<sup> </sup>Intra-ovarian injection of bone marrow-derived c-Kit<sup>+</sup> cells for ovarian rejuvenation in menopausal rats
Bibliographic record
Abstract
Introduction: Cell-based therapies with certain cell types are touted as novel and hopeful therapeutic intervention in the clinical setting. Here, we aimed to assess the regenerative potential of c-Kit+ cells in the rejuvenation of ovarian tissue and fertility rate in rat model of premature ovarian failure (POF). Methods: Rats were treated with 160 mg/kg/BW of 4-vinylcyclohexene dioxide for 15 days. Freshly enriched rat bone marrow-derived c-Kit+ (MACS) and c-Kit- cells (4×105 cells/10 µL) were transplanted into the ovaries of treatment and control animals. Prior to transplantation as well as 2, 4, 6, and 8 weeks post-transplantation, randomly-selected rats were euthanized and ovarian tissues were subjected to pathophysiological examinations and real-time PCR analyses. Results: POF status was confirmed by the presence of pathological features and a decreased number of immature and mature follicles compared with the control group (P < 0.05). Histological examination revealed a substantial reduction of atretic follicles in POF rats receiving c-Kit+ cells in comparison with POF rats that did not receive these cells (P < 0.05). Compared with the control samples, angiogenesis-related genes, Angpt2 and KDR, showed increased and decreased expressions in POF ovaries, respectively (P < 0.05). c-Kit+ cells had potential to restore angiogenesis in the ovarian tissue within normal ranges. Systemic levels of FSH did not significantly change in pre- or post-transplantation time points for any group (P > 0.05). Notable reduction of collagen deposition was found in c-Kit-treated rats. Transplantation of c-Kit+ cells also restored the reduced fertility rate (P < 0.05). Conclusion: The administration of c-Kit+ cells can modulate angiogenesis and pathological changes, leading to the rejuvenation of ovarian function of a rat model of premature menopause.
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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) | 0.003 | 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".