EVALUATION OF ACUTE AND SUB-ACUTE ORAL TOXICITY OF CLINACANTHUS NUTANS LEAVES EXTRACT IN MICE

Objective: This study aimed to evaluate acute and sub-acute oral toxicity of ethanol extract of Clinacanthus nutans leaves in Swiss mice. Methods: Acute oral toxicity study was performed as per OECD-423 guidelines. Sub-acute oral toxicity study was performed as per OECD-407 guidelines. The extract was dissolved in 10% dimethyl sulfoxide and administered orally, while the control group received only the vehicle. Results: The acute oral toxicity test on mice showed that this extract was well tolerated up to LD50 5000 mg/kg body weight/day oral dosage level and non-toxic to mice under the present experimental conditions. The sub-acute toxicity study was carried out on mice with the oral dosage of the extract from 100 mg/kg–500 mg/kg body weight/day and 5000 mg/kg body weight/day for 28 d. The results showed that this extract did not induce death or adverse effects in activity, feed consumption or body weight gain. There were not significant changes in heamotological and biochemical parameters between control and experiment groups. Conclusion: Thus, Clinacanthus nutans leaf has a very low toxicity value

Alumni survey of Masters of Public Health (MPH) training at the Hanoi School of Public Health

© 2007 Le et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licens

Quantum Chemistry–Machine Learning Approach for Predicting Properties of Lewis Acid–Lewis Base Adducts

Synthetic design allowing predictive control of charge transfer and other optoelectronic properties of Lewis acid adducts remains elusive. This challenge must be addressed through complementary methods combining experimental with computational insights from first principles. Ab initio calculations for optoelectronic properties can be computationally expensive and less straightforward than those sufficient for simple ground-state properties, especially for adducts of large conjugated molecules and Lewis acids. In this contribution, we show that machine learning (ML) can accurately predict density functional theory (DFT)-calculated charge transfer and even properties associated with excited states of adducts from readily obtained molecular descriptors. Seven ML models, built from a dataset of over 1000 adducts, show exceptional performance in predicting charge transfer and other optoelectronic properties with a Pearson correlation coefficient of up to 0.99. More importantly, the influence of each molecular descriptor on predicted properties can be quantitatively evaluated from ML models. This contributes to the optimization of a priori design of Lewis adducts for future applications, especially in organic electronics

Status Poles and Status Zoning to Model Residential Land Prices: Status-Quality Trade off Theory (Short Paper)

This study describes an approach for augmenting urban residential preference and hedonic house price models by incorporating Status-Quality Trade Off theory (SQTO). SQTO seeks explain the dynamic of urban structure using a multipolar, in which the location and strength of poles is driven by notions of residential status and dwelling quality. This paper presents in outline an approach for identifying status poles and for quantifying their effect on land and residential property prices. The results show how the incorporation of SQTO results in an enhanced understanding of variations in land / property process with increased spatial nuance. A number of future research areas are identified related to the status pole weights and the development of status pole index

Effectiveness of perindopril/amlodipine fixed-dose combination in the treatment of hypertension: a systematic review

Background: Uncontrolled blood pressure is a major risk factor for cardiovascular diseases. Fixed-dose combination (FDC) therapy offers a promising approach to addressing this challenge by providing a convenient single-tablet solution that enhances the effectiveness of blood pressure control. In our systematic review, we assess the effectiveness of perindopril/amlodipine FDC in managing blood pressure.Methods: We conducted a comprehensive search across four primary electronic databases, namely, PubMed, Virtual Health Library (VHL), Global Health Library (GHL), and Google Scholar, as of 8 February 2022. Additionally, we performed a manual search to find relevant articles. The quality of the selected articles was evaluated using the Study Quality Assessment Tools (SQAT) checklist from the National Institute of Health and the ROB2 tool from Cochrane.Results: Our systematic review included 17 eligible articles. The findings show that the use of perindopril/amlodipine FDC significantly lowers blood pressure and enhances the quality of blood pressure control. Compared to the comparison group, the perindopril/amlodipine combination tablet resulted in a higher rate of blood pressure response and normalization. Importantly, perindopril/amlodipine FDC contributes to improved patient adherence with minimal side effects. However, studies conducted to date have not provided assessments of the cost-effectiveness of perindopril/amlodipine FDC.Conclusion: In summary, our analysis confirms the effectiveness of perindopril/amlodipine FDC in lowering blood pressure, with combination therapy outperforming monotherapy and placebo. Although mild adverse reactions were observed in a small subset of participants, cost-effectiveness assessments for this treatment remain lacking in the literature

