Profiling Fanconi Anemia Gene Mutations among Iranian Patients.
Bibliographic record
Abstract
BACKGROUND: Fanconi anemia (FA) is a rare genetic syndrome characterized by developmental defects, bone marrow failure, and a high cancer risk. FA is usually inherited as an autosomal recessive condition. This disease is genetically heterogeneous and mutations in 16 different genes have been identified in FA patients to date. An accurate diagnosis needs detection of pathogenic variations in the FA genes along with positive results from chromosome breakage test. METHODS: In this study, 48 families with at least 2 affected FA patients and positive chromosome breakage test were enrolled from the Iranian population. Molecular analysis of FA genes was performed using Next Generation Sequencing (NGS) method and Multiple Ligation Dependent Probe Amplification (MLPA). RESULTS: Causal mutations for 30 (63%) patients were identified in homozygous or compound heterozygous forms. FANCA had the highest mutation frequency rate (83%) followed by FANCG (10%), FANCD2 (3%) and FANCL (3%). A significant proportion (44%) of FANCA mutations were large rearrangements. CONCLUSION: Genetic testing for FA patients improves the accuracy of diagnosis and also will be essential for genetic counselling and prenatal diagnosis for future pregnancies in the family. Availability of NGS technology has made the screening of all known FA genes at once more practical and affordable.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".