Investigating Combined Treatment of Menstrual Blood-Derived and Adipose-Derived Mesenchymal Stem Cell Conditioned Media in Endometriosis Mouse Models: A Research Protocol
Bibliographic record
Abstract
Endometriosis is a debilitating disease characterized by endometrial-like tissue abnormally growing in extrauterine regions, which causes symptoms that diminish one's quality of life and reduces fertility. Treatment currently involves invasive surgical procedures, and there is a high chance of lesion recurrence after removal. Novel research has demonstrated the potential of using conditioned media (CM) from various stem cells for regenerative properties. The proposed study aims to examine and compare the individual and coupled effects of menstrual-derived stem cell-conditioned media (menSC-CM) and adipose stem cell-conditioned media (adSC-CM) on endometrial tissue regeneration in mice models. The purity of stem cells obtained from human donors will be assessed using flow cytometric analysis with established cell markers. Nine-week-old C57BL/6 female mice will then undergo surgical engraftment of extrauterine tissue from transgenic eGFP donor mice, with groups receiving adSC-CM, menSC-CM, a combined therapy, or no treatment. A negative control group will also be included. Tissue collection will occur after the random allocation of mice to be sacrificed or mated. Previously recognized endometriotic markers, ICAM-1 and VEGF will be assessed via reverse transcription polymerase chain reaction (RT-PCR) to calculate gene expression levels and via immunohistochemistry to calculate the H-score. The H-score for GFP will also be calculated. Hematoxylin & Eosin (H&E) staining will be used for quantitative analysis of endometriosis lesion size. It is expected that all experimental groups receiving treatments will have significantly reduced endometriotic lesion size, with the group receiving the combined treatment having the largest reduction. However, as this combined therapy is yet to be studied, it could also have alternative effects. Similarly, it is predicted that mice receiving combined therapy will have the most improved pregnancy outcomes compared to the positive control and will have the lowest H-scores for ICAM-1, VEGF and GFP. This study has implications in providing insights that may be used for developing novel non-invasive treatment options, using mesenchymal stem cell source therapy, that aim to reduce endometriotic lesions and promote patients' quality of life.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".