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314 results on '"Samanipour, Saer"'

6. Workflow to facilitate the detection of new psychoactive substances and drugs of abuse in influent urban wastewater

Author

Bade, Richard, van Herwerden, Denice, Rousis, Nikolaos, Adhikari, Sangeet, Allen, Darren, Baduel, Christine, Bijlsma, Lubertus, Boogaerts, Tim, Burgard, Dan, Chappell, Andrew, Driver, Erin M., Sodre, Fernando Fabriz, Fatta-Kassinos, Despo, Gracia-Lor, Emma, Gracia-Marín, Elisa, Halden, Rolf U., Heath, Ester, Jaunay, Emma, Krotulski, Alex, Lai, Foon Yin, Löve, Arndís Sue Ching, O’Brien, Jake W., Oh, Jeong-Eun, Pasin, Daniel, Castro, Marco Pineda, Psichoudaki, Magda, Salgueiro-Gonzalez, Noelia, Gomes, Cezar Silvino, Subedi, Bikram, Thomas, Kevin V., Thomaidis, Nikolaos, Wang, Degao, Yargeau, Viviane, Samanipour, Saer, and Mueller, Jochen

Published
2024
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7. NORMAN guidance on suspect and non-target screening in environmental monitoring

Author

Hollender, Juliane, Schymanski, Emma L., Ahrens, Lutz, Alygizakis, Nikiforos, Béen, Frederic, Bijlsma, Lubertus, Brunner, Andrea M., Celma, Alberto, Fildier, Aurelie, Fu, Qiuguo, Gago-Ferrero, Pablo, Gil-Solsona, Ruben, Haglund, Peter, Hansen, Martin, Kaserzon, Sarit, Kruve, Anneli, Lamoree, Marja, Margoum, Christelle, Meijer, Jeroen, Merel, Sylvain, Rauert, Cassandra, Rostkowski, Pawel, Samanipour, Saer, Schulze, Bastian, Schulze, Tobias, Singh, Randolph R., Slobodnik, Jaroslav, Steininger-Mairinger, Teresa, Thomaidis, Nikolaos S., Togola, Anne, Vorkamp, Katrin, Vulliet, Emmanuelle, Zhu, Linyan, and Krauss, Martin

Published
2023
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13. Bridging knowledge gaps in human chemical exposure via drinking water with non-target screening.

Author

Ciccarelli, Davide, Samanipour, Saer, Rapp-Wright, Helena, Bieber, Stefan, Letzel, Thomas, O'Brien, Jake W., Marczylo, Tim, Gant, Timothy W., Vineis, Paolo, and Barron, Leon P.

Subjects
*MACHINE learning, *CHEMICAL potential, *ENVIRONMENTAL exposure, *CHEMICAL structure, *POLLUTANTS
Abstract

Fundamental knowledge gaps still exist in the exposome, especially regarding analytical space coverage, mapping and prioritization of a very large number of diverse chemical structures. This review focuses on the contributions of suspect and non-target screening (NTS) to contaminants characterization and toxicity assessment in drinking water. A comprehensive review of publications from 2013-2024 revealed only 172 substances identified with certainty using NTS and in 17 countries. The analytical approaches, their complementarity, effectiveness and use with compound identification frameworks are discussed. The use of 'intelligent' tools (including machine learning) to aid with substance identification, prioritization and toxicity assessment is emerging. Strategies for integration of NTS in epidemiology are also considered, including re-use of existing data. NTS holds great potential for chemical exposure assessment from drinking water and its contribution to the exposome. [ABSTRACT FROM AUTHOR]

Published
2025
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16. The NORMAN Suspect List Exchange (NORMAN-SLE): facilitating European and worldwide collaboration on suspect screening in high resolution mass spectrometry

