Search results

The consumption of rice that contains high levels of inorganic arsenic may cause human health risk. This study aims to determine As species concentrations, particularly iAs, in raw rice in Ho Chi Minh (HCM) City and its health risks.

A total of 60 polished raw composite samples of rice were purchased from traditional markets and supermarkets in HCM City. All samples were analyzed by HPLC-ICPMS for As species determination.

Mean concentrations of inorganic arsenic in all samples, which were purchased from supermarket and traditional market, were 88.8 µg/kg and 80.6 µg/kg, respectively. Overall, inorganic arsenic level was 84.7 µg/kg and contributed the highest proportion of arsenic species in rice with 67.7%. The proportion profiles for arsenic species were: As (III) (60 %); dimethylarsinic acid (32.2 %); As (V) (7.7 %) and methylarsonic acid (0.1 %). Inorganic arsenic level in raw rice was below the recommendation of World Health Organization. Using the benchmark dose recommended by the Joint FAO/WHO Expert Committee on Food Additives (JECFA), all exposure doses were lower than BMDL05. However, as the doses ranged from 3.0 to 8.6 of Margin of Exposure (MOE), the health risk of iAs from rice consumption remains public health concern.

The study results report on the surveillance data of the presence of inorganic arsenic in raw rice products, which are available in the supermarkets and traditional markets, and its health risk to consumers in a metropolitan city in Vietnam.

Diagnosing pain in neonates is difficult but critical. Although approximately thirty manual pain instruments have been developed for neonatal pain diagnosis, most are complex, multifactorial, and geared toward research. The goals of this work are twofold: 1) to develop a new video dataset for automatic neonatal pain detection called iCOPEvid (infant Classification Of Pain Expressions videos), and 2) to present a classification system that sets a challenging comparison performance on this dataset. The iCOPEvid dataset contains 234 videos of 49 neonates experiencing a set of noxious stimuli, a period of rest, and an acute pain stimulus. From these videos 20 s segments are extracted and grouped into two classes: pain (49) and nopain (185), with the nopain video segments handpicked to produce a highly challenging dataset. An ensemble of twelve global and local descriptors with a Bag-of-Features approach is utilized to improve the performance of some new descriptors based on Gaussian of Local Descriptors (GOLD). The basic classifier used in the ensembles is the Support Vector Machine, and decisions are combined by sum rule. These results are compared with standard methods, some deep learning approaches, and 185 human assessments. Our best machine learning methods are shown to outperform the human judges.