46 Use of time-lapse imaging technology to assess relationships of morphological and phototextural attributes of presumptive ovine zygotes and early embryos with their developmental competence in vitro
Bibliographic record
Abstract
The assessment of morphology and digital image opacity may provide valuable information on embryo viability because such traits are linked to embryonic gene expression, metabolism and ultrastructure. Time-lapse imaging has been used in research to monitor the dynamic nature of the developing pre-implantation embryo, which includes capturing alterations in various morphological parameters over time. The present study examined the effectiveness of time-lapse technology in assessing several morphometric and phototextural parameters for predicting the developmental potential of ovine embryos. The development of 37 long wool sheep embryos from IVF to the blastocyst stage was monitored and evaluated using Primo Vision time-lapse imaging technology. Image-Pro Plus software was then used to measure zona pellucida thickness, embryo diameter, cellular grey-scale pixel intensity and heterogeneity, and total area of the perivitelline space. A one-way analysis of variance (ANOVA) was done using SigmaPlot® 11.0 for all attributes at various time points during embryo development [i.e. presumptive zygote stage, t(0); first cleavage, t(2) or t(3); second cleavage, t(4) or t(6); and third cleavage, t(7) or t(8)]. Our results indicate that most parameters analysed did not differ among embryos varying in their developmental fate, with the exception of the perivitelline space area, which was greater (P < 0.05) for non-dividing embryos than for future blastocysts at the presumptive zygote stage (4040 ± 4137 vs. 857 ± 642 µm2, respectively; mean ± s.d.). Consequently, the measurement of perivitelline space at t(0) could be used to predict developmental potential of in vitro-produced ovine embryos, but further investigation is required.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".