Quantitative characteristics of serum lipoprotein phenotypes for HBV patients
Bibliographic record
Abstract
Objective·Previous studies showed that hepatitis B virus (HBV) infection caused outstanding changes in host lipoproteins. However, there are no reports on such component changes of lipoprotein subfractions. This study aimed to quantify the HBV-caused changes in the serum lipoprotein subfractions and their components.Methods·Hepatitis B surface antigen-positive [HBsAg (+)] patients at Division of Cardiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology from March to June 2017 were included (n=40), and 40 HBsAg-negative [HBsAg (-)] population were matched as controls. Serum lipoprotein subfractions and their components were quantified by using 1H-NMR. Orthogonal partial least-squares discriminant analysis (OPLS-DA), variance analysis between the two groups, logistic regression analysis and Spearman correlation analysis were conducted to reveal the lipoprotein changes in chronic HBV patients against controls.Results·HBsAg (+) population had significantly lower levels in most lipoprotein subfractions than HBsAg (-) population. After adjustments for age, gender, hypertension, diabetes mellitus and coronary heart disease, the levels of total VLDL, VLDL1-VLDL3, IDL, HDL4, non-HDL and their components were protective factors for HBV infection (OR < 1, P < 0.01). In contrast, VLDL5-(TAG/LP) was a risk factor for HBV infection (OR > 1, P < 0.01). In addition, the severity of inflammation in the HBsAg (+) population was negatively correlated with the levels of lipids in HDL4 with correlation coefficient ranging from -0.71 to -0.51 (P≤ 0.002). Six lipoprotein subfractions were obtained through feature screening, and the AUC of HBV infection diagnosis model was 0.861.Conclusion·HBV infection causes significant changes in liver-excretion of lipoproteins and their circulation metabolism; the lipoprotein phenotypes can differentiate HBV-infected patients from controls.
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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) | 0.001 | 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".