Prototype edge-grown nanowire sensor array for the real-time monitoring and classification of multiple gases

The monitoring and classification of different gases using a single resistive semiconductor sensor are challenging because of the similar response characteristics. An array of separated sensors can be used as an electronic nose, but such arrays have a bulky structure and complex fabrication processes. Herein, we easily fabricated a gas-sensor array based on edge-grown SnO2 nanowires for the real-time monitoring and classification of multiple gases. The array comprised four sensors and was designed on a glass substrate. SnO2 nanowires were grown on-chip from the edge of electrodes, made contact together, and acted as sensing elements. This method was advantageous over the post-synthesis technique because the SnO2 nanowires were directly grown from the edge of the electrodes rather than on the surface. Accordingly, damage to the electrode was avoided by alloying Sn with Pt at a high growth temperature. The sensing characteristics of the sensor array were further examined for different gases, including methanol, isopropanol, ethanol, ammonia, hydrogen sulphide and hydrogen. Radar plots were used to improve the selective detection of different gases and enable effective classification

Blood pressure screening during the May Measurement Month 2017 programme in Vietnam-South-East Asia and Australasia.

Elevated blood pressure (BP) is a growing burden worldwide, leading to over 10 million deaths each year. May Measurement Month (MMM) is a global initiative aimed at raising awareness of high BP and to act as a temporary solution to the lack of screening programmes worldwide. Our aim was to screen for hypertension (HTN) and cardiovascular risk factors in people aged ≥18 years in the community, thereby define the proportion of subjects with elevated BP and assess the awareness and the effectiveness of its treatment. An opportunistic cross-sectional survey of volunteers aged ≥18 years was carried out in May 2017. Blood pressure measurement, the definition of HTN and statistical analysis followed the standard MMM protocol. From May 2017 to June 2017, through 10 cities/provinces in Vietnam, 10 993 individuals with mean age 49.1 ± 16.2 years were screened during MMM17. After multiple imputation, 3154 (28.7%) had HTN. Of individuals not receiving antihypertensive medication, 1509 (16.1%) were hypertensive. Of individuals receiving antihypertensive medication, 620 (37.7%) had uncontrolled BP. Raised BP was also associated with additional risk factors including smoking, alcohol, overweight-obesity, and diabetes. May Measurement Month 17 was the largest BP screening campaign ever undertaken in Vietnam. Undiagnosed and uncontrolled HTN in Vietnam remains a substantial health problem. Local campaigns applying standardized methods such as MMM17, will be highly useful to screen for the significant number of individuals with raised BP and increase the awareness of HTN

Polymorphisms of SP110 are associated with both pulmonary and extra-pulmonary tuberculosis among the Vietnamese

Background: Tuberculosis (TB) is an infectious disease that remains a major cause of morbidity and mortality worldwide, yet the reasons why only 10% of people infected with Mycobacterium tuberculosis go on to develop clinical disease are poorly understood. Genetically determined variation in the host immune response is one factor influencing the response to M. tuberculosis. SP110 is an interferon-responsive nuclear body protein with critical roles in cell cycling, apoptosis and immunity to infection. However association studies of the gene with clinical TB in different populations have produced conflicting results. Methods: To examine the importance of the SP110 gene in immunity to TB in the Vietnamese we conducted a case-control genetic association study of 24 SP110 variants, in 663 patients with microbiologically proven TB and 566 unaffected control subjects from three tertiary hospitals in northern Vietnam. Results: Five SNPs within SP110 were associated with all forms of TB, including four SNPs at the C terminus (rs10208770, rs10498244, rs16826860, rs11678451) under a dominant model and one SNP under a recessive model, rs7601176. Two of these SNPs were associated with pulmonary TB (rs10208770 and rs16826860) and one with extra-pulmonary TB (rs10498244). Conclusion: SP110 variants were associated with increased susceptibility to both pulmonary and extra-pulmonary TB in the Vietnamese. Genetic variants in SP110 may influence macrophage signaling responses and apoptosis during M. tuberculosis infection, however further research is required to establish the mechanism by which SP110 influences immunity to tuberculosis infection. © 2014 Fox et al

The first genome sequences of human bocaviruses from Vietnam.

As part of an ongoing effort to generate complete genome sequences of hand, foot and mouth disease-causing enteroviruses directly from clinical specimens, two complete coding sequences and two partial genomic sequences of human bocavirus 1 (n=3) and 2 (n=1) were co-amplified and sequenced, representing the first genome sequences of human bocaviruses from Vietnam. The sequences may aid future study aiming at understanding the evolution of the pathogen

Long Short Term Memory Based Model for Abnormal Behavior Prediction in Elderly Persons

Smart home refers to the independency and comfort that are ensured by remote monitoring and assistive services. Assisting an elderly person requires identifying and accurately predicting his/her normal and abnormal behaviors. Abnormal behaviors observed during the completion of activities of daily living are a good indicator that the person is more likely to have health and behavioral problems that need intervention and assistance. In this paper, we propose a method, based on long short-term memory recurrent neural networks (LSTM), to automatically predicting an elderly person’s abnormal behaviors. Our method allows to model the temporal information expressed in the long sequences collected over time. Our study aims to evaluate the performance of LSTM on identifying and predicting elderly persons abnormal behaviors in smart homes. We experimentally demonstrated, through extensive experiments using a dataset, the suitability and performance of the proposed method in predicting abnormal behaviors with high accuracy. We also demonstrated the superiority of the proposed method compared to the existing state-of-the-art methods