Limiting Premenstrual Endometrial Hypoxia Inducible Factor 2 Alpha May Fine-Tune Endometrial Function at Menstruation
Bibliographic record
Abstract
CONTEXT: Heavy menstrual bleeding (HMB) is common and debilitating, but the precise endometrial mechanisms causing increased menstrual blood loss (MBL) remain undefined. We have previously identified a role for hypoxia in endometrial repair following progesterone withdrawal. OBJECTIVE: As hypoxia inducible factor 2 alpha (HIF2A) is known to alter vascular function in other tissues, we hypothezised that endometrial HIF2A is involved in premenstrual optimization of endometrial function during the secretory phase to limit MBL. RESULTS: Women with objective HMB had higher endometrial HIF2A during the mid-secretory phase when compared to those with normal MBL (P = 0.0269). In a mouse model of simulated menses, genetic or pharmacological manipulation of HIF2A did not significantly affect endometrial breakdown/repair, volume of MBL or endometrial hypoxia. However, 88% of Hif2a heterozygote mice reached early-full repair by 24 hours vs only 65% of wild-type mice. Mean MBL was 0.39 μL (±0.67) in Hif2a heterozygote mice vs 0.98 μL (±0.79) in wild-type mice. Conversely, when we increased HIF2A before menstruation, 11% reached early repair by 8 hours vs 30% of vehicle-treated mice. Mean MBL was 2.61 μL (±1.10) in mice with HIF2A stabilization and 2.24 μL (±1.14) in vehicle-treated mice. These nonsignificant but consistent trends indicate that increased endometrial HIF2A may contribute to delayed endometrial repair and HMB. CONCLUSIONS: Increased HIF2A in the secretory endometrium is unlikely to be sufficient to account for the phenotype of HMB, but limitation of HIF2 levels may optimize endometrial function at menstruation.
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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 | 0.000 |
| 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".