Author

Mohammed Taha, Hiba, Aalizadeh, Reza, Alygizakis, Nikiforos, Antignac, Jean-Philippe, Arp, Hans Peter H., Bade, Richard, Baker, Nancy, Belova, Lidia, Bijlsma, Lubertus, Bolton, Evan E., Brack, Werner, Celma, Alberto, Chen, Wen-Ling, Cheng, Tiejun, Chirsir, Parviel, Čirka, Ľuboš, D’Agostino, Lisa A., Djoumbou Feunang, Yannick, Dulio, Valeria, Fischer, Stellan, Gago-Ferrero, Pablo, Galani, Aikaterini, Geueke, Birgit, Głowacka, Natalia, Glüge, Juliane, Groh, Ksenia, Grosse, Sylvia, Haglund, Peter, Hakkinen, Pertti J., Hale, Sarah E., Hernandez, Felix, Janssen, Elisabeth M.-L., Jonkers, Tim, Kiefer, Karin, Kirchner, Michal, Koschorreck, Jan, Krauss, Martin, Krier, Jessy, Lamoree, Marja H., Letzel, Marion, Letzel, Thomas, Li, Qingliang, Little, James, Liu, Yanna, Lunderberg, David M., Martin, Jonathan W., McEachran, Andrew D., McLean, John A., Meier, Christiane, Meijer, Jeroen, Menger, Frank, Merino, Carla, Muncke, Jane, Muschket, Matthias, Neumann, Michael, Neveu, Vanessa, Ng, Kelsey, Oberacher, Herbert, O’Brien, Jake, Oswald, Peter, Oswaldova, Martina, Picache, Jaqueline A., Postigo, Cristina, Ramirez, Noelia, Reemtsma, Thorsten, Renaud, Justin, Rostkowski, Pawel, Rüdel, Heinz, Salek, Reza M., Samanipour, Saer, Scheringer, Martin, Schliebner, Ivo, Schulz, Wolfgang, Schulze, Tobias, Sengl, Manfred, Shoemaker, Benjamin A., Sims, Kerry, Singer, Heinz, Singh, Randolph R., Sumarah, Mark, Thiessen, Paul A., Thomas, Kevin V., Torres, Sonia, Trier, Xenia, van Wezel, Annemarie P., Vermeulen, Roel C. H., Vlaanderen, Jelle J., von der Ohe, Peter C., Wang, Zhanyun, Williams, Antony J., Willighagen, Egon L., Wishart, David S., Zhang, Jian, Thomaidis, Nikolaos S., Hollender, Juliane, Slobodnik, Jaroslav, and Schymanski, Emma L.

Published
2022
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26. Studying venom toxin variation using accurate masses from liquid chromatography–mass spectrometry coupled with bioinformatic tools

Author

Alonso, Luis L., van Thiel, Jory, Slagboom, Julien, Dunstan, Nathan, Modahl, Cassandra M., Jackson, Timothy N. W., Samanipour, Saer, Kool, Jeroen, Alonso, Luis L., van Thiel, Jory, Slagboom, Julien, Dunstan, Nathan, Modahl, Cassandra M., Jackson, Timothy N. W., Samanipour, Saer, and Kool, Jeroen

Abstract

This study provides a new methodology for the rapid analysis of numerous venom samples in an automated fashion. Here, we use LC-MS (Liquid Chromatography–Mass Spectrometry) for venom separation and toxin analysis at the accurate mass level combined with new in-house written bioinformatic scripts to obtain high-throughput results. This analytical methodology was validated using 31 venoms from all members of a monophyletic clade of Australian elapids: brown snakes (Pseudonaja spp.) and taipans (Oxyuranus spp.). In a previous study, we revealed extensive venom variation within this clade, but the data was manually processed and MS peaks were integrated into a time-consuming and labour-intensive approach. By comparing the manual approach to our new automated approach, we now present a faster and more efficient pipeline for analysing venom variation. Pooled venom separations with post-column toxin fractionations were performed for subsequent high-throughput venomics to obtain toxin IDs correlating to accurate masses for all fractionated toxins. This workflow adds another dimension to the field of venom analysis by providing opportunities to rapidly perform in-depth studies on venom variation. Our pipeline opens new possibilities for studying animal venoms as evolutionary model systems and investigating venom variation to aid in the development of better antivenoms.

Published
2024
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27. Inter-laboratory mass spectrometry dataset based on passive sampling of drinking water for non-target analysis

Author

Schulze, Bastian, van Herwerden, Denice, Allan, Ian, Bijlsma, Lubertus, Etxebarria, Nestor, Hansen, Martin, Merel, Sylvain, Vrana, Branislav, Aalizadeh, Reza, Bajema, Bernard, Dubocq, Florian, Coppola, Gianluca, Fildier, Aurélie, Fialová, Pavla, Frøkjær, Emil, Grabic, Roman, Gago-Ferrero, Pablo, Gravert, Thorsten, Hollender, Juliane, Huynh, Nina, Jacobs, Griet, Jonkers, Tim, Kaserzon, Sarit, Lamoree, Marja, Le Roux, Julien, Mairinger, Teresa, Margoum, Christelle, Mascolo, Giuseppe, Mebold, Emmanuelle, Menger, Frank, Miège, Cécile, Meijer, Jeroen, Moilleron, Régis, Murgolo, Sapia, Peruzzo, Massimo, Pijnappels, Martijn, Reid, Malcolm, Roscioli, Claudio, Soulier, Coralie, Valsecchi, Sara, Thomaidis, Nikolaos, Vulliet, Emmanuelle, Young, Robert, and Samanipour, Saer

