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Epidural abscess after obstetric epidural anesthesia occurs infrequently and may result in severe morbidity. We report a clinical case of an epidural abscess in Vietnam. A 31-year-old woman who was in labor was admitted to our hospital and given epidural anesthesia indicated to relieve labor pain. After three hours of anesthesia, cesarean section was indicated for the patient due to signs of fetal failure, the epidural catheter was then used to relieve pain for the first 48 hours postoperatively. On post-partum day 5 she presented with high fever of 39–40°C, fatigue, stimulation, dyspnea, and lumbar pain. The patient then suffered from sepsis and lower limb paralysis. She was diagnosed with epidural abscess based on the MRI of the lumbar spine. The abscess was treated by surgery for draining and appropriate antibiotics with a satisfactory outcome as she completely recovered her motor function and stable health status.

Silicon nitride has been widely employed as one of the most important engineering ceramics for many practical applications due to their excellent properties such as high temperature resistance, low density, high corrosion resistance and other mechanical properties. In this study, silicon nitride (Si3N4) ceramic materials was synthesized by a direct nitridation method of amorphous silicon powder under a flow mixture of argon and nitrogen or ammonia. The amorphous silicon powder was placed in a tube furnace at the temperature of from 1300oC to 1600oC for 30-800 min under a flow of gases mixture. The phase compositions and morphology of the obtained Si3N4 powder were characterized by using X-ray diffractometry (XRD) and SEM techniques, respectively. The a-Si3N4 formed at temperature of 1500oC under a mixed gases flow of 3 L/ min for 400 composed of straight rod-like fibers with a length in the range of 5 to 100 mm and diameters of about 0.3-4 mm. The effects of gas flow rate, conversion temperature, reduction time and gas compostion on the effectiveness of a-Si3N4 formation were investigated in detail. Effect of the auxiliary gases and synthesizing conditions for direct nitridation of Si3N4 on phase content and phase transformation of a-Si3N4 to β-Si3N4 were also evaluated.

The contamination characteristics of arsenic and other trace elements in groundwater and the potential risks of arsenic from the groundwater were investigated. Elevated contamination of arsenic, barium and manganese was observed in tube-well water of two villages (Chuyen Ngoai and Chau Giang) in Ha Nam province in the Northern Vietnam. Concentrations of As in the groundwater ranged from 12.8 to 884 µg/L with mean values in Chuyen Ngoai and Chau Giang were 614.7 and 160.1 µg/L, respectively. About 83 % of these samples contained As concentrations exceeding WHO drinking water guideline of 10 μg/L. The mean values of Mn and Ba in groundwater from Chuyen Ngoai and Chau Giang were 300 and 657 μg/L and 650 and 468 μg/L, respectively. The mean value of Ba concentration in groundwater in both Chuyen Ngoai and Chau Giang was about 22 % of the samples exceeded the WHO guideline (700 µg/L). Arsenic concentrations in human urine of residents from Chuyen Ngoai and Chau Giang were the range from 8.6 to 458 µg/L. The mean values of Mn and Ba in human urine of local people from Chuyen Ngoai were 46.9 and 62.8 μg/L, respectively, while those in people from Chau Giang were 25.9 and 45.9 μg/L, respectively. The average daily dose from ingesting arsenic for consuming both untreated and treated groundwater is from 0.02 to 11.5 and 0.003 to 1.6 μg/kg day, respectively. Approximately, 57 % of the families using treated groundwater and 64 % of the families using untreated groundwater could be affected by elevated arsenic exposure.

The named entity recognition (NER) problem has an important role in many natural language processing (NLP) applications and is one of the fundamental tasks for building NLP systems. Supervised learning methods can achieve high performance but they require a large amount of training data that is time-consuming and expensive to obtain. Active learning (AL) is well-suited to many problems in NLP, where unlabeled data may be abundant but labeled data is limited. The AL method aims to minimize annotation costs while maximizing the desired performance from the model. This study proposes a method to classify named entities from Tweet streams on Twitter by using an AL method with different query strategies. The samples were queried for labeling by human annotators based on query by committee and diversity-based querying. The experiments evaluated the proposed method on Tweet data and achieved promising results that proved better than the baseline.

Alginate oligosaccharides (AOS) have been known as a natural material with a wide variety of biological activities, which used for a long time. The evidence of using AOS to confer health benefits have been documented. The isolation and characterization of the properties, biological activity as well as the applications of AOS in various fields have been studied recently. In present work, we provide the recent research of AOS, particularly focusing on the applications in food and medicinal industry. This review also describes some experimental models, application and discuss the functional and biological mechanisms of AOS. In conclusion, AOS promotes beneficial effects on the immuno-metabolic response to various infectious diseases as well as it is promising as a biomaterial for functional foods and medicinal drugs development.