Effects of oil and grape seed tannin extract on intakes, digestibility, milk yield and composition of Saanen goats

An experiment was conducted as a 4×4 Latin square design using 4 lactating Saanen goats, 19 months old and 47.9±1.04 kg of body weight, to evaluate the effect of oil and grape seed tannin extract (GSTE) supplementation on feed intake, digestibility, milk yield and milk composition. Each experimental period lasted for 21 days including 16 days for adjustment and 5 days for sampling. Goats were fed a control diet (Ctrl) consisting of 60% concentrate and 40% fresh Para grass (dry matter, DM, basis) while other 3 treatments were supplementation of 2.5% soybean oil (SO); 2.5% soybean oil + tuna fish oil at 3:2 w:w (SFO); 2.5% soybean oil + tuna fish oil at 3:2 w:w + 0.8% GSTE (OCT). The results showed that oil and GSTE did not affect feed intake, digestibility, milk yield and composition of goats (P > 0.05). However, digestibility of EE was higher (P < 0.05) in SFO and OCT diets (85.4% and 84.7%, respectively) compared with Ctrl (76.2%). Combined data suggested that feeding 2.5% oil blend with or without 0.8% GSTE increased EE digestibility in goats without affecting intake, animal performance and milk composition

Ventilator-associated respiratory infection in a resource-restricted setting: impact and etiology.

BACKGROUND: Ventilator-associated respiratory infection (VARI) is a significant problem in resource-restricted intensive care units (ICUs), but differences in casemix and etiology means VARI in resource-restricted ICUs may be different from that found in resource-rich units. Data from these settings are vital to plan preventative interventions and assess their cost-effectiveness, but few are available. METHODS: We conducted a prospective observational study in four Vietnamese ICUs to assess the incidence and impact of VARI. Patients ≥ 16 years old and expected to be mechanically ventilated > 48 h were enrolled in the study and followed daily for 28 days following ICU admission. RESULTS: Four hundred fifty eligible patients were enrolled over 24 months, and after exclusions, 374 patients' data were analyzed. A total of 92/374 cases of VARI (21.7/1000 ventilator days) were diagnosed; 37 (9.9%) of these met ventilator-associated pneumonia (VAP) criteria (8.7/1000 ventilator days). Patients with any VARI, VAP, or VARI without VAP experienced increased hospital and ICU stay, ICU cost, and antibiotic use (p < 0.01 for all). This was also true for all VARI (p < 0.01 for all) with/without tetanus. There was no increased risk of in-hospital death in patients with VARI compared to those without (VAP HR 1.58, 95% CI 0.75-3.33, p = 0.23; VARI without VAP HR 0.40, 95% CI 0.14-1.17, p = 0.09). In patients with positive endotracheal aspirate cultures, most VARI was caused by Gram-negative organisms; the most frequent were Acinetobacter baumannii (32/73, 43.8%) Klebsiella pneumoniae (26/73, 35.6%), and Pseudomonas aeruginosa (24/73, 32.9%). 40/68 (58.8%) patients with positive cultures for these had carbapenem-resistant isolates. Patients with carbapenem-resistant VARI had significantly greater ICU costs than patients with carbapenem-susceptible isolates (6053 USD (IQR 3806-7824) vs 3131 USD (IQR 2108-7551), p = 0.04) and after correction for adequacy of initial antibiotics and APACHE II score, showed a trend towards increased risk of in-hospital death (HR 2.82, 95% CI 0.75-6.75, p = 0.15). CONCLUSIONS: VARI in a resource-restricted setting has limited impact on mortality, but shows significant association with increased patient costs, length of stay, and antibiotic use, particularly when caused by carbapenem-resistant bacteria. Evidence-based interventions to reduce VARI in these settings are urgently needed

Dynamic weighted ensemble for diarrhoea incidence predictions

Diarrhoea (DH) disease pose significant threats to national morbidity and mortality in Vietnam, especially on children. Being a climate sensitive disease, it has strong links to various meteorological factors like rainfalls or temperatures. Hence, together with global climate changes, the risk of diarrhoea has been increasing gradually while Vietnam is already a hotspot of diarrhoea worldwide. Thus, having an effective early warning system is becoming an urgent need. However, it has not been paid enough attention with very few research works, mainly focusing on quantilizing the relationships among various climate factors and diarrhoea incidences. Exploring more sophisticated machine learning techniques is therefore an interesting work towards more efficient and effective warning systems. This paper consists of two main contributions. First, many different state-of-the-art prediction models from traditional to most recent advantaged methods, e.g., SARIMA, SARIMAX, LSTM, CNN, Xgboost, SVM, LightGBM, Catboost, LightGBM, N-HiST, BlockRNN, TCN, TFT, or Transformer, are studied for predicting DH rates for a large number of locations (55 provinces) with different climates, geographics and socio-economy factors. It provides a useful view on the overall performances of different ML models on the prediction task, which is extremely useful for other researchers when developing early-warning systems for DH in other places. Second, we introduce a novel ensemble prediction model, called dynamic weighted ensemble (DWE), for further improving the DH prediction performance. DWE is a two layer ensemble approach. The first generates different meta models based on four base component models. The second layer employs a novel approach to predict the performances of all selected meta models and uses these predicted results to dynamically combine these models in a weighted scheme to produce final results. This is totally different to traditional ensemble approaches which only rely on fixed combinations of their components. To the best of our knowledge, DWE is also the first ensemble approach for diarrhoea prediction. Extensive experiments are conducted over all 55 provinces of Vietnam to demonstrate the performance of DWE and to reveal its important characteristics.</p