Published
2021
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37. The NORMAN Association and the European Partnership for Chemicals Risk Assessment (PARC): let’s cooperate!

Author

Dulio, Valeria, Koschorreck, Jan, van Bavel, Bert, van den Brink, Paul, Hollender, Juliane, Munthe, John, Schlabach, Martin, Aalizadeh, Reza, Agerstrand, Marlene, Ahrens, Lutz, Allan, Ian, Alygizakis, Nikiforos, Barcelo’, Damia’, Bohlin-Nizzetto, Pernilla, Boutroup, Susanne, Brack, Werner, Bressy, Adèle, Christensen, Jan H., Cirka, Lubos, Covaci, Adrian, Derksen, Anja, Deviller, Geneviève, Dingemans, Milou M. L., Engwall, Magnus, Fatta-Kassinos, Despo, Gago-Ferrero, Pablo, Hernández, Félix, Herzke, Dorte, Hilscherová, Klára, Hollert, Henner, Junghans, Marion, Kasprzyk-Hordern, Barbara, Keiter, Steffen, Kools, Stefan A. E., Kruve, Anneli, Lambropoulou, Dimitra, Lamoree, Marja, Leonards, Pim, Lopez, Benjamin, López de Alda, Miren, Lundy, Lian, Makovinská, Jarmila, Marigómez, Ionan, Martin, Jonathan W., McHugh, Brendan, Miège, Cécile, O’Toole, Simon, Perkola, Noora, Polesello, Stefano, Posthuma, Leo, Rodriguez-Mozaz, Sara, Roessink, Ivo, Rostkowski, Pawel, Ruedel, Heinz, Samanipour, Saer, Schulze, Tobias, Schymanski, Emma L., Sengl, Manfred, Tarábek, Peter, Ten Hulscher, Dorien, Thomaidis, Nikolaos, Togola, Anne, Valsecchi, Sara, van Leeuwen, Stefan, von der Ohe, Peter, Vorkamp, Katrin, Vrana, Branislav, and Slobodnik, Jaroslav

Published
2020
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38. Novel Per- and Polyfluoroalkyl Substances Discovered in Cattle Exposed to AFFF-Impacted Groundwater

Author

Dewapriya, Pradeep, primary, Nilsson, Sandra, additional, Ghorbani Gorji, Sara, additional, O’Brien, Jake W., additional, Bräunig, Jennifer, additional, Gómez Ramos, María José, additional, Donaldson, Eric, additional, Samanipour, Saer, additional, Martin, Jonathan W., additional, Mueller, Jochen F., additional, Kaserzon, Sarit L., additional, and Thomas, Kevin V., additional

Published
2023
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42. Novel Per- and Polyfluoroalkyl Substances Discovered in Cattle Exposed to AFFF-Impacted Groundwater

Author

Dewapriya, Pradeep, Nilsson, Sandra, Gorji, Sara Ghorbani, O'Brien, Jake W., Bräunig, Jennifer, Ramos, María José Gómez, Donaldson, Eric, Samanipour, Saer, Martin, Jonathan W., Mueller, Jochen F., Kaserzon, Sarit L., Thomas, Kevin V., Dewapriya, Pradeep, Nilsson, Sandra, Gorji, Sara Ghorbani, O'Brien, Jake W., Bräunig, Jennifer, Ramos, María José Gómez, Donaldson, Eric, Samanipour, Saer, Martin, Jonathan W., Mueller, Jochen F., Kaserzon, Sarit L., and Thomas, Kevin V.

