Chaiyu-Dixian Formula Exerts Protective Effects on Ovarian Follicular Abnormal Development in Chronic Unpredictable Mild Stress (CUMS) Rat Model
Bibliographic record
Abstract
BACKGROUND: Chronic stress has been known to impair the female reproductive function, but the mechanism remains to be further investigated. Chaiyu-Dixian Formula (CYDXF) has been reported to regulate human endocrine disorders clinically. However, whether this formula can affect chronic stress-induced ovarian follicular development is not clear. AIM OF THE STUDY: To examine effects of CYDXF on follicular development and explore possible mech anisms in a chronic unpredictable mild stress (CUMS) model. MATERIALS AND METHODS: ) (0.1 mg/kg) group, CUMS+CYDXF (2.73 g/kg) group, and CUMS+CYDXF (5.46 g/kg) group. Body weights and behavioral tests were documented. Serum hormone levels were determined by enzyme-linked immunosorbent assay (ELISA). Western blotting was used to detect the protein levels in the PI3K/Akt pathway and brain-derived neurotrophic factor (BDNF). The follicles were analyzed and classified according to their morphological characterization. RESULTS: CYDXF relieved depression-like behaviors and ameliorated the abnormality in rat estrous cycle within the rat model of CUMS. Moreover, CYDXF could regulate endocrine disorders, increase the proportion of antral follicles as well as decrease the proportion of follicular atresia, which suggested that CYDXF could alleviate abnormal follicular development and improve overall ovarian function. Furthermore, CYDXF also activated the BDNF-mediated PI3K/Akt signaling pathway. CONCLUSIONS: CYDXF (at dose of both 2.73 and 5.46 g/kg) attenuated chronic stress-induced abnormal ovarian follicular development by relieving depression-like behaviors and improving ovarian function through partly the regulation of the BDNF-mediated PI3K/Akt pathway.
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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) | 0.001 | 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".