Serum N-glycan markers for diagnosing liver fibrosis induced by hepatitis B virus
Bibliographic record
Abstract
BACKGROUND: Hepatitis B virus (HBV) infection is the primary cause of hepatitis with chronic HBV infection, which may develop into liver fibrosis, cirrhosis and hepatocellular carcinoma. Detection of early-stage fibrosis related to HBV infection is of great clinical significance to block the progression of liver lesion. Direct liver biopsy is regarded as the gold standard to detect and assess fibrosis; however, this method is invasive and prone to clinical sampling error. In order to address these issues, we attempted to find more convenient and effective serum markers for detecting HBV-induced early-stage liver fibrosis. AIM: To investigate serum N-glycan profiling related to HBV-induced liver fibrosis and verify multiparameter diagnostic models related to serum N-glycan changes. METHODS: < 0.05) in different fibrosis stages were selected in the modeling group, and multiparameter diagnostic models were established based on changed N-glycan levels by logistic regression analysis. The receiver operating characteristic (ROC) curve analysis was performed to evaluate diagnostic efficacy of N-glycans models. These models were then compared with the aspartate aminotransferase to platelet ratio index (APRI) , fibrosis index based on the four factors (FIB-4), glutamyltranspeptidase platelet albumin index (S index), GlycoCirrho-test, and GlycoFibro-test. Furthermore, we combined multiparameter diagnostic models with alanine aminotransferase (ALT) and platelet (PLT) tests and compared their diagnostic power. In addition, the diagnostic accuracy of N-glycan models was also verified in the validation group of patients. RESULTS: Multiparameter diagnostic models constructed based on N-glycan peak 1, 3, 4 and 8 could distinguish between different stages of liver fibrosis. The area under ROC curves (AUROCs) of Model A and Model B were 0.890 and 0.752, respectively differentiating fibrosis F0-F1 from F2-F4, and F0-F2 from F3-F4, and surpassing other serum panels. However, AUROC (0.747) in Model C used for the diagnosis of F4 from F0-F3 was lower than AUROC (0.795) in FIB-4. In combination with ALT and PLT, the multiparameter models showed better diagnostic power (AUROC = 0.912, 0.829, 0.885, respectively) when compared with other models. In the validation group, the AUROCs of the three combined models (0.929, 0.858, and 0.867, respectively) were still satisfactory. We also applied the combined models to distinguish adjacent fibrosis stages of 432 patients (F0-F1/F2/F3/F4), and the AUROCs were 0.917, 0.720 and 0.785. CONCLUSION: Multiparameter models based on serum N-glycans are effective supplementary markers to distinguish between adjacent fibrosis stages of patients caused by HBV, especially in combination with ALT and PLT.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".