FMR1 alleles in women with idiopathic infertility
Bibliographic record
Abstract
Introduction: The frequency of the premutated alleles of the FMR1 gene varies from 1: 100 to 1: 260 Israeli, Canadian, Finnish and American women, but it is unknown in Brazil. Premutation carriers may have reduced reproductive age and are at risk of transmitting the expanded allele to their offspring, and consequently fragile X syndrome. Objective: To observe the distribution range of the FMR1 gene alleles in a population of women with idiopathic infertility, without symptoms of premature ovarian insufficiency. Methods: The presence of premutation in FMR1 was assessed by conventional PCR, agarose and acrylamide gel and analysis of fragments in capillary electrophoresis. From the lymphocyte DNA obtained from 283 women undergoing infertility treatment. Results: It was observed that 169 patients had the normal heterozygous allele (59.7%), 114 had the normal homozygous allele (40.6%) and no patient had the premutation. Premature ovarian insufficiency is seen in 20 to 30% of women with the premutated allele. Thus, the condition can be asymptomatic in a large part of the premutation carriers. Brazil has a diverse population and, therefore, the allele frequencies of many gene variants are unknown. Previous Brazilian studies have shown a low frequency of the premutated allele in different patient cohorts. Corroborating these articles, the results demonstrated that the frequency of the premutated allele is low in the infertile women population studied. Conclusion: Tracking the size of the FMR1 gene alleles allows the expansion of knowledge about the frequency of risk alleles associated to genetic diseases in the Brazilian population.
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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 | 0.000 |
| 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".