Interferon gamma receptor 2 gene variants are associated with liver fibrosis in the general population: the Rotterdam Study
Bibliographic record
Abstract
Dear Editor, Increasing evidence suggests that genetic factors play a role in the multifactorial aetiology of liver fibrosis. In their recent paper, Zhao et al 1 support this by describing the role of the Jnk1 gene in liver fibrogenesis. Previously, the role of other inflammatory-related genes in the development of liver fibrosis has been described in patients with chronic liver diseases. In patients with chronic hepatitis C, Nalpas et al 2 demonstrated a strong association between single nucleotide polymorphisms (SNP) in the interferon gamma receptor 2 ( IFNGR2 ) gene and progression of liver fibrosis. The role of interferon gamma in liver fibrogenesis has been reported before, but the precise mechanism of this effect has not yet been fully elucidated.3 ,4 Currently, it is not known whether the genetic variants in the IFNGR2 gene also influence liver fibrogenesis in individuals without liver disease. Therefore, we aimed to study the association between the IFNGR2 SNPs and liver fibrosis in the general population. For this purpose, we assessed the extent of liver fibrosis non-invasively by measuring liver stiffness (LS) using transient elastography (TE) (Fibroscan, Echosens) in 1059 participants of the Rotterdam study. Genotyping in these, almost exclusively Caucasian (99.6%) participants was performed with the Infinium II HumanHap 550K Genotyping Bead-Chip V.3 (Illumina, San Diego, California, USA). The Rotterdam Study is a large, ongoing, prospective population-based cohort study in subjects aged ≥55 years in …
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".