Decline in Number of Elevated Blood CD3<sup>+</sup> CD56<sup>+</sup> NKT Cells in Response to Intravenous Immunoglobulin Treatment Correlates with Successful Pregnancy
Bibliographic record
Abstract
PROBLEM: Patients with elevated blood natural killer (NK) cells may be offered intravenous immunoglobulin (IVIG) treatment, but there is controversy about the utility of blood NK cell testing. Human CD56(+) NK cells include several subpopulations that include the putatively cytotoxic CD56(+) CD16(+) subset. In mouse models of pregnant failure, NKT cells appear to be important. However, a mouse model may only be pertinent to a subset of patients, as recurrent pregnancy failure is a heterogenous group. METHOD OF STUDY: An ethics-approved observational study was done to observe the effect of treatment on total blood lymphoid cells, and subsets of CD56(+) blood lymphocytes including CD56(+) CD3(+) NKT cells determined by flow cytometry, and to correlate with pregnancy outcome. Fifteen fertile women with a history of successful pregnancy and thirty-one women suffering from repeated implantation failure or recurrent spontaneous abortion provided serial blood samples during one menstrual cycle or prior to and during treatment. IVIG was administered to the latter group with or without heparin/aspirin. RESULTS: Eight of thirty infertile women presented with high numbers of CD56(+) CD3(+) NKT cells, which declined after treatment with IVIG. The elevated NKT cell group with or without concomitant autoimmunity achieved a significantly higher successful pregnancy rate over the course of the study, as compared to women with average numbers of NKT cells and no evidence of autoimmunity (P = 0.018). Elevated NKT levels alone was an independent predictor of success on treatment (P = 0.003). CONCLUSION: Elevated NKT cells in recurrent pregnancy loss or implantation failure can be ameliorated with IVIG treatment, and result in successful pregnancy. Assay of NKT cell numbers may identify patients who are more likely to benefit from IVIG therapy and merits further examination in randomized phase II studies.
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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 | 0.002 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".