Deoxyribonucleic acid methylation profiling of single human blastocysts by methylated CpG-island amplification coupled with CpG-island microarray
Bibliographic record
Abstract
OBJECTIVE: To study whether methylated CpG-island (CGI) amplification coupled with microarray (MCAM) can be used to generate DNA (deoxyribonucleic acid) methylation profiles from single human blastocysts. DESIGN: A pilot microarray study with methylated CpG-island amplification applied to human blastocyst genomic DNA and hybridized on CpG-island microarrays. SETTING: University research laboratory. PATIENT(S): Five cryopreserved sibling 2-pronuclear zygotes that were surplus to requirements for clinical treatment by in vitro fertilization were donated with informed consent from a patient attending Bourn Hall Clinic, Cambridge, United Kingdom. INTERVENTION(S): None. MAIN OUTCOME MEASURE(S): Successful generation of genome-wide DNA methylation profiles at CpG islands from individual human blastocysts, with common genomic regions of DNA methylation identified between embryos. RESULT(S): Between 472 and 734 CpG islands were methylated in each blastocyst, with 121 CpG islands being commonly methylated in all 5 blastocysts. A further 159 CGIs were commonly methylated in 4 of the 5 tested blastocysts. Methylation was observed at a number of CGIs within imprinted-gene, differentially methylated regions (DMRs), including placental and preimplantation-specific DMRs. CONCLUSION(S): The MCAM method is capable of providing comprehensive DNA methylation data in individual human blastocysts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".