Transforming growth factor-beta family members are regulated during induced luteolysis in cattle
Bibliographic record
Abstract
The transforming growth factors beta (TGFβ) are local factors produced by ovarian cells which, after binding to their receptors, regulate follicular deviation and ovulation.However, their regulation and function during corpus luteum (CL) regression has been poorly investigated.The present study evaluated the mRNA regulation of some TGFβ family ligands and their receptors in the bovine CL during induced luteolysis in vivo.On day 10 of the estrous cycle, cows received an injection of prostaglandin F2α (PGF) and luteal samples were obtained from separate groups of cows (n= 4-5 cows per time-point) at 0, 2, 12, 24 or 48 h after treatment.Since TGF beta family comprises more than 30 ligands, we focused in some candidates genes such as activin receptors (ACVR-1A, -1B, -2A, -2B) AMH, AMHR2, BMPs (BMP-1, -2, -3, -4, -6 and -7), BMP receptors (BMPR-1A, -1B and -2), inhibin subunits (INH-A, -BA, -BB) and betaglycan (TGFBR3).The mRNA levels of BMP4, BMP6 and INHBA were higher at 2 h after PGF administration (P<0.05) in comparison to 0 h.The relative mRNA abundance of BMP1, BMP2, BMP3, BMP4, BMP6, ACVR1B, INHBA and INHBB was upregulated up to 12 h post PGF (P<0.05).On the other hand, TGFBR3 mRNA that codes for a reservoir of ligands that bind to TGF-beta receptors, was lower at 48 h.In conclusion, findings from this study demonstrated that genes encoding several TGFβ family members are expressed in a time-specific manner after PGF administration.
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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".