Sample pre-treatment determines the clinical usefulness of acid-labile subunit immunoassays in the diagnosis of growth hormone deficiency and acromegaly
Bibliographic record
Abstract
OBJECTIVE: The usefulness of measuring the GH-dependent acid-labile subunit (ALS) in the management of GH deficiency (GHD) and acromegaly remains in question and is investigated in this study, comparing several different immunoassays for ALS. METHOD: We compared the diagnostic accuracy of a commercially available polyclonal Ab-based ELISA with SDS pre-treatment (SDS-ELISA) with a monoclonal Ab-based immunofluorometric assay, using two unfolding methods (urea (UREA) and Glycine-HCl (Gly)). The corresponding molecular weight (MW) of ALS and IGFBP-3 immunoreactivity was determined. The clinical usefulness of each assay was examined in adult GH disorders. RESULTS: ALS was lower in GHD and higher in acromegaly using all assays. In GHD, UREA had higher sensitivity and specificity than SDS-ELISA (59 and 69% versus 41 and 51% respectively). In acromegaly, sensitivity and specificity was 94 and 87% for UREA, 81 and 36% for Gly, and 44 and 44% for SDS-ELISA. After UREA, immunoreactivity for ALS and IGFBP-3 eluted at their predicted free MW using size-exclusion chromatography, whereas ALS immunoreactivity in SDS (300-600 kDa) and Gly (250-500 kDa) was at a high apparent MW consistent with aggregation. CONCLUSION: The diagnostic accuracy of ALS varies with assay choice and pre-treatment modality. UREA, which results in migration of ALS at the expected MW on a sizing column, has the highest specificity and sensitivity. Thus, if measured in an assay in which ALS is unfolded without aggregation, ALS is a clinically highly useful parameter for the assessment of GH.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".