Colorectal cancer is a leading cancer worldwide and in Vietnam. Adenomas (adenomatous polyps) is an important precursor of colorectal cancer. There is currently no study to determine the modifiable ...risk factors for colorectal adenomas, including body mass index (BMI) in Vietnam. We conducted an individually matched case‐control study of 1149 colorectal adenomas and 1145 controls in a large‐scale colorectal screening program involving 103 542 individuals aged 40‐75 years old in Hanoi, Vietnam. Conditional logistic regression was used to evaluate the association between BMI and colorectal adenomas prevalence, after controlling for potential confounders. Overall, comparing to normal weight (ie, 18.5‐22.9 kg/m2), underweight (ie, BMI < 18.5) was associated with a non‐statistically significant increased prevalence of colorectal adenomas (odd ratio OR = 1.29 and 95% confident interval CI: 0.88‐1.87). This association became significant among male (OR = 1.98, 95% CI: 1.20‐3.27), male who were ever smokers (OR = 2.59, 95% CI: 1.33‐5.03), nonregular exercise (OR = 2.44, 95% CI: 1.26‐4.73) and individuals with cardiometabolic disorders (OR = 3.46, 95% CI: 1.19‐10.00). The association between underweight and colorectal adenomas did not vary by smoking status, drinking status, family history of cancer, adenomas types or cardiometabolic disorders. No association was observed among obese individuals (BMI ≥ 25). In the population with low prevalence of obesity, we found that the association between BMI and colorectal adenomas followed a reversed J‐shape that underweight was associated with increased prevalence. Further studies are, therefore, warranted to replicate our results and to investigate the biologic mechanism the effect of underweight on colorectal adenomas prevalence.
What's new?
High body mass index (BMI) has been associated with an increased risk of colorectal adenomas in studies including Asian populations. To date, no study has identified modifiable risk factors for colorectal adenomas in Vietnam, a country with a rising colorectal cancer incidence and low obesity prevalence. This case‐control study based on a large‐scale colorectal cancer screening program in Vietnam reports a potential association between underweight and colorectal adenomas prevalence with an inverse J‐shape. The modification of specific factors such as maintaining a normal BMI range, quitting smoking and exercising regularly showed great potential for colorectal cancer prevention and control programs.
Antibiotic residues and antimicrobial resistance in surface water are issues of global concern, especially in developing countries. In this study, the occurrence of seven antibiotics and one ...antiparasitic agent was determined in surface water samples collected from four rivers running through Hanoi urban area in the Red River Delta, northern Vietnam. The pharmaceuticals in water samples were analyzed by solid-phase extraction combined with liquid chromatography–tandem mass spectrometry method. The concentrations of pharmaceuticals in our samples ranged from 3050 to 16,700 (median 7800) ng/L, which were generally higher than levels found in river water from many other locations in the world. Amoxicillin, oxfendazole, and lincomycin were the most dominant and frequently detected compounds (detection rate 100%), which together accounted for 76 ± 14% of total concentrations. Sulfacetamide and sulfamethoxazole were detected at moderate concentrations in more than two-thirds of the analyzed samples. The remaining antibiotics (i.e., azithromycin, ciprofloxacin, and ofloxacin) were found at lower detection frequency and concentrations. Antibiotic concentrations in the water samples were not significantly different between the investigated rivers. Meanwhile, levels of pharmaceuticals in the samples collected in February 2020 were higher than those found in the remaining samples, largely due to the sharp decrease in sulfamethoxazole and azithromycin concentrations of the samples collected in March and April. Considerable ecological risks of antibiotics in surface water were estimated for some compounds such as amoxicillin, ciprofloxacin, and ofloxacin.
Load identification is a core concept in non-intrusive load monitoring (NILM). Through NILM systems, users can check their home appliance usage habits and then adjust their behavior to save ...electricity. In this way, a NILM system offers an effective method to detect the event status of household appliances as well as individual loads' energy consumption. However, prior NILM methods have encountered a challenge in improving recognition accuracy for both linear load and non-linear load types. These methods used a representative feature, namely transient load signals. However, the transient signals on these loads differ in terms of transient time and transient shape, which is the main cause of reduced accuracy performance in load identification. To this end, this paper presents a novel method, HT-LSTM (Hilbert Transform Long Short-Term Memory), which enhances recognition of the various load types that contain the difference in the transient time and the transient shape of any load signal. The proposed method consists of two main parts: (i) generating a novel transient feature based on a Hilbert transform (HT), called APF (Amplitude-Phase-Frequency). APF features are sequential data, which is used for the classification model; and (ii) applying Sequence-to-Sequence Long Short-Term Memory (Seq2Seq LSTM) to identify appliances by using APF features as the input data. In this work, we evaluate the HT-LSTM method using two high-frequency public datasets, Building-Level fUlly-labeled dataset for Electricity Disaggregation (BLUED) and Plug Load Appliance Identification Dataset (PLAID). Also, we evaluate our method using a private dataset collected in the lab. Based on the experimental results obtained and comparison classification performance pointed, the proposed method outperforms previous methods of F-score measurement on both public datasets in load identification as well as the private dataset.
