Inhibition of apoptosis in vitellogenic ovarian follicles of rainbow trout (<i>Oncorhynchus mykiss</i>) by salmon gonadotropin, epidermal growth factor, and 17β‐estradiol
Bibliographic record
Abstract
We have examined the ability of selected hormones and growth factors to suppress the spontaneous onset on apoptotic DNA fragmentation in isolated vitellogenic rainbow trout ovarian follicles cultured in serum-free conditions. Primary culture of isolated follicles for 24 hr in serum-free conditions resulted in a 3-5-fold increase in the amount of fragmented DNA as compared to non-cultured controls, measured by radioactive 3'end-labeling. Culture in medium containing salmon gonadotropin (SG-G100; 1, 5 microg/ml) suppressed the spontaneous onset of DNA fragmentation in dose-dependent fashion. Culture with 1 ng/ml 17beta-estradiol, or 100 ng/ml epidermal growth factor also suppressed the spontaneous onset of apoptosis, whereas culture with higher concentrations of 17beta-estradiol (10 and 100 ng/ml), insulin-like growth factor I (IGF-I; 1, 10, and 100 ng/ml), or 8-bromo-cAMP (0.1, 1, and 5 mM) was ineffective in suppressing apoptosis. Apoptosis was confirmed as the mode of cell death through positive identification of nuclear morphological characteristics associated with apoptosis, and positive staining for fragmented DNA using in situ end-labeling (TUNEL); apoptotic cells identified in situ were almost exclusively localized to the thecal/epithelial region of the follicle. In summary, this study shows that vitellogenic ovarian follicles are susceptible to apoptosis and that both endocrine and locally-derived growth factors may play a role as cell survival factors by preventing apoptosis. The study also suggests that rainbow trout differ markedly from mammals both in terms of the cell types susceptible to apoptosis and the responsiveness to specific growth factors in terms of inhibiting apoptosis.
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 | 0.000 |
| 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) | 0.000 | 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".