Increased Expression of a Novel Heat Shock Protein Transcript in the Mouse Uterus During Decidualization and in Response to Progesterone1
Bibliographic record
Abstract
The objective of the present study was to identify and characterize transcripts whose levels are increased in the mouse uterus during decidualization. Using the method of suppression subtractive hybridization, we identified a novel transcript. This transcript contained a potential open reading frame that coded for a 196-amino-acid protein that shows homologies to the heat shock protein 20 family of genes. This transcript was expressed in several adult tissues and in the embryo. Its steady-state level was significantly greater in implantation segments of the uterus compared to nonimplantation segments. Furthermore, the steady-state levels of this novel transcript were significantly greater in uterine horns undergoing artificially induced decidualization compared to control contralateral horns. Using in situ hybridization methods, signals for the transcript were localized to the endometrial stromal cells that were undergoing decidualization. Finally, we found that progesterone caused a significant increase in the steady-state level of this novel transcript in the uterus when administered to ovariectomized mice. In the presence of estradiol-17 beta, this effect was significantly reduced. In conclusion, we have identified a novel transcript of a potential heat shock protein whose level is significantly increased in the uterus during decidualization and in response to progesterone.
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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) | 0.001 | 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".