Type 2 diabetes mellitus (T2DM) has been increasing rapidly in Vietnam as well as world-wide. One of the major causes of the condition is low fiber intake. It is difficult to eat large amounts of ...vegetables every day to reach a sufficient amount of fiber but Textured Soybean Protein is rich in fiber. The study aimed to examine the effectiveness of Textured Soybean Protein consumption on T2DM patients. In this randomized controlled trial, 47 T2DM patients were divided into an intervention group (n=24) and a control group (n=23). The intervention group were asked to consume 40 g Textured Soybean Protein in 2 dishes for 4 wk. The control group continued their usual diet. Fasting blood samples were drawn before and after intervention to measure fasting plasma glucose (FPG), fructosamine, low density lipoprotein-cholesterol (LDL-C), high density lipoprotein-cholesterol (HDL-C), total cholesterol (T-C), and triglycerides (TG). A 3-day food record was conducted at 1 wk before (baseline) and at the last week (final) of the intervention period. In the Textured Soybean Protein consumption group, there was a significant decrease in fructosamine (363±86 μmol/L to 347±82 μmol/L, p=0.03), T-C (5.2±0.9 mmol/L to 4.8±0.8 mmol/L, p=0.02) and TG (3.5±2.2 mmol/L to 2.8±2.0 mmol/L, p=0.02). Total energy intake in the two groups did not change significantly. There was a shift in the dietary pattern of the Textured Soybean Protein consumption group; lipid intake showed a significant decrease (p=0.001) and fiber intake increased by 6 g (p<0.001). The consumption of Textured Soybean Protein in the diet could have favorable effects in improving glycemic and lipid concentrations in T2DM patients.
BACKGROUND: Achieving access to clean water and basic sanitation remains as major challenges in Vietnam, especially for vulnerable groups such as minority people, despite all the progress made by the ...Millennium Development Goal number 7.C. OBJECTIVES: The study aimed to describe the access to improved water sources and sanitation of the ethnic minority people in Vietnam based on a national survey and to identify associated factors. METHODS: A cross-sectional study was conducted in 2019 with a sample size of 1385 ethnic minority households in 12 provinces in Vietnam. Multivariate logistic regression modeling was performed to examine the probability of having access to improved water sources and sanitation and sociodemographic status at a significance level of P < .05. RESULTS: The access to improved water sources and sanitation was unequal among the ethnic minority people in Vietnam, with the lowest access rate in the northern midland and mountainous and Central Highland areas and the highest access rate in the Mekong Delta region. Some sociodemographic variables that were likely to increase the ethnic minority people’s access to improved water sources and/or sanitation included older age, female household heads, household heads with high educational levels, religious households, and households in not poor status. CONCLUSION AND RECOMMENDATIONS: The study suggested more emphasis on religion for improving the ethnic minority’s access to improved water sources and sanitation. Besides, persons of poor and near-poor status and with low educational levels should be of focus in future water and sanitation intervention programs.
In recent years, many methods for intrusion detection systems (IDS) have been designed and developed in the research community, which have achieved a perfect detection rate using IDS datasets. Deep ...neural networks (DNNs) are representative examples applied widely in IDS. However, DNN models are becoming increasingly complex in model architectures with high resource computing in hardware requirements. In addition, it is difficult for humans to obtain explanations behind the decisions made by these DNN models using large IoT-based IDS datasets. Many proposed IDS methods have not been applied in practical deployments, because of the lack of explanation given to cybersecurity experts, to support them in terms of optimizing their decisions according to the judgments of the IDS models. This paper aims to enhance the attack detection performance of IDS with big IoT-based IDS datasets as well as provide explanations of machine learning (ML) model predictions. The proposed ML-based IDS method is based on the ensemble trees approach, including decision tree (DT) and random forest (RF) classifiers which do not require high computing resources for training models. In addition, two big datasets are used for the experimental evaluation of the proposed method, NF-BoT-IoT-v2, and NF-ToN-IoT-v2 (new versions of the original BoT-IoT and ToN-IoT datasets), through the feature set of the net flow meter. In addition, the IoTDS20 dataset is used for experiments. Furthermore, the SHapley additive exPlanations (SHAP) is applied to the eXplainable AI (XAI) methodology to explain and interpret the classification decisions of DT and RF models; this is not only effective in interpreting the final decision of the ensemble tree approach but also supports cybersecurity experts in quickly optimizing and evaluating the correctness of their judgments based on the explanations of the results.
Abstract
Background
Colorectal cancer is a leading cancer incidence and cause of death worldwide and in Vietnam. Although screening is considered an effective measure to prevent and control ...colorectal cancer, there is no such effort in Vietnam.
Methods
Between 01 January 2018 and 31 October 2019, a population-based colorectal cancer screening program was conducted in Hanoi, Vietnam. A health advocacy campaign and follow-up phone calls were used to enroll residents aged ≥40 years old to complete an immunochemical-fecal occult blood testing. Positive immunochemical-fecal occult blood testing was followed by a colonoscopy. We also conducted a systematic review of the colorectal cancer screening programs in the Asia-Pacific region that used similar approach by searching Ovid Medline and PubMed databases.