Effects of water scarcity awareness and climate change belief on recycled water usage willingness: Evidence from New Mexico, United States

The global water crisis is being exacerbated by climate change, even in the United States. Recycled water is a feasible alternative to alleviate the water shortage, but it is constrained by humans’ perceptions. The current study examines how residents’ water scarcity awareness and climate change belief influence their willingness to use recycled water directly and indirectly. Bayesian Mindsponge Framework (BMF) analytics was employed on a dataset of 1831 residents in Albuquerque, New Mexico, an arid inland region in the US. We discovered that residents’ willingness to use direct recycled potable water is positively affected by their awareness of water scarcity, but the effect is conditional on their belief in the impacts of climate change on the water cycle. Meanwhile, the willingness to use indirect recycled potable water is influenced by water scarcity awareness, and the belief in climate change further enhances this effect. These findings implicate that fighting climate change denialism and informing the public of the water scarcity situation in the region can contribute to the effectiveness and sustainability of long-term water conservation and climate change alleviation efforts

DQRA: Deep Quantum Routing Agent for Entanglement Routing in Quantum Networks

Quantum routing plays a key role in the development of the next-generation network system. In particular, an entangled routing path can be constructed with the help of quantum entanglement and swapping among particles (e.g., photons) associated with nodes in the network. From another side of computing, machine learning has achieved numerous breakthrough successes in various application domains, including networking. Despite its advantages and capabilities, machine learning is not as much utilized in quantum networking as in other areas. To bridge this gap, in this article, we propose a novel quantum routing model for quantum networks that employs machine learning architectures to construct the routing path for the maximum number of demands (source-destination pairs) within a time window. Specifically, we present a deep reinforcement routing scheme that is called Deep Quantum Routing Agent (DQRA). In short, DQRA utilizes an empirically designed deep neural network that observes the current network states to accommodate the network\u27s demands, which are then connected by a qubit-preserved shortest path algorithm. The training process of DQRA is guided by a reward function that aims toward maximizing the number of accommodated requests in each routing window. Our experiment study shows that, on average, DQRA is able to maintain a rate of successfully routed requests at above 80% in a qubit-limited grid network and approximately 60% in extreme conditions, i.e., each node can be repeater exactly once in a window. Furthermore, we show that the model complexity and the computational time of DQRA are polynomial in terms of the sizes of the quantum networks

An Efficient Hybrid Webshell Detection Method for Webserver of Marine Transportation Systems

An increase in the number of Maritime Intelligent Transport Systems (MITSs) also means an increase in the number of information security risks. Usually, the administration and operation of MITSs are done through web servers that are frequently targeted by hackers. In marine transportation industry, malicious code injection attacks (webshell) has been widely exploited by hackers to take full control of Web servers. Traditional webshell detection methods based on pattern matching that are no longer effective against new types of webshell. This motivates us to investigate the problem of detecting obfuscation or unknown webshells, termed OUW problem. In this work, we propose a pattern-matching-deep-learning hybrid ASP.NET webshell detection method (H-DLPMWD) to address the OUW problem. H-DLPMWD is based on Yara-based pattern matching to clean dataset; modeling ASP.NET code files as an operation code index (OCI) vectors; and applying CNN method to train and predict webshell in OCI vectors. To validate H-DLPMWD, our rigorous experimentation demonstrates that H-DLPMWD achieves an excellent accuracy of 98.49%, F1-score of 99.01%, and a low false positive rate of 1.75%

Adopting the Health Belief Model and Social Cognitive Theory Framework to Explore Factors Impacting STIs Prevention Behaviors Among Youth: A Case Study in Vietnam

Sexually transmitted infections (STIs) are an important health concern in Vietnam, with a consistently increasing prevalence, particularly in youths. These infections have significant consequences for personal health, longevity, and the welfare of society. Preventive measures against STIs among Vietnamese young have become common, bringing noteworthy challenges despite the importance of the circumstances. The authors evaluate the actual preventive measures used by this particular population for STIs. Our research explores the influences on STI prevention by combining the Social Cognitive Theory (SCT) with the Health Belief Model (HBM). We collected data from 835 respondents across various areas of Vietnam via SPSS 26.0 and SmartPLS 4.0 software. The results highlighted the key factors that influence STIs prevention and provided recommendations to enhance these preventive actions. The goal of this research is to reduce STIs rates and enhance sexual and reproductive health in young adults to ensure a better future. Keywords: STIs prevention behaviors, the Health Belief Model, Social Cognitive Theory, Youth in Vietnam DOI: 10.7176/JESD/15-3-01 Publication date:March 31st 202