Analysis of plastic-related chemical contaminants in human milk using targeted and non-targeted screening
Bibliographic record
Abstract
Human milk, which is vital for infant growth and development, may contain various xenobiotics, highlighting the need for its comprehensive biomonitoring on a regular basis. While traditional targeted approaches are widely employed for monitoring contaminants in biological samples around the world, numerous unknown chemicals are often overlooked. Plastic-related contaminants (PRCs), ubiquitous in the environment, represent one of the most extensive contaminants detected in human milk; bisphenols are one of the dominant classes of PRCs that have been studied. Despite the abundance of studies, however, data are lacking with respect to the levels of these contaminants in certain matrices and geographical regions. Furthermore, the use of targeted analysis (TA) limits the detection of previously underreported or unknown PRCs (i.e. preservatives, UV filters, synthetic antioxidants), suggesting the need to develop innovative tools such as non-targeted analysis (NTA) coupled with high-resolution mass spectrometry to detect these substances.The purpose of this study was to detect and identify the presence of classes of PRCs, focusing on bisphenols and parabens, that may be present in human milk. Specifically, the goal was to quantitatively assess and compare bisphenol types and levels across regions, to identify related unknowns along with other common and unusual parabens, as well as to evaluate their conjugation potential in human milk by employing both TA and NTA. There is a detailed review of all detected environmental contaminants in human milk, as well as highlights of all previous studies conducted on PRCs in Chapter 2. Limitations of the predominant use of TA, obstructing risk assessment and toxicity evaluation for chemicals present in human milk in terms of chemical mixtures, are also described. Evidently, there is a need for further data to fill data gaps with respect to the PRCs, such as bisphenols, in specific countries. To address this, bisphenols were detected in human milk from Canada and South Africa (Vhembe and Pretoria), a country where data are scarce (Chapter 3). An efficient QuEChERS extraction method was developed for the TA of 9 selected bisphenols in human milk. BPA was the predominant bisphenol detected in South African human milk, followed by BPS and BPAF. BPS was the exclusive bisphenol detectable in milk from Montreal, suggesting differences in exposure to bisphenols in these two countries and providing crucial insights for future investigations by health officials. The TA of human milk in Chapter 3 also reinforced the need for NTA as a valuable emerging tool to detect and identify different unknown contaminants. A customized database library for the detection of bisphenol-related unknowns using NTA is introduced in Chapter 4. Successful workflow implementation, with the extracted data using the same extraction method as in Chapter 3, was used to identify different bisphenol S related-unknowns that are used in thermal labels, along with different synthetic antioxidants and UV absorbers. Among these compounds, 2 synthetic antioxidants-related unknowns (including 1 metabolite) have not been reported previously in human milk studies.In Chapter 5, the use of NTA is extended for the identification of common and unusual parabens, along with other PRCs of interest. Seven parabens, various phthalate metabolites, and per- and polyfluoroalkyl substances were detected in human milk, including a unique paraben exclusive to South Africa. The detection of these different and unexpected PRCs highlights the usefulness of applying NTA in human milk biomonitoring. Together, this research has demonstrated that integrating TA with NTA in human milk biomonitoring can facilitate the detection of unexpected contaminants and is both cost effective and time efficient. This research also emphasizes the significance of applying NTA for the detection of family-specific contaminants, providing regulatory agencies with essential information on their presence for regular human milk biomonitoring
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".