Gene expression of porcine blastocysts from gilts fed organic or inorganic selenium and pyridoxine
Bibliographic record
Abstract
In this study, we determined how maternal dietary supplementation with pyridoxine combined with different sources of selenium (Se) affected global gene expression of porcine expanded blastocysts (PEB) during pregnancy. Eighteen gilts were randomly assigned to one of the three experimental diets (n=6 per treatment): i) basal diet without supplemental Se or pyridoxine (CONT); ii) CONT+0.3 mg/kg of Na-selenite and 10 mg/kg of HCl-pyridoxine (MSeB610); and iii) CONT+0.3 mg/kg of Se-enriched yeast and 10 mg/kg of HCl-pyridoxine (OSeB610). All gilts were inseminated at their fifth post-pubertal estrus and killed 5 days later for embryo harvesting. A porcine embryo-specific microarray was used to detect differentially gene expression between MSeB610 vs CONT, OSeB610 vs CONT, and OSeB610 vs MSeB610. CONT gilts had lower whole blood Se and erythrocyte pyridoxal-5-P concentrations than supplemented gilts (P<0.05). No treatment effect was observed on blood plasma Se-glutathione peroxidase activity (P=0.57). There were 10, 247, and 96 differentially expressed genes for MSeB610 vs CONT, OSeB610 vs CONT, and OSeB610 vs MSeB610 respectively. No specific biological process was associated with MSeB610 vs CONT. However, for OSeB610 vs CONT, upregulated genes were related with global protein synthesis but not to selenoproteins. The stimulation of some genes related with monooxygenase and thioredoxin families was confirmed by quantitative real-time RT-PCR. In conclusion, OSeB610 affects PEB metabolism more markedly than MSeB610. Neither Se sources with pyridoxine influenced the Se-glutathione peroxidase metabolic pathway in the PEB, but OSeB610 selectively stimulated genes involved with antioxidant defense.
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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 | 0.000 | 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".