<i>CFTR</i> Mutation Analysis in Western Iran: Identification of Two Novel Mutations.
Bibliographic record
Abstract
BACKGROUND: Cystic fibrosis (CF) is one of the most common autosomal recessive disorders in Caucasian population. The incidence of disorder varies among different religious, ethnic and geographical isolates. The aim of this study was to identify the spectrum and the frequency of known and unknown disease-causing mutations in Iranian CF patients. METHODS: gene including complete coding region and intron/exon boundaries using a direct sequencing method. RESULTS: In general, ten mutations were identified in 27 CF cases. Two out of 10 mutations, 754delT and GGTGGCdel/TTGins, were reported as novel mutations. The most common observed mutations in patients were R334W (40.74%), ΔF508 (18.5%), K710X (12.96%) and D110H (5.5%), 1897C>G (1.85%), R1162X (1.85%), S466X (1.85%) and T1036I (1.85%). CONCLUSION: The finding indicated a unique mutation panel which can be used in genetic counseling, prenatal diagnosis and future screening of CF in Iran. Although ΔF508 is the most common mutation in other populations including Caucasian, this mutation seem not to have an important role in Iranian CF patients. Findings suggest that a different approach in molecular genetics diagnostic strategies in Middle Eastern countries including Iran should be considered.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".