Diisocyanate conjugate and immunoassay characteristics influence detection of specific antibodies in HDI‐exposed workers
Bibliographic record
Abstract
BACKGROUND: The structural characteristics of diisocyanate chemical protein antigens vary depending upon the methods of production, and may influence diisocyanate antigen immunoassays. The impact of different antigen preparation methods on immunoassay sensitivity, specificity, and predictive value for identifying workers with diisocyanate asthma (DA) has not been systematically evaluated. OBJECTIVE: Evaluate the influence of preparation methodology of hexamethylene diisocyanate human serum albumin (HDI-HSA) conjugates on the performance of specific antibody assays for identifying workers with confirmed HDI asthma. METHODS: Asthmatic reactions to HDI exposure were assessed in 80 autobody shop workers by specific inhalation challenge (SIC). HDI-specific IgE and IgG in serum were measured by RAST and ELISA with seven different HDI-HSA conjugates prepared in liquid phase with monomeric or polymeric HDI, or vapour-phase monomeric HDI. The HDI : HSA substitution ratios were determined by mass spectrometry. RESULTS: DA was confirmed by SIC in 23 subjects. The maximal sensitivity for detecting specific IgE among workers with positive SIC results was higher with RAST and with polymeric vs. monomeric HDI-albumin conjugates (21.7% vs. 8.7%) with a generally high specificity (>or=95%). HDI-HSA specific IgG antibody was also detected in 22-43% of HDI asthmatics depending upon the conjugate used. The specificity of specific IgG varied from 88% to 96%, and it was higher for monomeric (vs. polymeric) HDI-albumin conjugates with low (vs. high) substitution ratios. CONCLUSION: The test performance of specific IgE and IgG immunoassays for identifying a positive SIC response varied with different HDI-HSA conjugates. Standard test antigens and common immunoassays must be used to minimize inter-laboratory variability.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".