Differential expression and regulation of nuclear oligomerization domain proteins NOD1 and NOD2 in human endometrium: a potential role in innate immune protection and menstruation
Bibliographic record
Abstract
Nuclear oligomerization domains (NODs) are cytosolic pattern recognition receptors (PRRs), present in epithelial cells, monocytes and dendritic cells. This study details their expression, regulation and role in human endometrium. Real-time PCR showed that NOD1 mRNA is constitutively expressed in endometrium. NOD2 is up-regulated in the late secretory phase of the menstrual cycle suggesting a role in menstruation. Both proteins are immunolocalized in endometrial epithelium, stroma and endothelium. In first trimester, decidua NODs are present in decidualized stroma. NOD function was examined in endometrial stromal cells (ESCs) and endometrial epithelial cells (EEpCs) in vitro. IkappaBalpha is up-regulated by stimulation of ESC and EEpC with an NOD1 ligand. IkappaBalpha, IL-8 and TNFalpha mRNA expression is increased in EEpC by a NOD2 ligand. NOD2 mRNA expression increases in response to IL-1 treatment while NOD1 transcripts are unaltered. NOD1 mRNA is increased in an in vitro model of decidualization of ESC. In summary, we report expression of NOD1 and NOD2 in human endometrium and show that they are differentially regulated. NOD2 and, to a lesser extent, NOD1 can function to increase expression of innate immune molecules in endometrium. NODs may have a role in innate immune protection in the uterus and NOD2 may regulate inflammation associated with menstruation.
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) | 0.001 | 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".