Evaluation of Natural Killer Cell Recruitment to Embryonic Attachment Sites During Early Porcine Pregnancy1
Bibliographic record
Abstract
Specialized natural killer (NK) lymphocytes are a feature of the pregnant uterus in humans and rodents. Conceptus-mediated recruitment of uterine (u)NK cells in the pig was proposed based on evidence that elevated uNK activity was temporally associated with increased leukocyte density in endometrium underlying conceptuses. The objective of this study was to determine whether uNK cells were more abundant at embryonic attachment sites during the early postattachment period. Mononuclear leukocytes were isolated from endometrium at attachment sites versus between attachment sites, and expression of CD16, a marker for NK cells, was assessed by flow cytometry. CD16 binding was normalized to leukocyte numbers in each sample. CD16+ small lymphocytes were more frequent in uterus than in blood (41% +/- 2% versus 26% +/- 4%). Differences between pregnant and luteal phase uterus (43% +/- 2% versus 31% +/- 7%, respectively) were not statistically significant. In pregnant animals, CD16+ lymphocytes were slightly but significantly more abundant in uterus at attachment sites versus between attachment sites at Days 15-17, 21-22, and 25-28. Before normalization, CD16+ large, granular cells were more abundant at attachment sites versus between attachment sites; however, these differences were removed when data were normalized according to leukocyte numbers. Further characterization showed that the proportion of large granular leukocytes expressing CD8, reactive with NK cells and T cell subsets, was 2-fold higher in pregnant uterus than in maternal blood. These results raise the possibility that uNK cells resembling those in blood may be transformed into larger, more granulated forms in the uterine microenvironment.
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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".