[The mutation spectrum of phenylalanine hydroxylase gene in patients with phenylketonuria in Henan province].
Bibliographic record
Abstract
OBJECTIVE: To investigate the characteristics of the phenylalanine hydroxylase (PAH) gene mutations in patients with phenylketonuria (PKU) in Henan province, China, in order for providing basic information for clinical genetic counseling and prenatal diagnosis. METHODS: All the exons and partial flanking introns of the PAH gene were detected by polymerase chain reaction (PCR) and bi-directional sequencing in 34 patients with PKU from Henan province. RESULTS: A total of 23 different disease-causing mutations were identified which corresponded to 92.65% (63/68) of the PAH alleles, including 12 missense mutations, 4 nonsense mutations, 4 splicing junction mutations, and 3 deletion mutations. Among them, A156P and P69_S70delinsP(delCTT) were novel mutations; IVS2+ 5G to C, G332E, IVS10-14C to G and L367 to Wfs were reported in Chinese population for the first time according to the PAH database (www.pahdb.mcgill.ca). Among all the 13 exons, exon 7 harbored the most type of mutations, exon 11 and exon 5 the second. The most common mutations included R243Q (17.65%, 12/68), V399V (11.76%, 8/68), IVS4-1G to A (8.82%, 6/68), R400T(7.35%, 5/68), Y166X(5.88%,4/68) and G247R(5.88%, 4/68). In addition, 9 other gene variations were found in this study. CONCLUSION: The mutation spectrum and frequency of the PAH gene of patients with phenylketonuria in Henan province were slightly different from those from other parts of China.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".