| 1. |
- Azeem, Muhammad, et al.
(författare)
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Chemical composition and repellent activity of native plants essential against dengue mosquito, Aedes aegypti
- 2019
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Ingår i: Industrial crops and products (Print). - : Elsevier BV. - 0926-6690 .- 1872-633X. ; 140
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Tidskriftsartikel (refereegranskat)abstract
- Aedes aegypti L. (Diptera: Culicidae) mosquito is an important vector of chikungunya, dengue and yellow fever. Plant based essential oils may serve as good alternatives to commercially available mosquito repellent, DEET. Steam distillation was used for the extraction of essential oils from fresh collected aerial parts of plants viz Chenopodium ambrosioides, Conyza sumatrensis, Erigeron canadensis, Eucalyptus camaldulensis, Mentha spicata, Parthenium hysterophorus, and Tagetes minuta. The essential oils were tested for mosquito repellent activity against laboratory reared female Ae. aegypti by human bait technique. Identification of chemical constituents of essential oils was carried out by gas chromatography-mass spectrometry (GC-MS). The essential oil of M. spicata, E. canadensis, P. hysterophorus, C. sumatrensis, T. minuta, C. ambrosioides, and E. camaldulensis exhibited 100%, 80%, 63.9%, 51.4%, 50.2%, 39.7%, and 13.7% mosquito repellent activity respectively, at the tested dose of 30 mu g/cm(2). The most abundant constituents of M. spicata, E. canadensis, P. hysterophorus, C. sumatrensis and T. minuta essential oils were piperitenone oxide (47.1%), limonene (41.3%), germacrene D (36.6%), cis-lachnophyllum ester (33.3%) and dihydrotagetone (20.9%) respectively. M. spicata essential oil completely inhibited the attractiveness of human hands toward female mosquitoes for more than 45 min thus showed bioactivity comparable to that of commercially used mosquito repellent, DEET. This study suggests that the dilute solution of M. spicata essential oil could be used as potent mosquito repellent against Ae. aegypti alternative to commercially available synthetic mosquito repellents.
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| 2. |
- Golsteijn, Laura, et al.
(författare)
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Assessing predictive uncertainty in comparative toxicity potentials of triazoles
- 2014
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Ingår i: Environmental Toxicology and Chemistry. - : Wiley. - 0730-7268 .- 1552-8618. ; 33:2, s. 293-301
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Tidskriftsartikel (refereegranskat)abstract
- Comparative toxicity potentials (CTPs) quantify the potential ecotoxicological impacts of chemicals per unit of emission. They are the product of a substance's environmental fate, exposure, and hazardous concentration. When empirical data are lacking, substance properties can be predicted. The goal of the present study was to assess the influence of predictive uncertainty in substance property predictions on the CTPs of triazoles. Physicochemical and toxic properties were predicted with quantitative structure-activity relationships (QSARs), and uncertainty in the predictions was quantified with use of the data underlying the QSARs. Degradation half-lives were based on a probability distribution representing experimental half-lives of triazoles. Uncertainty related to the species' sample size that was present in the prediction of the hazardous aquatic concentration was also included. All parameter uncertainties were treated as probability distributions, and propagated by Monte Carlo simulations. The 90% confidence interval of the CTPs typically spanned nearly 4 orders of magnitude. The CTP uncertainty was mainly determined by uncertainty in soil sorption and soil degradation rates, together with the small number of species sampled. In contrast, uncertainty in species-specific toxicity predictions contributed relatively little. The findings imply that the reliability of CTP predictions for the chemicals studied can be improved particularly by including experimental data for soil sorption and soil degradation, and by developing toxicity QSARs for more species. (c) 2013 SETAC
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| 3. |
- Iqbal, Muhammad Sarfraz, et al.
(författare)
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Description and Propagation of Uncertainty in Input Parameters in Environmental Fate Models
- 2013
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Ingår i: Risk Analysis. - : Wiley. - 0272-4332 .- 1539-6924. ; 33:7, s. 1353-66
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Tidskriftsartikel (refereegranskat)abstract
- Today, chemical risk and safety assessments rely heavily on the estimation of environmental fate by models. The key compound-related properties in such models describe partitioning and reactivity. Uncertainty in determining these properties can be separated into random and systematic (incompleteness) components, requiring different types of representation. Here, we evaluate two approaches that are suitable to treat also systematic errors, fuzzy arithmetic, and probability bounds analysis. When a best estimate (mode) and a range can be computed for an input parameter, then it is possible to characterize the uncertainty with a triangular fuzzy number (possibility distribution) or a corresponding probability box bound by two uniform distributions. We use a five-compartment Level I fugacity model and reported empirical data from the literature for three well-known environmental pollutants (benzene, pyrene, and DDT) as illustrative cases for this evaluation. Propagation of uncertainty by discrete probability calculus or interval arithmetic can be done at a low computational cost and gives maximum flexibility in applying different approaches. Our evaluation suggests that the difference between fuzzy arithmetic and probability bounds analysis is small, at least for this specific case. The fuzzy arithmetic approach can, however, be regarded as less conservative than probability bounds analysis if the assumption of independence is removed. Both approaches are sensitive to repeated parameters that may inflate the uncertainty estimate. Uncertainty described by probability boxes was therefore also propagated through the model by Monte Carlo simulation to show how this problem can be avoided.
