An Overview of Molecular and Cellular Mechanisms Associated with Porcine Pregnancy Success or Failure
Bibliographic record
Abstract
Prenatal mortality remains one of the major constraints for the commercial pig industry in North America. Twenty to thirty per cent of the conceptuses are lost early in gestation and an additional 10-15% is lost by mid-to-late gestation. Research over the last two decades has provided critical insights into how uterine capacity, placental efficiency, genetics, environment, nutrition and immune mechanisms impact successful conceptus growth; however, the exact cause and effect relationship in the context of foetal loss has yet to be determined. Similar to other mammalian species such as the human, mouse, rat, and primates, immune cell enrichment occurs at the porcine maternal-foetal interface during the window of conceptus attachment. However, unlike other species, immune cells are solely recruited by conceptus-derived signals. As pigs have epitheliochorial placentae where maternal and foetal tissue layers are separate, it provides an ideal model to study immune cell interactions with foetal trophoblasts. Our research is focused on the immune-angiogenesis axis during porcine pregnancy. It is well established that immune cells are recruited to the maternal-foetal interface, but their pregnancy specific functions and how the local milieu affects angiogenesis and inflammation at the site of foetal arrest remain unknown. Through a better understanding of how immune cells modulate crosstalk between the conceptus and the mother, it might be possible to therapeutically target immune cells and/or their products to reduce foetal loss. In this review, we provide evidence from the literature and from our own work into the immunological factors associated with porcine foetal loss.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".