Identification of novel genes and mechanisms responsible for recurrent pregnancy loss
Bibliographic record
Abstract
Approximately 1020% of clinically recognized pregnancies result in spontaneous loss.The occurrence of at least two of such events before 24 weeks of gestation is termed recurrent pregnancy loss (RPL), which affects 15% of couples trying to conceive, and approximately half of these cases remain clinically unexplained.Exome sequencing on patients with RPL revealed a homozygous nonsense mutation in HORMAD2 in a patient with eight recurrent miscarriages and no live birth.HORMAD2 is an essential protein of the synaptonemal complex.Microscopic morphological evaluation of one of the patient's miscarriages confirmed its diagnosis.Microsatellite genotyping on available DNA from two other miscarriages demonstrated that they are triploid digynic and resulted from the failure of maternal Meiosis II (MII).Single nucleotide polymorphism (SNP) microarray analysis revealed an additional Meiosis I (MI) abnormality that is the segregation of the two maternal homologous chromosomes 16 and 19 in one conception.My data will improve current understanding of the genetic causes and mechanisms underlying RPL, which will guide clinical management of such conditions and improve women's health.I am truly grateful for all the help and kindness I have received throughout my studies.I would like to begin by expressing my gratitude to the most important person for my MSc project, my supervisor Dr. Rima Slim.Thank you for all your trust in me, especially when I am stressed about my project.Without your guidance and support I would not have the courage to peruse a PhD.Thank you for opening your door to me and providing help every time I knocked, and for giving me the opportunity to attend the workshop at The Jackson Laboratory.I would like to extend my thanks to Dr. Teruko Taketo for training me in oocyte experiments.I cannot thank you enough for being so patient and for tolerating my mistakes.What I learned from both of you are incredibly valuable for my future studies
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".