One-pot fabrication of magnetic biochar by FeCl3-activation of lotus seedpod and its catalytic activity towards degradation of Orange G

An advanced magnetic biochar (MBC) was facilely prepared via one-pot FeCl _3 -activation of lotus seedpod. Simultaneous carbonization, activation, and magnetization formed magnetic Fe _3 O _4 nanoparticles and nanowires over the biochar base. The specific surface area (S _BET ) and the total pore volume (V _total ) of MBC were 349 m ^2 g ^−1 and 0.31 cm ^3 g ^−1 , which were 2.0-fold and 3.9-fold higher than those of biochar, respectively. In addition, the saturation magnetization of MBC reached 6.94 emu g ^−1 , facilitating its magnetic separation and recovery. In heterogeneous Fenton-like catalytic oxidation, 0.40 g l ^−1 MBC decolorized 100% Orange G and reduced 58% COD by 350 ppm H _2 O _2 within 120 min. The degradation kinetics were calculated with different MBC samples and reactions followed pseudo-first-order kinetics with the highest rate constant of 0.034 min ^−1 . Moreover, the catalytic activity dropped by only 6.4% after four reuse cycles, with negligible iron leaching of 1.31–1.44 mg l ^−1 . Based on these results, MBC could be a low-cost, highly effective, and relatively stable catalyst for treating Orange G in wastewater

Preparation and Biological Properties of Platinum(II) Complex-Loaded Copolymer PLA-TPGS

A new nanodrug system containing bis(menthone thiosemicarbazonato) Platinum(II) complex (Pt-thiomen) encapsulated with the block copolymers polylactide-d-α-tocopheryl polyethylene glycol 1000 succinate (PLA-TPGS) was prepared by a modified solvent extraction/evaporation technique. The characteristics of the nanoparticles including surface morphology, size distribution, structure, and biological activities such as antimicrobial and cytotoxic activities were in vitro investigated. The spherical nanoparticles were around 50 nm in size with core-shell structure and narrow-size distribution. The encapsulated Pt-thiomen can avoid interaction with proteins in the blood plasma. The inhibitory activity of Pt-thiomen-loaded PLA-TPGS nanoparticles on the growth of some bacteria, fungi, and Hep-G2 cells suggests a possibility of developing PLA-TPGS-Pt-thiomen nanoparticles as one of the potential chemotherapeutic agents

Economic burden of venous thromboembolism in surgical patients: A propensity score analysis from the national claims database in Vietnam.

BACKGROUND:Venous thromboembolism (VTE) associated with surgery can cause serious comorbidities or death and imposes a substantial economic burden to society. The study examined VTE cases after surgery to determined how this condition imposed an economic burden on patients based on the national health insurance reimbursement database. Methods: This retrospective analysis adopted the public payer's perspective. The direct medical cost was estimated using data from the national claims database of Vietnam from Jan 1, 2017 to Sep 31, 2018. Adult patients who underwent surgeries were recruited for the study. Patients with a diagnostic code of up to 90 days after surgery were considered VTE cases with the outcome measure being the surgery-related costs within 90 days. RESULTS:The 90-day cost of VTE patients was found to be US$2,939. The rate of readmission increased by 5.4 times, the rate of outpatient visits increased by 1.8 times and total costs over 90 days in patients with VTE undergoing surgery increased by 2.2 times. Estimation using propensity score matching method showed that an increase of US$1,019 in the 90-day cost of VTE patients. CONCLUSION:The VTE-related costs can be used to assess the potential economic benefit and cost-savings from prevention efforts

Determining Key Research Areas for Healthier Diets and Sustainable Food Systems in Viet Nam

