Embryonic Catalase Protects Against Ethanol-Initiated DNA Oxidation and Teratogenesis in Acatalasemic and Transgenic Human Catalase–Expressing Mice
Bibliographic record
Abstract
Reactive oxygen species (ROS) are implicated in fetal alcohol spectrum disorders (FASD) caused by alcohol (ethanol, EtOH). Although catalase detoxifies hydrogen peroxide, embryonic catalase activity is only about 5% of maternal levels. To determine the roles of ROS and embryonic catalase in FASD, pregnant mice with enhanced (expressing human catalase, hCat) or deficient (acatalasemic, aCat) catalase activity, or their respective wild-type (WT) controls, were treated ip on gestational day 9 with 4 or 6g/kg EtOH or its saline vehicle, and embryos and fetuses were, respectively, evaluated for oxidatively damaged DNA and structural anomalies. Untreated hCat and aCat dams had, respectively, more and less offspring than their WT controls. hCat progenies were protected from all EtOH fetal anomalies at the low dose (p < .01) and from reduced head diameter and resorptions at the high dose (p < .001). Conversely, aCat progenies were more sensitive to dose-dependent EtOH fetal anomalies (p < .001) and exhibited a 50% increase in maternal lethality (p < .05) at the high dose. Maternal pretreatment of aCat mice with polyethylene glycol-conjugated catalase (PEG-Cat) reduced EtOH fetal anomalies (p < .001). EtOH-initiated embryonic DNA oxidation was reduced in hCat and WT mice pretreated with PEG-Cat and enhanced in aCat mice. Plasma concentrations of EtOH in catalase-altered mice were similar to controls, precluding a pharmacokinetic basis for altered EtOH teratogenesis. Endogenous embryonic catalase, despite its low level, is an important embryoprotective enzyme for EtOH teratogenesis and a likely determinant of individual risk.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".