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| 4. |
- Iqbal, Muhammad Sarfraz
(författare)
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Environmental Modeling and Uncertainty
- 2013
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Environmental fate models are used to evaluate the fate and effects of chemicals for risk assessment. Fate models may be effective and low-cost substitutes for field measurements and are helpful to project future scenarios. Environmental models describe processes for chemical fate largely determined by environmental and chemical-specific parameters. There is uncertainty in such input parameters arising from lack of knowledge and inherent variability in environmental processes. The objectives of this thesis are to demonstrate and evaluate ways to quantify, implement, and reduce uncertainty in chemical-specific input parameters and in the process to improve the overall treatment of uncertainty in environmental modeling. The methods to treat uncertainty were combinations of multimedia environmental modeling, the use of testing and non-testing information in risk assessment, and probabilistic and non-probabilistic measures of uncertainty. This thesis contains six case studies related to chemical safety assessment which illustrate different aspects of treatment of uncertainty in a regulatory context. Dependent on nature of uncertainty and the available information, the approaches to treat uncertainty were probabilistic, non-probabilistic or combinations of these. Some case studies were put into the perspective to support chemical regulation under REACH. In three studies, the contribution of uncertainty in input parameters was evaluated on characteristics of uncertainty in assessed persistence, long-range transport potential and comparative toxicity potentials of chemicals in the environment. In other studies, the focus was on decision making such as prioritization of chemicals for risk assessment and the need for further testing to reduce input uncertainty. The main contributions are useful applications of a broader treatment of uncertainty in environmental modeling that address gaps and quality in available background knowledge. Epistemic uncertainty is treated by filling knowledge gaps using non-testing information from QSARs. Uncertainty in non-testing information is given a probabilistic treatment based on statistical principles of inference. Poor quality of background knowledge, such as sparse data or low confidence in individual QSAR predictions, is treated by non-probabilistic measures. Finally, the suggested treatments of uncertainty are implemented and evaluated in the context of chemical risk assessment.
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| 5. |
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| 6. |
- Iqbal, Muhammad Sarfraz, et al.
(författare)
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Treatment of Epistemic Uncertainty in Environmental Fate Models –Consequences on Chemical Safety Regulatory Strategies
- 2012
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- The practical impact of treatment of epistemic uncertainty on decision making wasillustrated on two kinds of decisions from chemical regulation. First, regulatory strategies derivedfrom a simplified decision model based on toxicity and persistence showed that regulated level ofexposure is more conservative (safe) when uncertainty has been given a non-probabilistictreatment. Persistence and its uncertainty had been assessed by a Level II fugacity model forwhich input parameters had been quantified either by Bayesian probabilities, fuzzy numbers(non-probabilistic), or combinations of these (probability boxes). These findings are restricted tohow we let decision makers respond to uncertainty in model predictions by the chosen set ofdecision rules. Further, the use of either treatment depends on the quality and quantity ofbackground knowledge and the required level of detail on the assessment. In the absence ofexperimentally tested physicochemical endpoints, European chemical regulation REACH allowsthe use of non-testing strategies such as Quantitative Structure-Property Relationships (QSPR) topredict the required information. The second decision problem was to select which chemicalsubstances to prioritize for experimental testing in order to strengthen the background knowledgefor chemical regulation with respect to the uncertainty in QSPR predictions. We found that thevalue of reducing uncertainty, given by the expected gain in net benefit for society, was affectedby its treatment and there were no consistent order of testing of the three compounds. However,value of information is a Bayesian probabilistic approach that, unless developed further, loose itsinterpretability under other treatments of uncertainty. The framework of a predictive model, riskmodel, decision model and value of information analysis provides a computational template forfurther evaluation of the effect of treatment of uncertainty on decision making.
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| 7. |
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| 8. |
- Iqbal, Muhammad Sarfraz, et al.
