Consumption of bee pollen affects rat ovarian functions
Bibliographic record
Abstract
The aim of this study was to examine possible effects of bee pollen added to the feed mixture (FM) on rat ovarian functions (secretion activity and apoptosis). We evaluated the bee pollen effect on the release of insulin-like growth factor I (IGF-I) and steroid hormones (progesterone and estradiol), as well as on the expression of markers of apoptosis (Bcl-2, Bax and caspase-3) in rat ovarian fragments. Female rats (n = 15) were fed during 90 days by FM without or with rape seed bee pollen in dose either 3 kg/1000 kg FM or 5 kg/1000 kg FM. Fragments of ovaries isolated from rats of each group (totally 72 pieces) were incubated for 24 h. Hormonal secretion into the culture medium was detected by RIA. The markers of apoptosis were evaluated by Western blotting. It was observed that IGF-I release by rat ovarian fragments was significantly (p < 0.05) decreased; on the other hand, progesterone and estradiol secretion was increased after bee pollen treatment at dose 5 kg/1000 kg FM but not at 3 kg/1000 FM. Accumulation of Bcl-2 was increased by bee pollen added at 3 kg/1000 kg FM, but not at higher dose. Accumulation of Bax was increased in ovaries of rats fed by bee pollen at doses either 3 or 5 kg/1000 kg FM, whilst accumulation of caspase-3 increased after feeding with bee pollen at dose 5 kg/1000 kg FM, but not at 3 kg/1000 kg FM. Our results contribute to new insights regarding the effect of bee pollen on both secretion activity (release of growth factor IGF-I and steroid hormones progesterone and estradiol) and apoptosis (anti- and pro-apoptotic markers Bcl-2, Bax and caspase-3). Bee pollen is shown to be a potent regulator of rat ovarian functions.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".