IFN-τ Modulates PBMC Cytokine Profile and T Cell Phenotype to Improve Endometrial Immune Composition in the Implantation Window: A Combined In Vitro and In Vivo Study
Bibliographic record
Abstract
Embryo implantation requires a finely tuned immune balance at the maternal–fetal interface. Interferon tau (IFN-τ), a key immunomodulator in ruminant implantation, may have therapeutic potential in human reproduction. This study investigated its effects on peripheral blood mononuclear cells (PBMCs) in vitro and the subsequent impact on endometrial immune composition following intrauterine administration of these cells. The work was conducted in two stages. First, in vitro assays were performed with PBMCs from 20 patients with recurrent implantation failure (RIF) cultured with or without IFN-τ for 24 h. Cytokines (IL-10, IL-4, TNF-α, IL-6) were measured by ELISA, and T cell subsets (Th, cytT, Th1, Th2, Th9, Tfh, Th17, Treg) were analyzed by flow cytometry. IFN-τ increased IL-4 and reduced TNF-α and IL-6, indicating a Th2 profile shift. T-cell analysis revealed fewer cytT, Th1, Th9, and Th17 cells, more Th2 cells, and improved Th/Tk, Th1/Th2, and Th17/Treg ratios after IFN-τ. A second clinical study included 55 RIF patients who received intrauterine IFN-τ-modulated PBMCs. Post-treatment endometrial biopsies revealed more helper T cells and macrophages, with higher Th/total T, Th/cytT, and Th/macrophage ratios, suggesting a tolerogenic environment. Overall, IFN-τ modulates PBMCs in vitro and promotes a favorable endometrial immune profile in vivo, highlighting its potential as an immunotherapy in assisted reproduction.
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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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".