Technical note: Validation of an in-house bovine serum enzyme immunoassay for progesterone measurement
Bibliographic record
Abstract
Measuring circulating progesterone (P4) of dairy cows is a key component of many research studies dealing with basic and applied reproduction physiology. The gold standard in dairy cows for the measurement of P4 in serum is radioimmunoassay (RIA), but it generates radioactive waste and requires licensed facilities. The purpose of this study was to develop and validate an in-house competitive enzyme immunoassay (EIA) to measure the P4 concentration in serum of dairy cattle. The secondary objective was to validate a commercial EIA. In the present study, a competitive EIA was developed using commercially available antibodies and conjugates. Ninety-six well microtiter plates were coated with the secondary antibody and incubated overnight. Following a washing step, the wells were blocked using the primary antibody. Serum samples were prepared by first extracting P4 using petroleum ether, then diluted in working conjugate solution. Samples were pipetted into the coated and blocked plates, then the matching HRP conjugate label (P4-3-HRP, East Coast Bio, North Berwick, ME) was added. The plates were incubated for 2 h, then washed. The substrate solution was added, and the plate was incubated up to 1 h at room temperature in the dark until a blue color had developed. A stop solution was added, and the optical density measured on a microplate reader was set at 450 nm. The binding proportion was calculated by a visible spectrum absorbance reader, and the amount of P4 was calculated using a log-logit regression line. The commercial EIA was executed as suggested by the manufacturer. The validation of the in-house EIA was done by calculating the inter- and intraassay coefficients of variation (CV) and evaluating the parallelism of diluted samples. The results from the in-house and commercial EIA were also compared with the ones from the RIA graphically (scatterplots and Bland-Altman plots) and statistically, using the Spearman correlation coefficient (r) and the Cohen's kappa statistics using a threshold of 1.0 ng/mL (κ). For the in-house EIA, the intraassay CV were all <10%, but the interassay for samples with small and large P4 concentration had CV of 12.5 and 11.0%, respectively. The correlations between the results from the EIA and the RIA were strong (in-house: r = 0.90; commercial: r = 0.83). At small concentrations (<1.0 ng/mL), however, the correlation with the gold standard was weak (in-house: r = 0.27; commercial: r = 0.14). This was likely due to the lack of accuracy at small concentrations, also shown by the absence of parallelism in samples ≤0.4 ng/mL. In conclusion, results from both the in-house and commercial EIA strongly correlated with the gold standard, but less so at smaller concentrations. The in-house EIA offers good accuracy to measure P4 in samples with a concentration >0.4 ng/mL, and a perfect agreement with RIA using a threshold of 1.0 ng/mL.
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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 | 0.023 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.009 |
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".