Immunological Mechanisms Affecting Angiogenesis and Their Relation to Porcine Pregnancy Success
Bibliographic record
Abstract
Prenatal mortality due to loss of lymphocyte-promoted endometrial angiogenesis is being investigated as a major cause of litter reductions during pregnancy in pigs. This review discusses immune mechanisms influencing porcine endometrial angiogenesis as well as additional signalling molecules that may play important roles in the compromise of peri-implantation and mid-gestation fetal pig survival. These include dendritic cells, signalling molecules such as toll-like receptors, chemokines and ficolins. Together these cells and molecules regulate immune responses and, ideally, protect the mother and prevent immune-based conceptus losses. Dendritic cells were recently shown to be angiogenic. Their tolerogenic role at the maternal-fetal interface coupled with the ability to secrete and respond to angiogenic factors suggests that dendritic cells are the key coordinators of angiogenesis at the porcine maternal-fetal interface. Chemokines coordinate the localization of immune effector and endothelial cells. The balance between pro-angiogenic and anti-angiogenic chemokines is addressed in relation to conceptus viability. Ficolins, components of the lectin-mediated complement activation pathway, are used for self/non-self recognition. Together, these components of the immune system could regulate lymphocyte- and non-lymphocyte-promoted endometrial angiogenesis to determine conceptus survival.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".