Targeted and Nontargeted Mass Spectrometry Analysis of Tear Fluid for Biomarker Discovery and Biomonitoring
Bibliographic record
Abstract
Humans are constantly exposed to environmental pollutants and hazardous chemicals, yet we lack effective tools for monitoring the concentrations of these substances within the body. Traditional biomonitoring programs rely on blood and urine samples, but the invasiveness of these collection methods can discourage participation and impose financial limitations. To address this, alternative human matrices are being explored to provide more convenient means of biomonitoring. One promising yet underutilized matrix is tear fluid, which is easily accessible, interfaces with ambient air, and communicates dynamically with blood, offering insights into both environmental exposures and internal physiological processes. This thesis aims to characterize and develop a method for tear-based biomonitoring by introducing a novel means of passive sampling. To this end, a sample preparation and analysis protocol was optimized for extracting, identifying, and quantifying polar and nonpolar chemicals from tear fluid passive samplers (silicone hydrogel contact lenses). To validate the passive sampling material could retain chemicals of interest, targeted approaches were developed with hydrophilic interaction liquid chromatography coupled with triple quadrupole mass spectrometry (HILIC-MS/MS) for polar metabolites and Orbitrap gas chromatography-mass spectrometry (GC-MS) for semi-volatile organic compounds. Additionally, to comprehensively characterize the chemical profile captured by the passive samplers, an optimized nontargeted workflow using high-resolution mass spectrometry was developed. Further, multiple platforms were utilized for broad coverage of chemicals, including HILIC and reverse-phase liquid chromatography (RP-LC) with quadrupole time-of-flight mass spectrometry (QTOF-MS), as well as Orbitrap GC-MS. This work lays the groundwork for a user-friendly biomonitoring approach that enables personalized chemical exposure assessment with convenient sampling. It also has the potential to facilitate larger-scale studies involving vulnerable or hard-to-reach populations. A comprehensive biomonitoring survey using tear fluid could uncover valuable insights into sources and persistence of chemical exposures and their correlations with health trends across time and regions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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".