Development of competitive ELISAs for 17β-estradiol and 17β-estradiol +estrone+estriol using rabbit polyclonal antibodies
Bibliographic record
Abstract
Estrogens are a family of feminizing hormones that are excreted by vertebrates. It has been documented that their presence in surface waters, even in the ng/L range, can have detrimental impacts on fish reproduction. Two competitive enzyme-linked immunosorbent assays using rabbit polyclonal antibodies were developed: one for 17beta-estradiol and a second one for 17beta-estradiol (E2)+estrone (E1)+estriol (E3). Two different conjugates were synthesized using the Mixed-anhydride (for the 17beta-estradiol ELISA) and the Mannich (for the E1 + E2 + E3 ELISA) reactions. The 17beta-estradiol ELISA was highly specific with an IC(50) of 243 ng/mL for 17beta-estradiol. The E1 + E2 + E3 ELISA exhibited cross-reactivity with estrone (85%) and estriol (62%) with an IC(50) of 18 ng/mL for 17beta-estradiol. Cross-reactivity was tested against 13 chemically related compounds and both immunoassays showed significant cross-reactivity with two estradiol conjugates: beta estradiol-17-valerate and beta estradiol-3-benzoate (from 57 to 84 %) for which, to our knowledge, there are currently no commercially available ELISA. Characteristics (sensitivity, inter and intra assay variation, and cross-reactivity) of the E1 + E2 + E3 ELISA were further compared to those from a commercial Estriol ELISA. The commercial ELISA was more specific, sensitive and its inter-assay variation was less (9.5% compared to 10% for the E1 + E2 + E3 ELISA) but the E1 + E2 + E3 ELISA had less intra-assay variation (4% compared to 5% for the commercial ELISA). Finally, a solid-phase extraction method compatible with the E1 + E2 + E3 immunoassay demonstrated that this combined approach of extraction and immunoassay had good potential for determining estrogen concentrations in environmental samples such as surface water in urban and agricultural ecosystems.
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.005 |
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".