A toxicity identification evaluation approach to studying estrogenic substances in hog manure and agricultural runoff
Bibliographic record
Abstract
Spreading liquid manure on agricultural fields is a routine way of disposing of animal manure and optimizing the use of nutrients for crops. Limited studies suggest that these wastes may contain a variety of endocrine-disrupting compounds (EDCs) that may be released into aquatic environments through runoff. The purpose of this study was to apply a toxicity identification and evaluation approach to isolate and identify estrogenic compounds in hog manure. A recombinant yeast estrogen screen bioassay was used to detect estrogenicity of high-performance liquid chromatography--separated hog manure fractions. Further analytical analyses of the fractions and comparison to authentic standards resulted in the identification of the endogenous estrogens 17 beta-estradiol (E2) and estrone, and the phytoestrogen metabolite, equol. High levels of equol (6.9-16.6 ppm) were found to be present in manure that was stored for several months. The endocrine-disrupting potential of equol was characterized further by using fish hormone estrogen receptor (ER), sex hormone binding protein (SSBP), and goldfish androgen receptor (AR) radioligand binding assays. Equol was found to be approximately 1,000- and 200-fold less potent that E2 in competing for binding sites of the SSBP and ER, respectively. Equol's potency was 2,200-fold less than testosterone for the AR. Additional studies confirmed the presence of compounds with estrogenic activity in tile drain water after application of hog manure to an agriculture field. In this case, the contribution of equol to the total estrogenicity of the tile drain water was minimal relative to that of natural estrogens. Overall, this study indicates that further work is warranted to assess the impact that EDCs that originate from agricultural runoff may have on the ecology or physiology of exposed biota.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".