Interactive Effects of Protein Deficiency and Intestinal Nematode Infection During Pregnancy on Expression of Growth Genes in Placenta of Mice
Bibliographic record
Abstract
Background Protein deficiency (PD) and intestinal nematode infection during pregnancy impair distinct aspects of fetal growth. We used microarrays to test the hypotheses that maternal PD and nematode infection have interactive effects on placental functions related to growth and that this would be evident in altered placental gene expression Methods Placentae were obtained on day 18 of pregnancy from CD‐1 mice fed protein sufficient (24% protein) or protein deficient (6% protein) isoenergetic diets and either uninfected or infected every 5 days with Heligmosomoides bakeri beginning on day 5 of pregnancy. Pathway analysis was performed on differentially expressed genes identified by Affymetrix GeneChip 2.0 ST mouse arrays. Results In response to H. bakeri , 323 transcripts were differentially expressed, including oxidative phosphorylation, ATP binding and hemopoiesis genes. In response to PD, 272 transcripts were differentially expressed, including endopeptidase activity and hemopoiesis genes. A significant interaction was observed for 248 transcripts, including several genes with functions in fetal growth. Notably, infection prevented PD‐induced down‐regulation of IGF‐1 receptor, insulin receptor substrate and prolactin. Conclusion In addition to their independent effects, the interaction of maternal PD and intestinal nematode infection had antagonistic effects on placental genes involved in fetal growth. These results highlight the complexity of fetal growth regulation during combined malnutrition and infection. Funding Natural Science and Engineering Council of Canada
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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) | 0.001 | 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".