Low-dose metronomic delivery of cyclophosphamide is less detrimental to granulosa cell viability, ovarian function, and fertility than maximum tolerated dose delivery in the mouse†
Bibliographic record
Abstract
Chemotherapy can cause early menopause or infertility in women and have a profound negative impact on the quality of life of young female cancer survivors. Various factors are known to influence the risk of chemotherapy-induced ovarian failure, including the drug dose and treatment duration; however, the scheduling of dose administration has not yet been evaluated as an independent risk factor. We hypothesized that low-dose metronomic (LDM) chemotherapy scheduling would be less detrimental to ovarian function than the traditional maximum tolerated dose (MTD) strategy. In vitro, MTD cyclophosphamide exposure resulted in decreased proliferation and increased granulosa cell apoptosis, while cells treated with LDM cyclophosphamide were not different from untreated controls. Treatments of MTD cyclophosphamide induced high levels of follicle atresia and enhanced follicle recruitment in mice. In contrast, LDM delivery of an equivalent dose of cyclophosphamide reduced growing follicle numbers, but was not associated with higher levels of follicle atresia or recruitment. MTD cyclophosphamide induced significant vascular disruption and DNA damage in vivo, while LDM chemotherapy with equal cumulative amounts of cyclophosphamide was not different from controls. MTD chemotherapy also had a negative effect on mouse-fertility outcomes. Our findings suggest that LDM scheduling could potentially minimize the long-term effects of cyclophosphamide on female fertility by preventing follicle depletion from enhanced activation.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".