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Record W4382982827 · doi:10.3390/ani13132171

Unraveling the Consequences of Oxygen Imbalance on Early Embryo Development: Exploring Mitigation Strategies

2023· review· en· W4382982827 on OpenAlexaff
Thamiris Vieira Marsico, Mara Viana Silva, Roniele Santana Valente, Kelly Annes, Vitor Braga Rissi, Werner Giehl Glanzner, Mateus José Sudano

Bibliographic record

VenueAnimals · 2023
Typereview
Languageen
FieldMedicine
TopicReproductive Biology and Fertility
Canadian institutionsMcGill University
FundersCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorFundação de Amparo à Pesquisa do Estado de São Paulo
KeywordsEmbryonic stem cellOxygen tensionBiologyEmbryoEmbryogenesisCryopreservationCell biologyIn vitroOxygenBiotechnologyGeneticsGeneChemistry

Abstract

fetched live from OpenAlex

Although well-established and adopted by commercial laboratories, the in vitro embryo production system still requires refinements to achieve its highest efficiency. Early embryonic development is a dynamic event, demanding suitable conditions to provide a high number of embryos with quality and competence. The first step to obtaining an optimized in vitro environment is to know the embryonic metabolism and energy request throughout the different stages of development. Oxygen plays a crucial role in several key biological processes necessary to sustain and complete embryonic development. Nonetheless, there is still controversy regarding the optimal in vitro atmospheric concentrations during culture. Herein, we discuss the impact of oxygen tension on the viability of in vitro-produced embryos during early development. The importance of oxygen tension is addressed as its roles regarding essential embryonic traits, including embryo production rates, embryonic cell viability, gene expression profile, epigenetic regulation, and post-cryopreservation survival. Finally, we highlight the damage caused by in vitro unbalanced oxygen tensions and strategies to mitigate the harmful effects.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.923
Threshold uncertainty score0.500

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.227
GPT teacher head0.378
Teacher spread0.151 · 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 designOther design
Domainnot available
GenreReview

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

Citations30
Published2023
Admission routes1
Has abstractyes

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