(författare)
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Understanding quantitative structure-property relationships uncertainty in environmental fate modeling
- 2013
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Ingår i: Environmental Toxicology and Chemistry. - : Wiley. - 0730-7268 .- 1552-8618. ; 32:5, s. 1069-1076
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Tidskriftsartikel (refereegranskat)abstract
- In cases in which experimental data on chemical-specific input parameters are lacking, chemical regulations allow the use of alternatives to testing, such as in silico predictions based on quantitative structure–property relationships (QSPRs). Such predictions are often given as point estimates; however, little is known about the extent to which uncertainties associated with QSPR predictions contribute to uncertainty in fate assessments. In the present study, QSPR-induced uncertainty in overall persistence (POV) and long-range transport potential (LRTP) was studied by integrating QSPRs into probabilistic assessments of five polybrominated diphenyl ethers (PBDEs), using the multimedia fate model Simplebox. The uncertainty analysis considered QSPR predictions of the fate input parameters' melting point, water solubility, vapor pressure, organic carbon–water partition coefficient, hydroxyl radical degradation, biodegradation, and photolytic degradation. Uncertainty in POV and LRTP was dominated by the uncertainty in direct photolysis and the biodegradation half-life in water. However, the QSPRs developed specifically for PBDEs had a relatively low contribution to uncertainty. These findings suggest that the reliability of the ranking of PBDEs on the basis of POV and LRTP can be substantially improved by developing better QSPRs to estimate degradation properties. The present study demonstrates the use of uncertainty and sensitivity analyses in nontesting strategies and highlights the need for guidance when compounds fall outside the applicability domain of a QSPR.
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| 9. |
- Saeed, Amna, et al.
(författare)
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Discovery of Phenylcarbamoylazinane-1,2,4-Triazole Amides Derivatives as the Potential Inhibitors of Aldo-Keto Reductases (AKR1B1 & AKRB10) : Potential Lead Molecules for Treatment of Colon Cancer
- 2022
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Ingår i: Molecules. - : MDPI. - 1431-5157 .- 1420-3049. ; 27:13
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Tidskriftsartikel (refereegranskat)abstract
- Both members of the aldo-keto reductases (AKRs) family, AKR1B1 and AKR1B10, are over-expressed in various type of cancer, making them potential targets for inflammation-mediated cancers such as colon, lung, breast, and prostate cancers. This is the first comprehensive study which focused on the identification of phenylcarbamoylazinane-1, 2,4-triazole amides (7a-o) as the inhibitors of aldo-keto reductases (AKR1B1, AKR1B10) via detailed computational analysis. Firstly, the stability and reactivity of compounds were determined by using the Guassian09 programme in which the density functional theory (DFT) calculations were performed by using the B3LYP/SVP level. Among all the derivatives, the 7d, 7e, 7f, 7h, 7j, 7k, and 7m were found chemically reactive. Then the binding interactions of the optimized compounds within the active pocket of the selected targets were carried out by using molecular docking software: AutoDock tools and Molecular operation environment (MOE) software, and during analysis, the Autodock (academic software) results were found to be reproducible, suggesting this software is best over the MOE (commercial software). The results were found in correlation with the DFT results, suggesting 7d as the best inhibitor of AKR1B1 with the energy value of -49.40 kJ/mol and 7f as the best inhibitor of AKR1B10 with the energy value of -52.84 kJ/mol. The other potent compounds also showed comparable binding energies. The best inhibitors of both targets were validated by the molecular dynamics simulation studies where the root mean square value of <2 along with the other physicochemical properties, hydrogen bond interactions, and binding energies were observed. Furthermore, the anticancer potential of the potent compounds was confirmed by cell viability (MTT) assay. The studied compounds fall into the category of drug-like properties and also supported by physicochemical and pharmacological ADMET properties. It can be suggested that the further synthesis of derivatives of 7d and 7f may lead to the potential drug-like molecules for the treatment of colon cancer associated with the aberrant expression of either AKR1B1 or AKR1B10 and other associated malignancies.
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| 10. |
- Sahlin, Ullrika, et al.
(författare)
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Arguments for considering uncertainty in QSAR predictions in hazard and risk assessments
- 2013
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Ingår i: ATLA (Alternatives to Laboratory Animals). - : SAGE Publications. - 0261-1929. ; 41:1, s. 91-110
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Tidskriftsartikel (refereegranskat)abstract
- Chemical regulation allows non-in vivo testing (i.e. in silico-derived and in vitro-derived) information to replace experimental values from in vivo studies in hazard and risk assessments. Although non-in vitro testing information on chemical activities or properties is subject to added uncertainty as compared to in vivo testing information, this uncertainty is commonly not (fully) taken into account. Considering uncertainty in predictions from quantitative structure-activity relationships (QSARs), which are a form of non-in vivo testing information, may improve the way that QSARs support chemical safety assessment under the EU Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH) system. We argue that it is useful to consider uncertainty in QSAR predictions, as it: a) supports rational decision-making; b) facilitates cautious risk management; c) informs uncertainty analysis in probabilistic risk assessment; d) may aid the evaluation of QSAR predictions in weight-of-evidence approaches; and e) provides a probabilistic model to verify the experimental data used in risk assessment. The discussion is illustrated by using case studies of QSAR integrated hazard and risk assessment from the EU-financed CADASTER project.
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