| 1. |
- García-Gómez, Elisa, et al.
(author)
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Characterization of scrubber water discharges from ships using comprehensive suspect screening strategies based on GC-APCI-HRMS
- 2023
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In: Chemosphere. - 0045-6535 .- 1879-1298. ; 343
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Journal article (peer-reviewed)abstract
- An extended suspect screening approach for the comprehensive chemical characterization of scrubber discharge waters from exhaust gas cleaning systems (EGCSs), used to reduce atmospheric shipping emissions of sulphur oxides, was developed. The suspect screening was based on gas chromatography coupled with high-resolution mass spectrometry (GC-HRMS) and focused on the identification of polycyclic aromatic hydrocarbons (PAHs) and their alkylated derivatives (alkyl-PAHs), which are among the most frequent and potentially toxic organic contaminants detected in these matrices. Although alkyl-PAHs can be even more abundant than parent compounds, information regarding their occurrence in scrubber waters is scarce. For compound identification, an in-house compound database was built, with 26 suspect groups, including 25 parent PAHs and 23 alkyl-PAH homologues. With this approach, 7 PAHs and 12 clusters of alkyl-PAHs were tentatively identified, whose occurrence was finally confirmed by target analysis using GC coupled with tandem mass spectrometry (GC-MS/MS). Finally, a retrospective analysis was performed to identify other relevant (poly)cyclic aromatic compounds (PACs) of potential concern in scrubber waters. According to it, 18 suspect groups were tentatively identified, including biphenyls, dibenzofurans, dibenzothiophenes and oxygenated PAHs derivatives. All these compounds could be used as relevant markers of scrubber water contamination in heavy traffic marine areas and be considered as potential stressors when evaluating scrubber water toxicity.
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| 3. |
- Stewart, R., et al.
(author)
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Knowledge discovery for Deep Phenotyping serious mental illness from Electronic Mental Health records
- 2018
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In: F1000 Research. - : F1000 Research Ltd. - 2046-1402. ; 7
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Journal article (peer-reviewed)abstract
- Background: Deep Phenotyping is the precise and comprehensive analysis of phenotypic features in which the individual components of the phenotype are observed and described. In UK mental health clinical practice, most clinically relevant information is recorded as free text in the Electronic Health Record, and offers a granularity of information beyond what is expressed in most medical knowledge bases. The SNOMED CT nomenclature potentially offers the means to model such information at scale, yet given a sufficiently large body of clinical text collected over many years, it is difficult to identify the language that clinicians favour to express concepts. Methods: By utilising a large corpus of healthcare data, we sought to make use of semantic modelling and clustering techniques to represent the relationship between the clinical vocabulary of internationally recognised SMI symptoms and the preferred language used by clinicians within a care setting. We explore how such models can be used for discovering novel vocabulary relevant to the task of phenotyping Serious Mental Illness (SMI) with only a small amount of prior knowledge. Results: 20 403 terms were derived and curated via a two stage methodology. The list was reduced to 557 putative concepts based on eliminating redundant information content. These were then organised into 9 distinct categories pertaining to different aspects of psychiatric assessment. 235 concepts were found to be expressions of putative clinical significance. Of these, 53 were identified having novel synonymy with existing SNOMED CT concepts. 106 had no mapping to SNOMED CT. Conclusions: We demonstrate a scalable approach to discovering new concepts of SMI symptomatology based on real-world clinical observation. Such approaches may offer the opportunity to consider broader manifestations of SMI symptomatology than is typically assessed via current diagnostic frameworks, and create the potential for enhancing nomenclatures such as SNOMED CT based on real-world expressions.
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