Investigation of 1377C/T polymorphism of the Toll-like receptor 3 among patients with chronic hepatitis B
Bibliographic record
Abstract
The immunopathogenesis of chronic hepatitis B (CHB) has not been clarified yet. Toll-like receptors (TLR) are a receptor family that initiates immunity with exogenous-endogenous ligands and plays a role in the pathogenesis of infections. In this study, we aimed to investigate the frequency of TLR 3 1377C/T (rs3775290) polymorphism and its role in patients with CHB. We included 50 healthy individuals as control group and 73 active and 43 inactive hepatitis B patients. All DNA samples were isolated from blood samples. For the detection of TLR 3 1377C/T single-nucleotide polymorphism, restriction fragment length polymorphism was used. A statistically significant difference was determined in Hepatitis B virus (HBV) DNA levels of CHB patients with the CC, CT, and TT genotypes (p = 0.013). The highest levels of HBV DNA were detected in individuals with TT genotypes. Additionally, the frequency of CC genotype was higher in the active CHB patients compared with that of the inactive CHB patients (p = 0.044). No statistically significant difference in TLR 3 1377C/T polymorphism was detected between healthy controls and the hepatitis B patients (p = 0.342). In conclusion, HBV DNA level was higher in the individuals with TT genotype, and CC genotype was more frequent in the active CHB patients. These results suggest a possible association between CHB and TLR 3 gene (1377C/T) polymorphism.
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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) | 0.000 | 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".