Maternal Obesity and the Uterine Immune Cell Landscape: The Shaping Role of Inflammation
Bibliographic record
Abstract
Inflammation is often equated to the physiological response to injury or infection. Inflammatory responses defined by cytokine storms control cellular mechanisms that can either resolve quickly (i.e., acute inflammation) or remain prolonged and unabated (i.e., chronic inflammation). Perhaps less well-appreciated is the importance of inflammatory processes central to healthy pregnancy, including implantation, early stages of placentation, and parturition. Pregnancy juxtaposed with disease can lead to the perpetuation of aberrant inflammation that likely contributes to or potentiates maternal morbidity and poor fetal outcome. Maternal obesity, a prevalent condition within women of reproductive age, associates with increased risk of developing multiple pregnancy disorders. Importantly, chronic low-grade inflammation is thought to underlie the development of obesity-related obstetric and perinatal complications. While diverse subsets of uterine immune cells play central roles in initiating and maintaining healthy pregnancy, uterine leukocyte dysfunction as a result of maternal obesity may underpin the development of pregnancy disorders. In this review we discuss the current knowledge related to the impact of maternal obesity and obesity-associated inflammation on uterine immune cell function, utero-placental establishment, and pregnancy health.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| 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".