Vietnamese food systems are undergoing rapid transformation, with important implications for human and environmental health and economic development. Poverty has decreased, and diet quality and under-nutrition have improved significantly since the end of the Doi Moi reform period (1986-1993) as a result of Viet Nam opening its economy and increasing its regional and global trade. Yet poor diet quality is still contributing the triple burden of malnutrition, with 25 percent stunting among children under age 5, 26 percent and 29 percent of women and children, respectively, anemic, and 21 percent of adults overweight. Agricultural production systems have shifted from predominantly diverse smallholder systems to larger more commercialized and specialized systems, especially for crops, while the ‘meatification’ of the Vietnamese diet is generating serious trade-offs between improved nutrition and sustainability of the Vietnamese food systems. The food processing industry has developed rapidly, together with food imports, resulting in new and processed food products penetrating the food retail outlets, trending towards an increase in the Westernized consumption patterns that are shifting nutrition-related problems towards overweight and obesity and, with it, an increase of non-communicable disease-related health risks. While regulatory policies exist across the food system, these are not systematically implemented, making food safety a major concern for consumers and policy makers alike. Where data exists, it is not easy to aggregate with data from across food system dimensions, making it difficult for Viet Nam to make an informed analysis of current and potential food system trade-offs. In our research, we reviewed existing literature and data, and applied a food systems framework to develop an initial food systems profile for Viet Nam and to identify a comprehensive set a of research questions to fill current data gaps identified through the review. Insights on these would provide the comprehensive evidence needed to inform policy makers on how to develop new food systems policies for Viet Nam, and further refine and improve existing policies to achieve better quality diets and more sustainable food systems in Viet Nam. Based on these, we then engaged with stakeholders to develop research priorities in the Viet Nam context and identified 25 priority research questions. This paper aims to stimulate such reflections by clearly outlining key areas for research, government policy, and development programs on priority investment to build the evidence base around inclusive food systems interventions that aim to result in healthier diets and more sustainable food systems for Viet Nam. <br/

Determining Key Research Areas for Healthier Diets and Sustainable Food Systems in Viet Nam

Vietnamese food systems are undergoing rapid transformation, with important implications for human and environmental health and economic development. Poverty has decreased, and diet quality and under-nutrition have improved significantly since the end of the Doi Moi reform period (1986-1993) as a result of Viet Nam opening its economy and increasing its regional and global trade. Yet poor diet quality is still contributing the triple burden of malnutrition, with 25 percent stunting among children under age 5, 26 percent and 29 percent of women and children, respectively, anemic, and 21 percent of adults overweight. Agricultural production systems have shifted from predominantly diverse smallholder systems to larger more commercialized and specialized systems, especially for crops, while the ‘meatification’ of the Vietnamese diet is generating serious trade-offs between improved nutrition and sustainability of the Vietnamese food systems. The food processing industry has developed rapidly, together with food imports, resulting in new and processed food products penetrating the food retail outlets, trending towards an increase in the Westernized consumption patterns that are shifting nutrition-related problems towards overweight and obesity and, with it, an increase of non-communicable disease-related health risks. While regulatory policies exist across the food system, these are not systematically implemented, making food safety a major concern for consumers and policy makers alike. Where data exists, it is not easy to aggregate with data from across food system dimensions, making it difficult for Viet Nam to make an informed analysis of current and potential food system trade-offs. In our research, we reviewed existing literature and data, and applied a food systems framework to develop an initial food systems profile for Viet Nam and to identify a comprehensive set a of research questions to fill current data gaps identified through the review. Insights on these would provide the comprehensive evidence needed to inform policy makers on how to develop new food systems policies for Viet Nam, and further refine and improve existing policies to achieve better quality diets and more sustainable food systems in Viet Nam. Based on these, we then engaged with stakeholders to develop research priorities in the Viet Nam context and identified 25 priority research questions. This paper aims to stimulate such reflections by clearly outlining key areas for research, government policy, and development programs on priority investment to build the evidence base around inclusive food systems interventions that aim to result in healthier diets and more sustainable food systems for Viet Nam

Deep learning models for forecasting dengue fever based on climate data in Vietnam

BackgroundDengue fever (DF) represents a significant health burden in Vietnam, which is forecast to worsen under climate change. The development of an early-warning system for DF has been selected as a prioritised health adaptation measure to climate change in Vietnam.ObjectiveThis study aimed to develop an accurate DF prediction model in Vietnam using a wide range of meteorological factors as inputs to inform public health responses for outbreak prevention in the context of future climate change.MethodsConvolutional neural network (CNN), Transformer, long short-term memory (LSTM), and attention-enhanced LSTM (LSTM-ATT) models were compared with traditional machine learning models on weather-based DF forecasting. Models were developed using lagged DF incidence and meteorological variables (measures of temperature, humidity, rainfall, evaporation, and sunshine hours) as inputs for 20 provinces throughout Vietnam. Data from 1997-2013 were used to train models, which were then evaluated using data from 2014-2016 by Root Mean Square Error (RMSE) and Mean Absolute Error (MAE).Results and discussionLSTM-ATT displayed the highest performance, scoring average places of 1.60 for RMSE-based ranking and 1.95 for MAE-based ranking. Notably, it was able to forecast DF incidence better than LSTM in 13 or 14 out of 20 provinces for MAE or RMSE, respectively. Moreover, LSTM-ATT was able to accurately predict DF incidence and outbreak months up to 3 months ahead, though performance dropped slightly compared to short-term forecasts. To the best of our knowledge, this is the first time deep learning methods have been employed for the prediction of both long- and short-term DF incidence and outbreaks in Vietnam using unique, rich meteorological features.ConclusionThis study demonstrates the usefulness of deep learning models for meteorological factor-based DF forecasting. LSTM-ATT should be further explored for mitigation strategies against DF and other climate-sensitive diseases in the coming years