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Record W2900282244 · doi:10.1016/j.envint.2018.10.055

Metals and oxidative potential in urban particulate matter influence systemic inflammatory and neural biomarkers: A controlled exposure study

2018· article· en· W2900282244 on OpenAlexafffund
Ling Liu, Bruce Urch, Mieczysław Szyszkowicz, Greg J. Evans, Mary Speck, Angela Huang, Karen Leingartner, Robin Shutt, Guillaume Pelletier, Diane R. Gold, Jeffrey R. Brook, Krystal J. Godri Pollitt, Frances Silverman

Bibliographic record

VenueEnvironment International · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsSt. Michael's HospitalEnvironment and Climate Change CanadaPublic Health OntarioUniversity of TorontoHealth Canada
FundersHealth CanadaNational Institutes of HealthNational Institute of Environmental Health SciencesAllerGenEnvironment and Climate Change CanadaUniversity of TorontoClean Air Regulatory AgendaCanada Foundation for InnovationU.S. Environmental Protection Agency
KeywordsOxidative stressBiomarkerChemistryParticulatesInflammationGlutathioneUrineInhalation exposureInternal medicineSystemic inflammationMedicineEndocrinologyToxicityBiochemistry

Abstract

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BACKGROUND: Oxidative stress and inflammation are considered to be important pathways leading to particulate matter (PM)-associated disease. In this exploratory study, we examined the effects of metals and oxidative potential (OP) in urban PM on biomarkers of systemic inflammation, oxidative stress and neural function. METHODS: ). Exposures lasted 130 min, separated by ≥2 weeks. Metal concentrations and OP (measured by ascorbate and glutathione depletion in synthetic airway fluid) in PM were analyzed. Blood and urine samples were collected pre-exposure, and 1-h and 21-h post exposure for assessment of biomarkers. We used mixed-regression models to analyze associations adjusting for PM size and mass concentration. RESULTS: Results for metals were expressed as change (%) from daily pre-exposure biomarker levels after exposure to a metal at a level equivalent to the mean concentration. Exposure to various metals (silver, aluminum, barium, copper, iron, potassium, lithium, nickel, tin, and/or vanadium) was significantly associated with increased levels of various blood or urinary biomarkers. For example, the blood inflammatory marker vascular endothelia growth factor (VEGF) increased 5.3% (95% confidence interval: 0.3%, 10.2%) 1-h post exposure to nickel; the traumatic brain injury marker ubiquitin C-terminal hydrolase L1 (UCHL1) increased 11% (1.2%, 21%) and 14% (0.3%, 29%) 1-h and 21-h post exposure to barium, respectively; and the systemic stress marker cortisol increased 1.5% (0%, 2.9%) and 1.5% (0.5%, 2.8%) 1-h and 21-h post exposure to silver, respectively. Urinary DNA oxidation marker 8‑hydroxy‑deoxy‑guanosine increased 14% (6.4%, 21%) 1-h post exposure to copper; urinary neural marker vanillylmandelic acid increased 29% (3%, 54%) 1-h post exposure to aluminum; and urinary cortisol increased 88% (0.9%, 176%) 1-h post exposure to vanadium. Results for OP were expressed as change (%) from daily pre-exposure biomarker levels after exposure to ascorbate-related OP at a level equivalent to the mean concentration, or for exposure to glutathione-related OP at a level above the limit of detection. Exposure to ascorbate- or glutathione-related OP was significantly associated with increased inflammatory and neural biomarkers including interleukin-6, VEGF, UCHL1, and S100 calcium-binding protein B in blood, and malondialdehyde and 8-hydroxy-deoxy-guanosine in urine. For example, UCHL1 increased 9.4% (1.8%, 17%) in blood 21-h post exposure to ascorbate-related OP, while urinary malondialdehyde increased 19% (3.6%, 35%) and 8-hydroxy-deoxy-guanosine increased 24% (2.9%, 48%) 21-h post exposure to ascorbate- and glutathione-related OP, respectively. CONCLUSION: Our results from this exploratory study suggest that metal constituents and OP in ambient PM may influence biomarker levels associated with systemic inflammation, oxidative stress, perturbations of neural function, and systemic physiological stress.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.012
GPT teacher head0.260
Teacher spread0.247 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations99
Published2018
Admission routes2
Has abstractyes

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