Mesenchymal stem cell-derived angiogenin promotes primodial follicle survival and angiogenesis in transplanted human ovarian tissue
Bibliographic record
Abstract
BACKGROUND: We have recently reported that human bone marrow-derived mesenchymal stem cells (MSCs) facilitate angiogenesis and prevent follicle loss in xenografted human ovarian tissues. However, the mechanism underlying this effect remains to be elucidated. Thus, determining the paracrine profiles and identifying the key secreted factors in MSCs co-transplanted with ovarian grafts are essential for the future application of MSCs. METHODS: In this study, we used cytokine microarrays to identify differentially expressed proteins associated with angiogenesis in frozen-thawed ovarian tissues co-transplanted with MSCs. The function of specific secreted factors in MSCs co-transplanted with human ovarian tissues was studied via targeted blockade with short-hairpin RNAi and the use of monoclonal neutralizing antibodies. RESULTS: Our results showed that angiogenin (ANG) was one of the most robustly up-regulated proteins (among 42 protein we screened, 37 proteins were up-regulated). Notably, the targeted depletion of ANG with short-hairpin RNAi (shANG) or the addition of anti-ANG monoclonal neutralizing antibodies (ANG Ab) significantly reversed the MSC-stimulated angiogenesis, increased follicle numbers and protective effect on follicle apoptosis. CONCLUSION: Our results indicate that ANG plays a critical role in regulating angiogenesis and follicle survival in xenografted human ovarian tissues. Our findings provide important insights into the molecular mechanism by which MSCs promote angiogenesis and follicle survival in transplanted ovarian tissues, thus providing a theoretical basis for their further application.
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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 | 0.000 |
| 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".