Abstract

The leaching of per- and polyfluoroalkyl substances (PFASs) from Australian firefighting training grounds has resulted in extensive contamination of groundwater and nearby farmlands. Humans, farm animals, and wildlife in these areas may have been exposed to complex mixtures of PFASs from aqueous film-forming foams (AFFFs). This study aimed to identify PFAS classes in pooled whole blood (n = 4) and serum (n = 4) from cattle exposed to AFFF-impacted groundwater and potentially discover new PFASs in blood. Thirty PFASs were identified at various levels of confidence (levels 1a–5a), including three novel compounds: (i) perfluorohexanesulfonamido 2-hydroxypropanoic acid (FHxSA-HOPrA), (ii) methyl((perfluorohexyl)sulfonyl)sulfuramidous acid, and (iii) methyl((perfluorooctyl)sulfonyl)sulfuramidous acid, belonging to two different classes. Biotransformation intermediate, perfluorohexanesulfonamido propanoic acid (FHxSA-PrA), hitherto unreported in biological samples, was detected in both whole blood and serum. Furthermore, perfluoroalkyl sulfonamides, including perfluoropropane sulfonamide (FPrSA), perfluorobutane sulfonamide (FBSA), and perfluorohexane sulfonamide (FHxSA) were predominantly detected in whole blood, suggesting that these accumulate in the cell fraction of blood. The suspect screening revealed several fluoroalkyl chain-substituted PFAS. The results suggest that targeting only the major PFASs in the plasma or serum of AFFF-exposed mammals likely underestimates the toxicological risks associated with exposure. Future studies of AFFF-exposed populations should include whole-blood analysis with high-resolution mass spectrometry to understand the true extent of PFAS exposure.

Published
2023
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43. NORMAN guidance on suspect and non-target screening in environmental monitoring

Author

0000-0002-4660-274X, 0000-0001-6868-8145, 0000-0002-5430-6764, 0000-0002-5727-4999, 0000-0001-5910-3248, 0000-0001-7005-8775, 0000-0002-2801-1751, 0000-0001-9763-8737, 0000-0002-3805-4122, 0000-0002-4227-5948, 0000-0002-5987-0399, 0000-0002-4269-1036, 0000-0003-2293-7913, 0000-0002-4663-8742, 0000-0002-6744-1279, 0000-0001-9725-3351, 0000-0002-7373-7738, 0000-0002-7386-4454, 0000-0002-4551-6823, 0000-0002-9587-2981, 0000-0002-2543-9023, 0000-0001-9778-4283, 0000-0001-8270-6979, 0000-0003-4641-8659, 0000-0002-9744-8914, 0000-0003-4500-3400, 0000-0003-2622-6318, 0000-0001-7809-1529, 0000-0002-4624-4735, 0000-0002-5393-1533, 0000-0002-1131-5687, 0000-0002-6325-1531, 0000-0003-2036-1447, 0000-0002-0362-4244, Hollender, Juliane, Schymanski, Emma L., Ahrens, Lutz, Alygizakis, Nikiforos, Béen, Frederic, Bijlsma, Lubertus, Brunner, Andrea M., Celma, Alberto, Fildier, Aurelie, Fu, Qiuguo, Gago-Ferrero, Pablo, Gil-Solsona, Rubén, Haglund, Peter, Hansen, Martin, Kaserzon, Sarit, Kruve, Anneli, Lamoree, Marja, Margoum, Christelle, Meijer, Jeroen, Merel, Sylvain, Rauert, Cassandra, Rostkowski, Pawel, Samanipour, Saer, Schulze, Bastian, Schulze, Tobias, Singh, Randolph R., Slobodnik, Jaroslav, Steininger-Mairinger, Teresa, Thomaidis, Nikolaos S., Togola, Anne, Vorkamp, Katrin, Vulliet, Emmanuelle, Zhu, Linyan, Krauss, Martin, 0000-0002-4660-274X, 0000-0001-6868-8145, 0000-0002-5430-6764, 0000-0002-5727-4999, 0000-0001-5910-3248, 0000-0001-7005-8775, 0000-0002-2801-1751, 0000-0001-9763-8737, 0000-0002-3805-4122, 0000-0002-4227-5948, 0000-0002-5987-0399, 0000-0002-4269-1036, 0000-0003-2293-7913, 0000-0002-4663-8742, 0000-0002-6744-1279, 0000-0001-9725-3351, 0000-0002-7373-7738, 0000-0002-7386-4454, 0000-0002-4551-6823, 0000-0002-9587-2981, 0000-0002-2543-9023, 0000-0001-9778-4283, 0000-0001-8270-6979, 0000-0003-4641-8659, 0000-0002-9744-8914, 0000-0003-4500-3400, 0000-0003-2622-6318, 0000-0001-7809-1529, 0000-0002-4624-4735, 0000-0002-5393-1533, 0000-0002-1131-5687, 0000-0002-6325-1531, 0000-0003-2036-1447, 0000-0002-0362-4244, Hollender, Juliane, Schymanski, Emma L., Ahrens, Lutz, Alygizakis, Nikiforos, Béen, Frederic, Bijlsma, Lubertus, Brunner, Andrea M., Celma, Alberto, Fildier, Aurelie, Fu, Qiuguo, Gago-Ferrero, Pablo, Gil-Solsona, Rubén, Haglund, Peter, Hansen, Martin, Kaserzon, Sarit, Kruve, Anneli, Lamoree, Marja, Margoum, Christelle, Meijer, Jeroen, Merel, Sylvain, Rauert, Cassandra, Rostkowski, Pawel, Samanipour, Saer, Schulze, Bastian, Schulze, Tobias, Singh, Randolph R., Slobodnik, Jaroslav, Steininger-Mairinger, Teresa, Thomaidis, Nikolaos S., Togola, Anne, Vorkamp, Katrin, Vulliet, Emmanuelle, Zhu, Linyan, and Krauss, Martin

