Analysis of antibiotic multi-resistant bacteria and resistance genes in the effluent of an intensive shrimp farm (Long An, Vietnam).

In Vietnam, intensive shrimp farms heavily rely on a wide variety of antibiotics (ABs) to treat animals or prevent disease outbreak. Potential for the emergence of multi-resistant bacteria is high, with the concomitant contamination of adjacent natural aquatic habitats used for irrigation and drinking water, impairing in turn human health system. In the present study, quantification of AB multi-resistant bacteria was carried out in water and sediment samples from effluent channels connecting a shrimp farming area to the Vam Co River (Long An Province, Vietnam). Bacterial strains, e.g. Klebsiella pneumoniae and Aeromonas hydrophila, showing multi-resistance traits were isolated. Molecular biology analysis showed that these strains possessed from four to seven different AB resistance genes (ARGs) (e.g. sul1, sul2, qnrA, ermB, tetA, aac(6)lb, dfrA1, dfr12, dfrA5), conferring multidrug resistance capacity. Sequencing of plasmids present within these multi-resistant strains led to the identification of a total of forty-one resistance genes, targeting nine AB groups. qPCR analysis on the sul2 gene revealed the presence of high copy numbers in the effluent channel connecting to the Vam Co River. The results of the present study clearly indicated that multi-resistant bacteria present in intensive shrimp cultures may disseminate in the natural environment. This study offered a first insight in the impact of plasmid-born ARGs and the related pathogenic bacteria that could emerged due to inappropriate antibiotic utilization in South Vietnam.

Community-based prevalence and associated factors of sarcopenia in the Vietnamese elderly.

Sarcopenia, a condition characterized by muscle mass decline, is one of the leading health problems in the elderly. This study determined the rate of sarcopenia according to criteria by Asian Working Group for Sarcopenia (AWGS) and related factors in elderly people. A community-based cross-sectional study was conducted in 632 people aged 60 years or over in Ho Chi Minh City. Data were collected through a predefined questionnaire and direct measurement. Sarcopenia was identified based on the Inbody 770 machine and AWGS criteria. The prevalence of sarcopenia was 32.0%. Participants with advanced age, low education, unemployment, low level of family economics and frailty were more likely to have sarcopenia. Among these, frailty had the highest impact on sarcopenia, with significantly higher odds of having sarcopenia found in participants with pre-frailty (OR = 4.80, 95% CI 2.75-8.38, p < 0.001) and frailty (OR = 21.16, 95% CI 8.96-49.97, p < 0.001). In contrast, BMI was inversely associated with sarcopenia. Sarcopenia is prevalent in the Vietnamese elderly. Apart from social demographic characteristics including age, education, employment and family economic status, frailty appeared to be an important risk factor. Early screening, referral, and treatment of sarcopenia among the elderly having a high risk of sarcopenia are recommended.

Multichannel Esophageal Heart Rate Monitoring of Preterm Infants.

OBJECTIVE: Autonomic dysregulation in preterm infants requires continuous monitoring of vital signs such as heart rate over days to months. Unfortunately, common surface electrodes are prone to electrocardiography (ECG) signal artifacts and cause serious skin irritations during long-term use. In contrast, esophageal ECG is known to be very sensitive due to the proximity of electrodes and heart and insensitive to external influences. This study addresses if multichannel esophageal ECG qualifies for heart rate monitoring in preterm infants. METHODS: We recorded esophageal leads with a multi-electrode gastric feeding tube in a clinical study with 13 neonates and compared the heartbeat detection performance with standard surface leads. A computationally simple and versatile ECG wave detection algorithm was used. RESULTS: Multichannel esophageal ECG manifested heartbeat sensitivity and positive predictive value greater than 98.5% and significant less false negative (FN) ECG waves as compared to surface ECG due to site-typical electrode motion artifacts. False positive bradycardia as indicated with more than 13 consecutive FN ECG waves was equally expectable in esophageal and surface channels. No adverse events were reported for the multi-electrode gastric feeding tube. CONCLUSION: Heart rate monitoring of preterm infants with multiple esophageal electrodes is considered as feasible and reliable. Less signal artifacts will improve the detection of bradycardia, which is crucial for immediate interventions, and reduce alarm fatigue. SIGNIFICANCE: Due to the possibility to integrate the multichannel ECG into a gastric feeding tube and meanwhile omit harmful skin electrodes, the presented system has great potential to facilitate future intensive care of preterm infants.

Combination of data-driven models and interpolation technique to develop of PM10 map for Hanoi, Vietnam.

The degradation of air quality is the most concerned issue of our society due to its harmful impacts on human health, especially in cities with rapid urbanization and population growth like Hanoi, the capital of Vietnam. This study aims at developing a new approach that combines data-driven models and interpolation technique to develop the PM10 concentration maps from meteorological factors for the central area of Hanoi. Data-driven models that relate the PM10 concentration with the meteorological factors at the air quality monitoring stations in the study area were developed using the Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) algorithms. Models' performance comparison showed that ANN models yielded better goodness-of-fit indices than MLR models at all stations in the study area with average coefficient of correlation (r) and Nash-Sutcliffe Efficiency Index (NSE) of 0.51 and 0.34 for the former, and 0.7 and 0.49 for the latter. These indices indicates that the ANN-based data-driven models outperformed the MLR-based models. Thus, the ANN-based models and the Inverse Distance Weighting (IDW) interpolation technique were then combined for mapping the monthly PM10 concentration with a spatial resolution of 1 km from global meteorological data. With this combination, the PM10 concentration maps account for both local PM10 concentration and impacts of spatio-temporal variations of meteorological factors on the PM10 concentration. This study provides a promising method to predict the PM concentration with a high spatio-temporal resolution from meteorological data.