Neither lymphotoxin α nor lymphotoxin β receptor expression is required for biogenesis of lymphoid aggregates or differentiation of natural killer cells in the pregnant mouse uterus
Bibliographic record
Abstract
Gene ablation studies in mice indicate that lymphotoxin (LT)alpha, LTbeta and LTbetaR are essential for the genesis of lymph nodes (LN), normal structural development of peripheral lymphoid tissues and the differentiation of natural killer (NK) cells. LTbetaR binds to the heterotrimeric cytokines LTalpha1beta2 and LIGHT. LTs also regulate stromal cell expression of lymphocyte homing chemokines. Uterine decidualization in normal (+/+) mice is accompanied by the appearance and maturation of large numbers of uterine NK (uNK) cells that differentiate from precursors mobilized to the uterus from secondary lymphoid tissues. uNK cells accumulate in a transient, lymphocyte-rich region known as the metrial gland or, more recently, the mesometrial lymphoid aggregrate of pregnancy (MLAp). To determine if LTs contribute to development of the MLAp, and to the differentiation and/or localization of uNK cells, a histological study was undertaken of implantation sites from LTalpha null, LTbetaR null and gestation day-matched, normal mice. Implantation sites from the gene-ablated mice contained abundant numbers of uNK cells that localized appropriately. This indicates that the stromally derived molecules supporting NK cell differentiation in the uterus differ from those used in secondary lymphoid organs.
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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 | 0.001 |
| 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".