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Record W2591846204 · doi:10.1093/biolreprod/85.s1.743

Non-Invasive Assessment of Spent Bovine Culture Media Using Proton Nuclear Magnetic Resonance.

2011· article· en· W2591846204 on OpenAlexaff
Kayla Jane Perkel, Pavneesh Madan

Bibliographic record

VenueBiology of Reproduction · 2011
Typearticle
Languageen
FieldMedicine
TopicReproductive Biology and Fertility
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsBiologyBlastocystEmbryo cultureEmbryoOocyteMetabolomicsHuman fertilizationAndrologyIn vitro fertilisationZygoteEmbryonic stem cellIn vitro maturationEmbryogenesisMetabolomeMetaboliteEmbryo qualityCell biologyBiotechnologyGeneticsBioinformaticsBiochemistryGene

Abstract

fetched live from OpenAlex

Early embryonic mortality is a great determinant of reproductive efficiency in mammals, including cattle. In in vitro production, approximately 90% of oocytes undergo fertilization following in vitro maturation; however, of these fertilized oocytes approximately 30-40% will reach the blastocyst stage. Factors involved in early embryonic mortality include oocyte and sperm quality, genetic factors, environmental and oxidative stresses, and largely in in vitro production, sub-optimal culture conditions. Current methods of embryo viability evaluation depend on morphological assessment, which in spite of its importance, is a poor predictor of embryo viability. Development of refined biological techniques like metabolomics has enabled us to explore the health of a cell based on its secreted metabolite constituents. Advanced analytical techniques, such as proton nuclear magnetic resonance (H1 NMR) has little chemical bias, gives detailed structural information of isolated metabolites and allow metabolites to be measured simultaneously with minimal sample preparation. We hypothesize that, embryos developing at different rates differ in their metabolomic signatures. The specific objective of study was to determine the metabolomic signatures of slow growing and fast growing embryos at timed stages of development, thereby determining possible biomarkers of embryo competency. Oocytes were collected from ovaries obtained from a local abattoir and then matured and fertilized in vitro using standard protocols. Presumptive zygotes were either placed for individual culture in 25 μl media drops. Media samples from fast growing embryos were collected at 2-cell (31 hours post fertilization), 4-cell (42 hours post fertilization), 8-cell (49 hours post fertilization) or 16-cell (72 hours post fertilization) and from slow growing embryos, which required additional 8 to 12 hours to reach equivalent embryo stage. The 18 μl media collection was diluted in 600 μl of 100% deuterium oxide (D2O) which included the internal reference standard TSP (sodium 3-(trimethyl-2,2,3,3)-1-propionic acid-d4). H1 NMR analysis was performed using a 600MHz Bruker NMR spectrometer. Preliminary data indicates distinct differences between metabolomic fingerprints of plain media, 2-cell, 4-cell, 8-cell, and 16-cell slow growing and fast growing groups. Specifically, slow growing embryos had lower lactate uptake and increased pyruvate production, while fast growing embryos demonstrated higher levels of pyruvate uptake. Moreover, in the presence of hydrogen peroxide, a reactive oxygen species, pyruvate can be decarboxylated to produce acetate, a metabolite which is higher in slow growing embryos compared to fast growing embryos. The data suggests that slow growing embryos have a slower metabolism and lower utilization of media substrates, as well as an increased response to oxidative stress. The results provide evidence towards the use of metabolomics for the development of a non-invasive tool for assessing embryo viability and the discovery of potential embryo viability biomarkers. (poster)

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.050
GPT teacher head0.311
Teacher spread0.261 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations0
Published2011
Admission routes1
Has abstractyes

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