Results
During study period, 103 542 individuals among 672 742 eligible residents attended the screening of whom 81.5% participants finished immunochemical-fecal occult blood testing test and the positive rate was 6.1%. The coverage rate for immunochemical-fecal occult blood testing test was 11.9%. Among 2278 individuals who underwent colonoscopy, 3.5% were histologically diagnosed with cancer, 17.8% with advanced adenomas, and 23.1% with non-advanced adenomas. Males had significantly higher detection rate of advanced adenomas, cancer or ≥ two polyps/tumor than females (P < 0.0001). The systematic review showed that in two-step modality (i.e. immunochemical-fecal occult blood testing/fecal immunochemical test and colonoscopy), the test positive was from 4.1 to 10.6%. Once colonoscopy was performed subsequently, the rate of cancer among positive participants was from 1.7 to 16.4% and that of advanced adenomas was from 7.1 to 23.1%.
Conclusion
We showed that the two-step modality is a promising strategy for colorectal cancer screening in Vietnam that might apply to similar settings with limited resources
This report described the first-ever population-based colorectal cancer screening program in 103 542 individuals in Vietnam, using immunochemical-fecal occult blood testing test and subsequent colonoscopy among those with positive test results of immunochemical-fecal occult blood testing.
The Industrial Internet of Things (IIoT) has advanced digital technology and the fastest interconnection, which creates opportunities to substantially grow industrial businesses today. Although IIoT ...provides promising opportunities for growth, the massive sensor IoT data collected are easily attacked by cyber criminals. Hence, IIoT requires different high security levels to protect the network. An Intrusion Detection System (IDS) is one of the crucial security solutions, which aims to detect the network’s abnormal behavior and monitor safe network traffic to avoid attacks. In particular, the effectiveness of the Machine Learning (ML)-based IDS approach to building a secure IDS application is attracting the security research community in both the general cyber network and the specific IIoT network. However, most available IIoT datasets contain multiclass output data with imbalanced distributions. This is the main reason for the reduction in the detection accuracy of attacks of the ML-based IDS model. This research proposes an IDS for IIoT imbalanced datasets by applying the eXtremely Gradient Boosting (XGBoost) model to overcome this issue. Two modern IIoT imbalanced datasets were used to assess our proposed method’s effectiveness and robustness, X-IIoTDS and TON_IoT. The XGBoost model achieved excellent attack detection with F1 scores of 99.9% and 99.87% on the two datasets. This result demonstrated that the proposed approach improved the detection attack performance in imbalanced multiclass IIoT datasets and was superior to existing IDS frameworks.
Multi-drug resistance to antibiotics represents a growing challenge in treating infectious diseases. Outside the hospital, bacteria with the multi-drug resistance (MDR) phenotype have an increased ...prevalence in anthropized environments, thus implying that chemical stresses, such as metals, hydrocarbons, organic compounds, etc., are the source of such resistance. There is a developing hypothesis regarding the role of metal contamination in terrestrial and aquatic environments as a selective agent in the proliferation of antibiotic resistance caused by the co-selection of antibiotic and metal resistance genes carried by transmissible plasmids and/or associated with transposons. Efflux pumps are also known to be involved in either antibiotic or metal resistance. In order to deal with these situations, microorganisms use an effective strategy that includes a range of expressions based on biochemical and genetic mechanisms. The data from numerous studies suggest that heavy metal contamination could affect the dissemination of antibiotic-resistant genes. Environmental pollution caused by anthropogenic activities could lead to mutagenesis based on the synergy between antibiotic efficacy and the acquired resistance mechanism under stressors. Moreover, the acquired resistance includes plasmid-encoded specific efflux pumps. Soil microbiomes have been reported as reservoirs of resistance genes that are available for exchange with pathogenic bacteria. Importantly, metal-contaminated soil is a selective agent that proliferates antibiotic resistance through efflux pumps. Thus, the use of multi-drug efflux pump inhibitors (EPIs) originating from natural plants or synthetic compounds is a promising approach for restoring the efficacy of existing antibiotics, even though they face a lot of challenges.
Nowadays climate change problems have been more and more concerns and urgent in the real world. Especially, the energy power consumption monitoring is a considerate trend having positive effects in ...decreasing affecting climate change. Non-Intrusive Load Monitoring (NILM) is the best economic solution to solve the electrical consumption monitoring issue. NILM captures the electrical signals from the aggregate energy consumption, feature extraction from these signals and then learning and predicting the switch ON/OFF of appliances used these feature extracted. This paper proposed a NILM framework including data acquisition, data feature extraction, and classification model. The main contribution is to develop a new transient signal in a different aspect. The proposed transient signal is extracted from the active power signal in the low-frequency sampling rate. This transient signal is used to detect the event of household appliances. In household appliances event detection, we applied to Decision Tree and Long Short-Time Memory (LSTM) models. The average accuracies of these models achieved 92.64% and 96.85%, respectively. The computational and result experiments present the solution effectiveness for the accurate transient signal extraction in the electrical input signals.
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