<i><scp>HLA</scp>‐C</i> and <i><scp>KIR</scp></i> combined genotype as new response marker for <scp>HB</scp>eAg‐positive chronic hepatitis B patients treated with interferon‐based combination therapy
Bibliographic record
Abstract
Current treatment for chronic hepatitis B infection (CHB) consists of interferon-based therapy. However, for unknown reasons, a large proportion of patients with CHB do not respond to this treatment. Hence, there is a pressing need to establish response markers to select patients who will benefit from therapy and to spare potential nonresponders from unnecessary side effects of antiviral therapy. Here, we assessed whether HLA-C and KIR genotypes were associated with treatment outcome for CHB. Twelve SNPs in or near the HLA-C gene were genotyped in 86 CHB patients (41 HBeAg positive; 45 HBeAg negative) treated with peginterferon alfa-2a + adefovir. Genotyping of killer immunoglobin-like receptors (KIRs) was performed by SSP-PCR. One SNP in HLA-C (rs2308557) was significantly associated with combined response in HBeAg-positive CHB patients (P = 0.003). This SNP is linked to the HLA-C group C1 or C2 classification, which controls KIR binding. The combination of KIR2DL1 with its ligand HLA-C2 was observed significantly more often in HBeAg-positive patients with a combined response (13/14) than in nonresponders (11/27, P = 0.001). Patients with the KIR2DL1/C2 genotype had significantly higher baseline ALT levels (136 vs 50 U/L, P = 0.002) than patients without this combination. Furthermore, KIR2DL1-C2 predicted response independent of HBV genotype and ALT at baseline. HLA-C and KIR genotype is strongly associated with response in HBeAg-positive CHB patients treated with interferon-based therapy. In combination with other known response markers, HLA-C/KIR genotype could enable the selection of patients more likely to respond to interferon-based therapy.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".