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Record W2989815900 · doi:10.1071/rdv32n2ab200

200 Maturation method affects lipid accumulation in bovine oocytes

2019· article· en· W2989815900 on OpenAlexaff
Otávio Augusto Costa de Faria, T. S. Kawamoto, Luzia Renata Oliveira Dias, Andrei Antonioni Guedes Fidelis, L. O. Leme, José Felipe Warmling Sprícigo, Margot Alves Nunes Dode

Bibliographic record

VenueReproduction Fertility and Development · 2019
Typearticle
Languageen
FieldMedicine
TopicReproductive Biology and Fertility
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsOocyteOvulationAndrologyIn vitro maturationEmbryoIn vivoBiologyFollicleIn vitroEmbryo transferFolliculogenesisChemistryCryopreservationEndocrinologyCell biologyBiochemistryBiotechnologyMedicineHormone

Abstract

fetched live from OpenAlex

In vitro maturation is a key step in in vitro embryo production, since its success will depend on the availability of good quality oocytes. Previous studies have shown that it is during this stage that the greatest accumulation of lipid droplets occurs, which is reflected in the amount of lipid present in embryos produced in vitro. However, this is not observed when maturation is performed in vivo. Therefore, we hypothesised that lipid accumulation would be avoided if oocyte maturation were carried out in ovarian follicles following intrafollicular transfer of immature oocytes (IFIOT). We compared lipid accumulation in oocytes matured in vitro, in vivo, and by IFIOT. A total of 90 Nellore heifers were distributed in 3 experimental groups: donors of immature oocytes (D-IMA), ovulators of IFIOT oocytes (D-OV), and superstimulated donors of in vivo-matured oocytes (D-FSH). All animals rotated through all groups during the experiment. To obtain immature oocytes, the D-IMA were submitted to ovum pickup (OPU), in which aspiration medium was supplemented with 500 μM 3-isobutyl-1-methylxanthine (IBMX), and, after selection, part of the oocytes were cultured in vitro for 22 h (MatF) and part were used for IFIOT (MatT). To perform MatT, the D-OV had their ovulation synchronized by a progesterone and benzoate oestradiol protocol, in which 30 h after the implant removal, the IFIOT was performed on the dominant follicle. The D-FSH oocytes were stimulated with 80 mg of FSH (Folltropin; Vetoquinol) over 4 days, every 12 h, in decreasing doses. At the same time that the immature oocytes were placed in MatF and IFIOT, ovulation was induced with the gonadotrophin releasing hormone (GnRH) analogue (50 µg of lecirelin) in D-OV and D-FSH groups. After 22 h, matured oocytes were either removed from culture (MatF) or recovered from follicles by OPU (MatT and MatS). From the recovered oocytes of all groups, only those with a polar body were used for lipid droplet evaluation. To quantify lipid accumulation, denuded oocytes were fixed and stained with boron-dipyrromethene (Bodipy) 493/503 (20 µg mL−1) and evaluated by confocal microscopy. Captured images were evaluated in the ImageJ program (National Institutes of Health), and lipid content was determined by calculating the ratio of the area of the lipid droplets to total oocyte area. Data were analysed by ANOVA with statistical significance set at P < 0.05. A total of 95 oocytes were evaluated: 25 immature (CT), 24 in vitro (MatF), 30 in vivo (MatS), and 16 in vivo (MatT). The mean area containing lipid droplets in immature oocytes (14% ± 0.9) was similar (P > 0.05) to that observed in both in vivo maturation systems (MatS = 17.26% ± 0.8 and MatT = 14.11% ± 0.9). However, in the MatF oocytes, lipid content (24.34% ± 1) increased during maturation and was higher than in the other groups (P < 0.05). We showed for the first time that oocytes matured by IFIOT are similar to those in vivo matured with regard to lipid content, which may imply their superior quality over those matured in vitro. This new maturation method opens new possibilities for biotechnologies that need to use mature oocytes, such in vitro embryo production, oocyte and embryo cryopreservation, cloning, and transgenesis. This study was supported by FAP-DF and Capes.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.118
Threshold uncertainty score0.547

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.042
GPT teacher head0.329
Teacher spread0.288 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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