Multimorbidity and its social determinants among older people in southern provinces, Vietnam

Background: Developing countries are poorly equipped for health issues related to ageing populations making multimorbidity challenging. As in Vietnam the focus tends to be on single conditions. Hence little is known about burden of multimorbidity. This study aimed to examine the prevalence and the determinants of multimorbidity among older people in Southern Vietnam. Methods: A cross-sectional study was conducted in two provinces of Southern Vietnam with a sample of 2400 people aged 60 years and older. The presence of chronic disease was ascertained by medical examination done by physicians at commune health stations. Information on social and demographic factors was collected using structured questionnaire. Univariate and multivariable logistic regression analyses were used to examine the factors associated with multimorbidity. Results: Nearly 40 % of older people had multimorbidity. Currently not working, and healthcare utilisation were associated with higher prevalence of multimorbidity. Living in urban areas and being literate were associated with lower prevalence of multimorbidity. Conclusion: The study found a high burden of multimorbidity among illiterate, especially those living in rural areas. This highlights the need for targeted community based programs aimed at reducing the burden of chronic disease

Aquatic toxicity prediction of diverse pesticides on two algal species using QSTR modeling approach

With the aim of identification of toxic nature of the diverse pesticides on the aquatic compartment, a large dataset of pesticides (n = 325) with experimental toxicity data on two algal test species (Pseudokirchneriella subcapitata (PS) (synonym: Raphidocelis subcapitata, Selenastrum capricornutum) and Scenedemus subspicatus (SS)) was gathered and subjected to quantitative structure toxicity relationship (QSTR) analysis to predict aquatic toxicity of pesticides. The QSTR models were developed by multiple linear regressions (MLRs), and the genetic algorithm (GA) was used for the variable selection. The developed GA-MLR models were statistically robust enough internally (Q2LOO = 0.620–0.663) and externally (Q2Fn = 0.693–0.868, CCCext = 0.843–0.877). The leverage approach of applicability domain (AD) and prediction reliability indicator assured the reliability of the developed models. The mechanistic interpretation highlighted that the presence of SO2, F and aromatic rings influenced the toxicity of pesticides towards PS species while the presence of alkyl, alkyl halide, aromatic rings and carbonyl was responsible for the toxicity of pesticides towards SS species. Additionally, we have reported the application of developed models to pesticides without experimental value and the cumulative toxicity of pesticides on the aquatic environment by using principal component analysis (PCA). The reliable prediction and prioritization of toxic compounds from the developed models will be useful in the aquatic toxicity assessment of pesticides. Graphical abstract: [Figure not available: see fulltext.

Multioutput Adaptive Neuro-Fuzzy Inference System Based Modeling of Heated Catalytic Converter Performance

ABSTRACT: Catalytic converters are the most effective means of reducing pollutant emissions from internal combustion engines under normal operating conditions. But the future emission requirements cannot be met by three way catalysts (TWC) as they cannot effectively remove hydrocarbon (HC) and carbon monoxide (CO) emissions from the outlet of internal combustion engines in the cold-start phase. Therefore, significant efforts have been put in improving the cold-start behavior of catalytic converters. In the experimental study, to improve cold-start performance of catalytic converter for HC and CO, a burner heated catalyst (BHC) has been tested in a four stroke, spark ignition engine. The modeling of catalytic converter performance of the engine during cold start is a difficult task. It involves complicated heat transfer and processes and chemical reactions at both the catalytic converter and exhaust pipe. In this study, to overcome these difficulties, multi-output adaptive neuro-fuzzy inference system (M-ANFIS) is used for prediction of catalyst temperature, HC emissions and CO emissions. The training data for M-ANFIS is obtained from experimental measurements. In comparison of performance analysis of M-ANFIS the deviation coefficients of standard and heated catalyst temperature, standard and heated catalyst HC emissions, and standard and heated catalyst CO emissions for the test conditions are less than 4.825%, 1.502%, 4.801%, 4.725%, 4.79% and 4.898%, respectively. The statistical coefficient of multiple determinations for the investigated cases is about 0.9981-0.9998. The degree of accuracy is acceptable in predicting the parameters of the system. So, it can be concluded that M-ANFIS provides a feasible method in predicting the system parameters. In this paper we propose a new type of multi output adaptive neuro-fuzzy inference system (M-ANFIS) with several outputs. To proves its performances, the proposed multi output ANFIS is used to make the approximation at the same time of three different functions. Simulation results show that this neuro-fuzzy system can approximate, with the desired precision, these three functions

A key review on oxadiazole analogs as potential methicillin-resistant Staphylococcus aureus (MRSA) activity: structure-activity relationship studies

Methicillin-resistant Staphylococcus aureus (MRSA) is becoming dangerous to human beings due to easy transmission mode and leading to the difficult-to-treat situation. The rapid resistance development of MRSA to many approved antibiotics is of major concern. There is a lot of scope to develop novel, efficient, specific, and nontoxic drug candidates to fight against MRSA isolates. The interesting molecular structure and adaptable feature of oxadiazole moiety which are bioisosteres of esters and amides, and these functional groups show improved resistance to esterases mediated hydrolytic cleavage, attracting researchers to develop required novel antibiotics based on oxadiazole core. This review summarizes the developments of oxadiazole-containing derivatives as potent antibacterial agents against multidrug-resistant MRSA strains and discussing the structure-activity relationship (SAR) in various directions. The current survey is the highlight of the present scenario of oxadiazole hybrids on MRSA studies, covering articles published from 2011 to 2020. This collective information may become a good platform to plan and develop new oxadiazole-based small molecule growth inhibitors of MRSA with minimal side effects. (C) 2021 Elsevier Masson SAS. All rights reserved