Abstract

Increasing production and use of chemicals and awareness of their impact on ecosystems and humans has led to large interest for broadening the knowledge on the chemical status of the environment and human health by suspect and non-target screening (NTS). To facilitate effective implementation of NTS in scientific, commercial and governmental laboratories, as well as acceptance by managers, regulators and risk assessors, more harmonisation in NTS is required. To address this, NORMAN Association members involved in NTS activities have prepared this guidance document, based on the current state of knowledge. The document is intended to provide guidance on performing high quality NTS studies and data interpretation while increasing awareness of the promise but also pitfalls and challenges associated with these techniques. Guidance is provided for all steps; from sampling and sample preparation to analysis by chromatography (liquid and gas—LC and GC) coupled via various ionisation techniques to high-resolution tandem mass spectrometry (HRMS/MS), through to data evaluation and reporting in the context of NTS. Although most experience within the NORMAN network still involves water analysis of polar compounds using LC–HRMS/MS, other matrices (sediment, soil, biota, dust, air) and instrumentation (GC, ion mobility) are covered, reflecting the rapid development and extension of the field. Due to the ongoing developments, the different questions addressed with NTS and manifold techniques in use, NORMAN members feel that no standard operation process can be provided at this stage. However, appropriate analytical methods, data processing techniques and databases commonly compiled in NTS workflows are introduced, their limitations are discussed and recommendations for different cases are provided. Proper quality assurance, quantification without reference standards and reporting results with clear confidence of identification assignment complete the guidance together with a gloss

Published
2023

50. An in-silico coverage evaluation of RPLC-MS chemical space via two different retention indices scales

Author

Samanipour, Saer, Nikolopoulos, Alexandros, van Herwerden, Denice, O'Brien, Jake, Barron, Leon, and Thomas, Kevin

Subjects
NTS, NTA, Retention index, Machine learning, High resolution mass spectrometry, Chemical space
Abstract

Resolving the human and environmental exposome is an extremely challenging task due to the complexity of their chemical space (e.g. the number of potential structures). Reversed phase liquid chromatography (RPLC), particularly C18, coupled to high resolution mass spectrometry (HRMS) is one of the most dominant techniques for non-targeted analysis (NTA) and thus tackling the exposome. The NTA approaches, even though the most comprehensive available, do not provide any information about the covered chemical spaces. This implies that what was outside of the covered chemical space remains unknown. Another limitation of NTA is the difficulty in confident chemical identification, since multiple compounds may be potential candidates, especially considering the ever expanding the chemical databases. In this study, a machine learning based tool was built to access the coverage of RPLC chemical space. For this purpose, two independent quantitative structure–retention relationship (QSRR)-based models were used to predict a conventional and a novel type of retention indices (ri) for around 100k compounds from the NORMAN database (i.e. SusDat). To whether a chemical is analyzable with an RPLC system, we employed a leverage-based and a probability density-based approach. Combining the data gathered from the models, it can be predicted whether a compound can be analyzed using a C18 LC column with a certain level of confidence. This assessment showed an accuracy of 63% correct classifications and set up a concrete workflow suitable for similar studies.

Published
2022
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