Extended analysis on peripheral blood cytokines correlated with hepatitis B virus viral load in chronically infected patients – a systematic review and meta-analysis
Bibliographic record
Abstract
Background Hepatitis B Virus (HBV) can affect life quality. Monitoring and understanding the fluctuations of the HBV level of viremia related to the intricate immune activity of the host helps in the development of new treatment strategies and evaluation patterns. This meta-analysis presents the correlations between cytokines and the level of viremia in chronic HBV patients for a better comprehension of the immune mechanisms behind this infection. Methods We used PRISMA guidelines for this meta-analysis. The databases assessed were PUBMED, WEB OF SCIENCE, SCOPUS, and Cochrane Library. ZOTERO and PlotDigitizer helped the systematic research process. We extracted information related to the correlations between cytokines and the HBV-DNA level. Effect measures included comparisons between standardized mean differences and correlation coefficients. We evaluated retrieved articles with the Newcastle-Ottawa Quality Assessment Scale (NOS). The R 4.2.2 software displayed the statistical calculation and graphical representations. Results From 58,169 records, we extracted 16 articles with 32 different cytokine determinations. The main interleukins included in detection panels were IL-10 and IL-21. The meta-correlation analysis comprised 1,199 chronic HBV patients. The standardized mean difference between cytokine levels in HBV patients and healthy controls was 0.82 (95% CI = [−0.19, 1.84], p = 0.11). We observed a significant, fair, pooled correlation coefficient between IL-10, IL-9, and the viral load (r = 0.52, 95% CI = [0.19, 0.85]). Conclusion This meta-analysis brings novelty because it gives a first rigorous systematic look at multiple studies with many cytokines. Our research approaches a debatable issue and gives a possible solution for settling controversies. Future studies can arise towards understanding the immune disruption in HBV and the development of new, improved assays for prognosis.
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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 | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.039 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".