An enzyme linked immunosorbent assay (ELISA) for the determination of the human haptoglobin phenotype
Bibliographic record
Abstract
BACKGROUND: Haptoglobin (Hp) is an abundant serum protein which binds extracorpuscular hemoglobin (Hb). Two alleles exist in humans for the Hp gene, denoted 1 and 2. Diabetic individuals with the Hp 2-2 genotype are at increased risk of developing vascular complications including heart attack, stroke, and kidney disease. Recent evidence shows that treatment with vitamin E can reduce the risk of diabetic vascular complications by as much as 50% in Hp 2-2 individuals. We sought to develop a rapid and accurate test for Hp phenotype (which is 100% concordant with the three major Hp genotypes) to facilitate widespread diagnostic testing as well as prospective clinical trials. METHODS: A monoclonal antibody raised against human Hp was shown to distinguish between the three Hp phenotypes in an enzyme linked immunosorbent assay (ELISA). Hp phenotypes obtained in over 8000 patient samples using this ELISA method were compared with those obtained by polyacrylamide gel electrophoresis or the TaqMan PCR method. RESULTS: Our analysis showed that the sensitivity and specificity of the ELISA test for Hp 2-2 phenotype is 99.0% and 98.1%, respectively. The positive predictive value and the negative predictive value for Hp 2-2 phenotype is 97.5% and 99.3%, respectively. Similar results were obtained for Hp 2-1 and Hp 1-1 phenotypes. In addition, the ELISA was determined to be more sensitive and specific than the TaqMan method. CONCLUSIONS: The Hp ELISA represents a user-friendly, rapid and highly accurate diagnostic tool for determining Hp phenotypes. This test will greatly facilitate the typing of thousands of samples in ongoing clinical studies.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".