Arsenic Metabolites, Including <i>N</i>-Acetyl-4-hydroxy-m-arsanilic Acid, in Chicken Litter from a Roxarsone-Feeding Study Involving 1600 Chickens
Bibliographic record
Abstract
The poultry industry has used organoarsenicals, such as 3-nitro-4-hydroxyphenylarsonic acid (Roxarsone, ROX), to prevent disease and to promote growth. Although previous studies have analyzed arsenic species in chicken litter after composting or after application to agricultural lands, it is not clear what arsenic species were excreted by chickens before biotransformation of arsenic species during composting. We describe here the identification and quantitation of arsenic species in chicken litter repeatedly collected on days 14, 24, 28, 30, and 35 of a Roxarsone-feeding study involving 1600 chickens of two strains. High performance liquid chromatography separation with simultaneous detection by both inductively coupled plasma mass spectrometry and electrospray ionization tandem mass spectrometry provided complementary information necessary for the identification and quantitation of arsenic species. A new metabolite, N-acetyl-4-hydroxy-m-arsanilic acid (N-AHAA), was identified, and it accounted for 3-12% of total arsenic. Speciation analyses of litter samples collected from ROX-fed chickens on days 14, 24, 28, 30, and 35 showed the presence of N-AHAA, 3-amino-4-hydroxyphenylarsonic acid (3-AHPAA), inorganic arsenite (As(III)), arsenate (As(V)), monomethylarsonic acid (MMA(V)), dimethylarsinic acid (DMA(V)), and ROX. 3-AHPAA accounted for 3-19% of the total arsenic. Inorganic arsenicals (the sum of As(III) and As(V)) comprised 2-6% (mean 3.5%) of total arsenic. Our results on the detection of inorganic arsenicals, methylarsenicals, 3-AHPAA, and N-AHAA in the chicken litter support recent findings that ROX is actually metabolized by the chicken or its gut microbiome. The presence of the toxic metabolites in chicken litter is environmentally relevant as chicken litter is commonly used as fertilizer.
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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) | 0.000 | 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".