UTERINE AND PERIPHERAL NK CELLS IN RECURRENT SPONTANEOUS ABORTION: A QUALITATIVE SYSTEMATIC REVIEW OF BIOMARKER MECHANISMS (KIR/HLA-C) AND IMMUNOMODULATIVE THERAPIES (2010-2025)
Bibliographic record
Abstract
Objective: To critically synthesize evidence (2010–2025) on the role of uterine (uNK) and peripheral (pNK) Natural Killer cells in recurrent pregnancy loss (RPL), covering uNK biology, KIR/HLA-C genetics, clinical utility of NK testing, and the effects of immunomodulatory therapies (intravenous immunoglobulin, corticosteroids, lipid emulsion). Methods: Systematic review (PRISMA-2020) conducted across PubMed/MEDLINE, Embase, Web of Science, and Scopus (Jan-2010–Oct-2025). Human observational studies, clinical trials, and systematic reviews assessing uNK/pNK, KIR/HLA-C, reproductive outcomes (live birth, miscarriage recurrence, preeclampsia), or NK- targeted therapies were included. Risk of bias: RoB-2 (RCTs) and Newcastle–Ottawa (observational). Narrative qualitative synthesis. Results: Evidence supports a pivotal role of uNK in spiral artery remodeling and maternal–fetal tolerance. KIR/HLA-C combinations, notably maternal KIR AA with fetal HLA-C2, were associated with increased RPL risk, though findings were heterogeneous. Clinical NK testing remains unstandardized and not recommended for therapeutic selection per ESHRE 2022/2023 guidelines. Immunotherapies such as IVIG showed potential benefits in selected subgroups by reducing NK cytotoxicity and improving live birth rates, yet evidence remains moderate; prednisolone trials are ongoing. Conclusions: uNK and pNK are key regulators of reproductive immunology. The KIR/HLA-C pathway may influence RPL risk, but standardized phenotyping and large randomized multicenter trials are required. Routine NK testing is not endorsed by current guidelines, and immunotherapies remain investigational outside research protocols.
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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 | 0.022 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".