| 1. |
- Lauria, Mélanie Z., 1993-, et al.
(författare)
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Closing the Organofluorine Mass Balance in Marine Mammals Using Suspect Screening and Machine Learning-Based Quantification
- 2024
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Ingår i: Environmental Science and Technology. - 0013-936X .- 1520-5851. ; 58:5, s. 2458-2467
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Tidskriftsartikel (refereegranskat)abstract
- High-resolution mass spectrometry (HRMS)-based suspect and nontarget screening has identified a growing number of novel per- and polyfluoroalkyl substances (PFASs) in the environment. However, without analytical standards, the fraction of overall PFAS exposure accounted for by these suspects remains ambiguous. Fortunately, recent developments in ionization efficiency (IE) prediction using machine learning offer the possibility to quantify suspects lacking analytical standards. In the present work, a gradient boosted tree-based model for predicting log IE in negative mode was trained and then validated using 33 PFAS standards. The root-mean-square errors were 0.79 (for the entire test set) and 0.29 (for the 7 PFASs in the test set) log IE units. Thereafter, the model was applied to samples of liver from pilot whales (n = 5; East Greenland) and white beaked dolphins (n = 5, West Greenland; n = 3, Sweden) which contained a significant fraction (up to 70%) of unidentified organofluorine and 35 unquantified suspect PFASs (confidence level 2–4). IE-based quantification reduced the fraction of unidentified extractable organofluorine to 0–27%, demonstrating the utility of the method for closing the fluorine mass balance in the absence of analytical standards.
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| 2. |
- Palm, Emma, et al.
(författare)
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Gas Phase Reactivity of Isomeric Hydroxylated Polychlorinated Biphenyls
- 2024
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Ingår i: Journal of the American Society for Mass Spectrometry. - 1044-0305 .- 1879-1123.
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Tidskriftsartikel (refereegranskat)abstract
- Identification of stereo- and positional isomers detected with high-resolution mass spectrometry (HRMS) is often challenging due to near-identical fragmentation spectra (MS2), similar retention times, and collision cross-section values (CCS). Here we address this challenge on the example of hydroxylated polychlorinated biphenyls (OH-PCBs) with the aim to (1) distinguish between isomers of OH-PCBs using two-dimensional ion mobility spectrometry (2D-IMS) and (2) investigate the structure of the fragments of OH-PCBs and their fragmentation mechanisms by ion mobility spectrometry coupled to high-resolution mass spectrometry (IMS-HRMS). The MS2 spectra as well as CCS values of the deprotonated molecule and fragment ions were measured for 18 OH-PCBs using flow injections coupled to a cyclic IMS-HRMS. The MS2 spectra as well as the CCS values of the parent and fragment ions were similar between parent compound isomers; however, ion mobility separation of the fragment ions is hinting at the formation of isomeric fragments. Different parent compound isomers also yielded different numbers of isomeric fragment mobilogram peaks giving new insights into the fragmentation of these compounds and indicating new possibilities for identification. For spectral interpretation, Gibbs free energies and CCS values for the fragment ions of 4 '-OH-CB35, 4 '-OH-CB79, 2-OH-CB77 and 4-OH-CB107 were calculated and enabled assignment of structures to the isomeric mobilogram peaks of [M-H-HCl](-) fragments. Finally, further fragmentation of the isomeric fragments revealed different fragmentation pathways depending on the isomeric fragment ions.
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| 3. |
- Rahu, Ida, et al.
(författare)
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Predicting the Activity of Unidentified Chemicals in Complementary Bioassays from the HRMS Data to Pinpoint Potential Endocrine Disruptors
- 2024
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Ingår i: Journal of Chemical Information and Modeling. - 1549-9596 .- 1549-960X. ; 64:8, s. 3093-3104
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Tidskriftsartikel (refereegranskat)abstract
- The majority of chemicals detected via nontarget liquid chromatography high-resolution mass spectrometry (HRMS) in environmental samples remain unidentified, challenging the capability of existing machine learning models to pinpoint potential endocrine disruptors (EDs). Here, we predict the activity of unidentified chemicals across 12 bioassays related to EDs within the Tox21 10K dataset. Single- and multi-output models, utilizing various machine learning algorithms and molecular fingerprint features as an input, were trained for this purpose. To evaluate the models under near real-world conditions, Monte Carlo sampling was implemented for the first time. This technique enables the use of probabilistic fingerprint features derived from the experimental HRMS data with SIRIUS+CSI:FingerID as an input for models trained on true binary fingerprint features. Depending on the bioassay, the lowest false-positive rate at 90% recall ranged from 0.251 (sr.mmp, mitochondrial membrane potential) to 0.824 (nr.ar, androgen receptor), which is consistent with the trends observed in the models' performances submitted for the Tox21 Data Challenge. These findings underscore the informativeness of fingerprint features that can be compiled from HRMS in predicting the endocrine-disrupting activity. Moreover, an in-depth SHapley Additive exPlanations analysis unveiled the models' ability to pinpoint structural patterns linked to the modes of action of active chemicals. Despite the superior performance of the single-output models compared to that of the multi-output models, the latter's potential cannot be disregarded for similar tasks in the field of in silico toxicology. This study presents a significant advancement in identifying potentially toxic chemicals within complex mixtures without unambiguous identification and effectively reducing the workload for postprocessing by up to 75% in nontarget HRMS.
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| 4. |
- Szabo, Drew, 1990-, et al.
(författare)
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Online and Offline Prioritization of Chemicals of Interest in Suspect Screening and Non-targeted Screening with High-Resolution Mass Spectrometry
- 2024
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Ingår i: Analytical Chemistry. - 0003-2700 .- 1520-6882. ; 96:9, s. 3707-3716
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Forskningsöversikt (refereegranskat)abstract
- Recent advances in high-resolution mass spectrometry (HRMS) have enabled the detection of thousands of chemicals from a single sample, while computational methods have improved the identification and quantification of these chemicals in the absence of reference standards typically required in targeted analysis. However, to determine the presence of chemicals of interest that may pose an overall impact on ecological and human health, prioritization strategies must be used to effectively and efficiently highlight chemicals for further investigation. Prioritization can be based on a chemical's physicochemical properties, structure, exposure, and toxicity, in addition to its regulatory status. This Perspective aims to provide a framework for the strategies used for chemical prioritization that can be implemented to facilitate high-quality research and communication of results. These strategies are categorized as either online or offline prioritization techniques. Online prioritization techniques trigger the isolation and fragmentation of ions from the low-energy mass spectra in real time, with user-defined parameters. Offline prioritization techniques, in contrast, highlight chemicals of interest after the data has been acquired; detected features can be filtered and ranked based on the relative abundance or the predicted structure, toxicity, and concentration imputed from the tandem mass spectrum (MS2). Here we provide an overview of these prioritization techniques and how they have been successfully implemented and reported in the literature to find chemicals of elevated risk to human and ecological environments. A complete list of software and tools is available from https://nontargetedanalysis.org/.
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