Relationship between HLA-DRB1 Allele Polymorphisms and Familial Aggregations of Hepatocellular Carcinoma
Bibliographic record
Abstract
OBJECTIVE: We explored the relationship between HLA-DRB1 allele polymorphisms and familial aggregation of hepatocellular carcinoma (fhcc). METHODS: Polymerase chain reaction sequence-specific primers were used to determine HLA-DRB1 genotypes for 130 members of families with 2 or more liver cancer patients and for 130 members of families without any diagnosed cancers. The genotype profiles were then compared to explore the relationship between HLA-DRB1 gene polymorphism and fhcc. RESULT: Of 11 selected alleles, the frequencies of DRB1*11 and DRB1*12 were significantly lower in the fhcc group than in no-cancer group (p < 0.05; odds ratio: 0.286; 95% confidence interval: 0.091 to 0.901; and odds ratio: 0.493; 95% confidence interval: 0.292 to 0.893). Differences in the frequencies of the other 9 alleles were not statistically significant in the two groups (p > 0.05). CONCLUSIONS: Our research suggests that if genetic factors play a role in fhcc, the deficiency in the DRB1*11 and DRB1*12 alleles might be the risk factor at work in Guangxi Zhuang Autonomous Region, P.R.C.
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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 | 0.002 |
| 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) | 0.002 | 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".