A Leukocyte Migration Assay Assists Understanding of Interleukin-1β-Induced Leukocyte Migration Into Preterm Mouse Uterus
Bibliographic record
Abstract
Neutrophils and other leukocytes invade the mouse uterus at term birth, which is normal for activating the uterus for labor. To better understand the regulation of this migration at term and interleukin (IL)-1β—induced preterm birth, we developed a mouse leukocyte migration assay (mLMA) and used it with rytvela, an IL-1 receptor allosteric antagonist. The mLMA uses term peripheral blood leukocytes that migrate in a Boyden chamber in response to a chemoattractant. We tested several mouse uterine tissues after homogenization and sedimentation of debris for chemoattractant activity. The most active chemoattractant homogenate came from the mouse lower uterus on gestational day (GD) 18.5. Using flow cytometry, we demonstrated that 99% of the cells that migrate are neutrophils. IL-1β administered on GD 16 stimulated neutrophil migration and invasion into the uterus and the fetal brain along with preterm birth on GD 17. Preterm birth and the increased leukocyte invasion of the maternal uterus and fetal brain were all blocked by the co-administration of rytvela. To test where the site of IL-1β action might be, we examined the potency of lower uterine chemoattractant and the activation of leukocytes following IL-1β +/- rytvela administration. IL-1β did not increase lower uterus homogenate chemoattractant activity, but it significantly (p < 0.05) increased leukocyte activation as defined by cytokine and chemokine expression. Rytvela blocked this activation of leukocytes by IL-1β. We conclude that IL-1β stimulates preterm birth in mice by increasing leukocyte activation leading to increased uterine and fetal brain leukocyte invasion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".