Faculty Opinions recommendation of Depletion of polymorphonuclear leukocytes has no effect on preterm delivery in a mouse model of Escherichia coli-induced labor.
Bibliographic record
Abstract
Objective-To investigate the role of polymorphonuclear leukocytes (PMNs) in a mouse model of E. coli-induced labor.Study Design-Intraperitoneal injection of rabbit anti-mouse PMN antiserum or control was performed in CD-1 mice 29 hours and 5 hours prior to laparotomy and intrauterine injection of either killed E. coli or phosphate buffered saline on Day 14.5 of pregnancy.Preterm delivery was defined as delivery of at least one pup within 48 hours.Circulating leukocyte counts were determined manually or by flow cytometry at the time of surgery and 8, 24 and 48 hours afterwards.Maternal and fetal tissues were analyzed in a separate group of animals 8 hours after surgery.Results-Pre-treatment with anti-PMN antiserum significantly decreased the numbers of circulating leukocytes and the proportion of neutrophils among all leukocytes by 70-80% at surgery and at least 8 hours thereafter.Neutrophil depletion significantly reduced two markers of neutrophil activation in uterus and placenta (neutrophil elastase and myeloperoxidase activity) and neutrophil infiltration into gestational tissues in bacterially treated animals to baseline (control) levels, but did not affect preterm birth rates.The large E. coli-induced increases in uterine inflammatory markers (interleukin 1β, tumor necrosis factor, CCL5, cyclooxygenase 2) were not affected or were only minimally affected by neutrophil depletion.Conclusions-Although PMN antiserum reduces both neutrophil number and activity, it does not diminish sensitivity to bacterially induced delivery or meaningfully alter the expression of
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.044 | 0.032 |
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".