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Record W2119836615 · doi:10.1186/1745-6673-6-9

Elimination kinetics of diisocyanates after specific inhalative challenges in humans: mass spectrometry analysis, as a basis for biomonitoring strategies

2011· article· en· W2119836615 on OpenAlexaff
Lygia Therese Budnik, Dennis Nowak, R. Merget, Catherine Lemière, Xaver Baur

Bibliographic record

VenueJournal of Occupational Medicine and Toxicology · 2011
Typearticle
Languageen
FieldMedicine
TopicOccupational exposure and asthma
Canadian institutionsHôpital du Sacré-Cœur de Montréal
FundersDeutsche Forschungsgemeinschaft
KeywordsBiomonitoringIsocyanateUrineChromatographyGas chromatography–mass spectrometryMass spectrometryMedicineMetaboliteGas chromatographyChemistryEnvironmental chemistryToxicologyOrganic chemistryBiologyInternal medicinePolyurethane

Abstract

fetched live from OpenAlex

BACKGROUND: Isocyanates are some of the leading occupational causes of respiratory disorders, predominantly asthma. Adequate exposure monitoring may recognize risk factors and help to prevent the onset or aggravation of these aliments. Though, the biomonitoring appears to be most suitable for exposure assessment, the sampling time is critical, however. In order to settle the optimal time point for the sample collection in a practical biomonitoring approach, we aimed to measure the elimination of isocyanate urine metabolites. METHODS: A simple biomonitoring method enabling detection of all major diamine metabolites, from mono-, poly- and diisocyanates in one analytical step, has been established. Urine samples from 121 patients undergoing inhalative challenge tests with diisocyanates for diagnostic reasons were separated by gas chromatography and analyzed with mass spectrometry (GC-MS) at various time points (0-24 h) after the onset of exposure. RESULTS: After controlled exposures to different concentrations of diisocyanates (496 ± 102 ppb-min or 1560 ± 420 ppb-min) the elimination kinetics (of respective isocyanate diamine metabolites) revealed differences between aliphatic and aromatic isocyanates (the latter exhibiting a slower elimination) and a dose-response relationship. No significant differences were observed, however, when the elimination time patterns for individual isocyanates were compared, in respect of either low or high exposure or in relation to the presence or absence of prior immunological sensitization. CONCLUSIONS: The detection of isocyanate metabolites in hydrolyzed urine with the help of gas chromatography combined with mass spectrometric detection system appears to be the most suitable, reliable and sensitive method to monitor possible isocyanate uptake by an individual. Additionally, the information on elimination kinetic patterns must be factored into estimates of isocyanate uptake before it is possible for biomonitoring to provide realistic assessments of isocyanate exposure. The pathophysiological elimination of 1,6-hexamethylene diamine, 2,4-diamine toluene, 2,6-diamine toluene, 1,5-naphthalene diamine, 4,4'-diphenylmethane diamine and isophorone diamines (as respective metabolites of: 1,6-hexamethylene diisocyanate, 2,4-toluene diisocyanate and 2,6 toluene diisocyanate, 1,5-naphthalene diisocyanate, 4,4'-diphenylmethane diisocyanate and isophorone diisocyanates) differs between individual isocyanates' diamines.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.113
GPT teacher head0.371
Teacher spread0.258 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations31
Published2011
Admission routes1
Has abstractyes

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