The activity of transcription factor Stat5 responds to prolactin, growth hormone, and IGF-I in rat and bovine mammary explant culture.
Bibliographic record
Abstract
Signal transducer and activator of transcription-5 (Stat5) is known to play a critical role in prolactin-induced beta-casein gene transcription in rodents. In nonmammary cells, Stat5 is activated by multiple hormones and cytokines, including growth hormone. We hypothesized that Stat5 may serve as a common point in the signal transduction pathways of hormones that promote milk protein gene expression in bovine mammary cells, which are regulated by GH and IGF-I in addition to prolactin. Assays for Stat5 DNA binding activity and protein were validated in mammary explant culture. The Stat5 protein abundance was not changed by any of the short-term hormonal treatments used in our study, suggesting that short-term regulation of Stat5 is predominantly at the level of protein activation. Both rat and bovine explant culture showed a rapid stimulation of Stat5 DNA binding activity by prolactin, GH, and IGF-I at the high concentrations typically used in explant cultures as well as at levels within physiologic ranges. Growth hormone stimulated Stat5 activity at a lower concentration in bovine than in rat cultures, but in both species the presence of GH increased the response of Stat5 activity to prolactin. These results suggest that transcription factor Stat5 may represent part of a common route by which different extracellular signals converge and are transduced intracellularly to coordinately regulate cell function in the mammary gland.
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 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".