Prospect of preimplantation genetic diagnosis for heritable mitochondrial DNA diseases
Bibliographic record
Abstract
To perform preimplantation genetic diagnosis for women carrying heteroplasmic mitochondrial DNA (mtDNA) mutations, it is necessary to ensure that the proportion of mutant mtDNA diagnosed in the biopsied cell gives an accurate indication of the mutant load in the remaining embryo. A heteroplasmic mouse model, carrying NZB and BALB mtDNA genotypes, was used to study the relative proportions of each mtDNA genotype in the ooplasm and first polar body of mature oocytes, and between blastomeres of early cleavage stage embryos. The levels of heteroplasmy varied widely in the gametes compared with the maternal genotype. However, the distribution of the two mtDNA genotypes was virtually identical between the ooplasm and polar body of a mature oocyte, and also between the blastomeres of each 2-, 4- and 6-8-cell embryo. Therefore, the level of heteroplasmy diagnosed from the polar body of an unfertilized oocyte or from a single blastomere of an embryo is representative of the level in the embryo as a whole. Reliable results were obtained from both polar bodies and blastomeres, but the efficiency of diagnosis was greater with blastomeres. We conclude that preimplantation genetic diagnosis is feasible for mtDNA diseases, although it should be approached with caution, as it is possible that transmission of some pathogenic mutations could behave in a different manner.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".