Progesterone enhances HLA-G gene expression in JEG-3 choriocarcinoma cells and human cytotrophoblasts in vitro
Bibliographic record
Abstract
BACKGROUND: Evidence suggests that HLA-G plays a critical role in maternal immune tolerance to the fetus. However, regulation of HLA-G gene expression is not well understood. Many studies have suggested that progesterone may also be important in suppressing maternal immune response to the fetus. Therefore, we hypothesized that this steroid hormone may play a role in regulating HLA-G gene expression. The objective of the study was to explore potential effects of progesterone on HLA-G gene expression in vitro. METHODS: Cultured first trimester trophoblasts and JEG-3 choriocarcinoma cells were treated with progesterone and its antagonist RU486. HLA-G gene transcription was determined by real-time PCR while HLA-G translation was investigated by a specific enzyme-linked immunosorbent assay for HLA-G and western blot analysis. RESULTS: HLA-G mRNA and protein expression in trophoblasts and JEG-3 cells were elevated by progesterone in dose- and time-dependent manners. The effect of progesterone can be completely inhibited by co-incubation with RU486 at the same concentrations. CONCLUSION: Progesterone has an up-regulatory effect on HLA-G gene expression in first trimester trophoblasts and JEG-3 cells in vitro.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".