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Record W4401526152 · doi:10.3390/ani14162331

MicroRNAomic Analysis of Spent Media from Slow- and Fast-Growing Bovine Embryos Reveal Distinct Differences

2024· article· en· W4401526152 on OpenAlexafffund
Paul Del Rio, Sierra DiMarco, Pavneesh Madan

Bibliographic record

VenueAnimals · 2024
Typearticle
Languageen
FieldMedicine
TopicReproductive Biology and Fertility
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBlastocystEmbryoZygoteBiologyAndrologyOocyteEmbryogenesisCell biologyEmbryo qualitymicroRNAGeneticsGeneMedicine

Abstract

fetched live from OpenAlex

In bovine embryos, the microRNA (miRNA) expression has been profiled at each stage of early development in vitro. The miRNAomic analysis of spent media has the potential to reveal characteristics of embryo health; however, applications are limited without categorizing miRNA profiles by embryo quality. Time-lapse imaging has shown the timing of embryo development in vitro may be indicative of their developmental potential. The study aimed to characterize miRNAs in the spent media of bovine embryos with different growth rates during the pre-implantation phase. Bovine cumulus-oocyte complexes were aspirated from ovaries, fertilized, and cultured to blastocyst stage of development. At the 2-cell, 8-cell, and blastocyst stage, each microdrop of 30 presumptive zygotes were classified as slow- or fast-growing based on the percentage of embryos that had reached the desired morphological stage. A comparative analysis was performed on the spent media of slow- and fast-growing embryos using the results of a GeneChip miRNA 4.0 array hybridization. In total, 34 differentially expressed miRNAs were identified between the comparison groups: 14 miRNAs were found in the 2-cell samples, 7 in the 8-cell samples, and 12 in the blastocyst samples. The results demonstrate distinct miRNAs populations can be identified between slow- and fast-growing embryos, highlighting the novel biomarkers of developmental potential at each stage of pre-implantation development.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

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)0.000
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 score0.408
Threshold uncertainty score0.345

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.026
GPT teacher head0.284
Teacher spread0.258 · 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".

Quick stats

Citations1
Published2024
Admission routes2
Has abstractyes

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