Increasing Oocyte Yield Through the Modification of Hormone Delivery
Bibliographic record
Abstract
The McGill Integrated Core for Animal Modeling (MICAM) creates mouse disease models using CRISPR-Cas9 technology. A key step to generating genetically modified animal models is to produce fertilized oocytes. To obtain large numbers of oocytes, female mice must be superovulated by hormone injections. Typically, superovulation is induced by the administration of five international units (IU) of pregnant mare serum gonadotropin (PMSG) and five IU of human chorionic gonadotropin (hCG) by intraperitoneal (IP) injections 48 hours apart. However, a recent report has shown the administration of PMSG by subcutaneous (SC) injection results in a higher average yield of oocytes per mouse. This would allow the superovulation of fewer mice to generate the same number of oocytes, a key refinement to the process. This study split cohorts of female mice into two groups per injection session. Half of a cohort was given hormones using the standard superovulation regime, and half were given PMSG SC. Both cohorts were given hCG IP. On average, 23.58 oocytes were collected per female mouse given PMSG SC, while 16.43 oocytes were collected per female mouse given PMSG IP. This resulted in 7.15 more oocytes collected per female mouse administered PMSG SC rather than IP. For every three mice injected with PMSG IP, two females need to be injected with PMSG SC to collect the same number of oocytes. This study demonstrates that the administration of PMSG SC does result in the collection of more oocytes per mouse, reducing the number of female mice needed to be housed and superovulated.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".