New microextraction techniques in exposome Research: Bridging environmental exposures and human health
Bibliographic record
Abstract
Exposome studies encompass comprehensive investigations into the various exposures individual living organisms encounter from exogenous factors throughout their lifetime, and how these exposures may affect their health status. These studies may focus on health risks associated with single chemical exposures or the cumulative effect of multiple chemicals, aiming to elucidate dose−response relationships. Numerous analytical methods are available for measuring contaminants and/or their endogenous metabolites across various matrices, including biofluids, food, air, water, and soil. In recent years, microextraction techniques have gained increasing relevance in exposome research due to their ability to enhance analytical throughput while minimizing invasion to the systems under study. Among these techniques, solid phase microextraction (SPME), a well-established sample preparation method in environmental and toxicological studies, has gained particular attention for its ability to extract a wide range of compounds, including short-lived and unstable compounds, directly from living organisms ( in vivo SPME). As a result, SPME is gaining traction in exposome studies for its ability to both detect exogenous compounds in environmental matrices and track the fate of these contaminants, including their metabolism, accumulation, and metabolic effects in living systems. This review explores recent applications of microextraction in exposome research, highlighting the advantages of different coatings, adsorbent chemistries, and geometric designs— such as fiber SPME, thin film SPME (TFME), needle trap devices (NTDs), coated tips (arrow-SPME), blade SPME, and the newly introduced swab-SPME—for the analysis of exogenous and endogenous compounds in environmental samples and in biological matrices. We also examine the emerging applications of direct-MS coupled with SPME in exposome research and provide an overview of the significant potential of SPME in both targeted and untargeted screening of low molecular weight molecules within exposomics and metabolomics applications. • Microextraction advances enable exposome and biological effect analysis. • SPME allows non-lethal in vivo sampling in diverse exposome matrices. • Needle trap Devices (NTDs) excel in breath/air analysis for exposome research. • SPME-direct-MS integration enables rapid environmental exposure screening. • SPME & NTD monitor free/particle-bound pollutants in real samples.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".