole task is not easy to learn with this method alone. The main reason is that it is difficult for human performers to provide a complete demonstration dataset to robots, because it is not feasible to prepare all possible reaction situations. In addition, unintended reaction information may be included in the dataset during the demonstration process. These problems may prevent robots from acquiring the complete motor skills. Thus, initial motor skills are learned to classify reaction force/moment signals and generate reaction motion trajectories from human demonstrations, and thei...