Optimal Conditions for Successful In Vitro Fertilization and Subsequent Embryonic Development in Sprague-Dawley Rats1
Bibliographic record
Abstract
The present study was conducted to determine the optimal conditions for successful in vitro fertilization (IVF) in Sprague-Dawley (SD) rats. The IVF of oocytes from SD and Wistar rats was compared in different fertilization media (mR1ECM, IVF-20, and modified Krebs-Ringer bicarbonate solution [mKRB]), and IVF conditions were then optimized for oocytes of the SD strain. Results showed that in mR1ECM medium, fertilization rates were markedly lower in SD rats (15%) than in the Wistar strain (73%), although this response was significantly improved by increasing the NaCl concentration. In addition, fertilization rates in SD rats were higher in modified IVF-20 (73%) than in IVF-20 (18%) and mKRB (53%). In contrast, fertilization rates in Wistar rats were higher in IVF-20 and modified IVF-20 than in mKRB (78%, 74%, and 36%, respectively). Further investigation concerning the effects of the NaCl supplementation (10- 40 mM) in IVF-20 on the fertilization of oocytes in the SD strain indicated that significantly higher percentages of oocytes were fertilized in IVF-20 supplemented with 30 mM NaCl (66%) and developed to the blastocyst stage (47%) in vitro. After transfer, embryos derived from this IVF system developed to term at a percentage comparable to that of in vivo-fertilized controls. In conclusion, differences exist in optimal IVF conditions between rat strains, and a modified culture medium has been successfully developed for assessment of the developmental competence of oocytes in SD rats.
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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 | 0.001 |
| 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".