Expression of gap junctional connexins 26, 32, and 43 mRNA in ovarian preovulatory follicles and corpora lutea in sheep
Bibliographic record
Abstract
The objective of the current study was to evaluate the expression of connexins (Cx)26, Cx32, and Cx43 mRNA in granulosa and theca cells during the peri-ovulatory period (experiment 1) and in the corpus luteum (CL) during the estrous cycle (experiment 2) and during prostaglandin F2alpha (PGF)-induced luteal regression (experiment 3) in FSH-treated ewes. In experiment 1, Cx26, Cx32, and Cx43 mRNA was expressed in granulosa and theca cells, and expression of Cx32 and Cx43 mRNA, but not Cx26, was greater (p<0.001) in granulosa than in theca cells throughout the peri-ovulatory period. Expression of Cx43 mRNA in granulosa and theca cells decreased (p<0.01) 24 h after hCG treatment. In experiment 2, expression of Cx26 mRNA in the CL tended to be greater (p<0.06) on day 10 than on days 5 or 15, but expression of Cx43 mRNA was greater (p<0.01) on day 5 than on days 10 and 15 of the estrous cycle. In experiment 3, expression of Cx26, but not Cx32 or Cx43 mRNA decreased (p<0.001) during PGF-induced luteal regression. In all 3 experiments, expression of Cx32 mRNA was much less than Cx26 and Cx43 mRNA. Moreover, Cx32 mRNA expression was unchanged during the peri-ovulatory period or during several stages of luteal development and PGF-induced regression of the CL. Thus, we have shown that the mRNA expression pattern of Cx26 and Cx43 changes during peri-ovulatory period and during several stages of the luteal development. This suggests that Cx26 and Cx43 play a role in ovarian tissue remodeling during the critical time around ovulation and throughout luteal tissue growth, differentiation, and regression in sheep.
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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".