Analysis of low-density lipoprotein receptor gene mutations in a Chinese patient with clinically homozygous familial hypercholesterolemia.
Bibliographic record
Abstract
OBJECTIVE: To screen the point mutation of the low-density lipoprotein receptor (LDL-R) gene in Chinese familial hypercholesterolemia (FH) patients, characterize the relationship between the genotype and the phenotype and discuss the molecular pathological mechanism of FH. METHODS: A patient with clinical phenotype of homozygous FH and her parents were investigated for mutations in the promoter and all eighteen exons of the LDL-R gene. Screening was carried out using Touch-down PCR and direct DNA sequencing; multiple alignment analysis by DNASIS 2.5 was used to find base alteration, and the LDL-R gene mutation database was searched to identify the alteration. In addition, the apolipoprotein B gene (apo B) was screened for known mutations (R3500Q) that cause familial defective apo B100 (FDB) by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP). RESULTS: Two new heterozygous mutations in exons 4 and 9 of the LDL-R gene were identified in the proband (C122Y and T383I) as well as her parents. Both of the mutations have not been published in the LDL-R gene mutation database. No mutation of apo B100 (R3500Q) was observed. CONCLUSION: Two new mutations (C112Y and T383I) were found in the LDL-R gene, which may result in FH and may be particularly pathogenetic genotypes in Chinese people.
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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) | 0.001 | 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 | 0.001 | 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".