Pretreatment with periodate‐oxidized adenosine enhances developmental toxicity of inorganic arsenic in mice
Bibliographic record
Abstract
BACKGROUND: Inorganic arsenic, given by injection to pregnant laboratory animals, can induce malformations. Arsenic methylation can be inhibited by periodate-oxidized adenosine (PAD). Severe human health effects from high chronic arsenic exposure have mainly been reported in populations with significant levels of malnutrition, which may enhance toxicity by diminishing arsenic methylating capacity. This study sought to determine the effect of inhibition of arsenic methylation on the developmental toxicity of arsenic in a mammalian model. METHODS: PAD (100 microM/kg, i.p.), was given to pregnant CD-1 strain mice 30 min before 7.5mg/kg sodium arsenite [As(III)], i.p., or 17.9 mg/kg sodium arsenate [As(V)], i.p., on gestation day 8 (GD 8; copulation plug = GD 0). Control dams received As(III), As(V), or PAD alone or were untreated. Test dams were killed on GD 17, and their litters were examined for mortality and gross and skeletal defects. RESULTS: Pretreatment with PAD before either arsenical resulted in increased maternal toxicity and lower fetal weights. Pretreatment also caused higher prenatal mortality, with 8 of 21 and 5 of 17 litters totally resorbed in the PAD plus As(III) and PAD plus As(V) treatment groups, respectively. Significant increases in the incidences of exencephaly, ablepharia, and anomalies of the vertebral centra, sternebrae, and ribs were also associated with PAD pretreatment. Short tail (3 fetuses in 3 litters) was seen only following PAD plus As(III) treatment. CONCLUSIONS: These results demonstrate that the developmental toxicity of inorganic arsenic can be enhanced by PAD, due possibly to inhibited methylation of arsenic.
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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) | 0.001 | 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 | 0.001 |
| 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".