A Systematic Review of the Frequency of Regulatory T Cells in Hepatitis B and Hepatitis C
Bibliographic record
Abstract
Background: Regulatory T cells (Tregs) play an important role in sustaining the hepatitis B and C viruses (HBV and HCV) persistence and protecting the liver tissues from cytokine-associated detrimental effects through unclear mechanisms. This paper aims to review the frequency of Tregs during the course of HBV and HCV infection.Method: Electronic databases were searched to identify studies investigated the frequency of intrahepatic and peripheral Tregs of the patients infected with HBV and/ or HCV.Results: The majority of studies reported the increase of intrahepatic and peripheral Tregs in acute and chronic infection of HBV and HCV. The decrease of peripheral Tregs occurred in patients with chronic hepatitis B who respond to interferon α or nucleos(t)ide analogues treatment as well as those with chronic hepatitis C who were treated with interferon, ribavirin or liver transplantation.Conclusion: Infection with HBV and HCV appears to induce the production of Tregs in blood and hepatocytes whereas treatment may decrease Tregs levels. As the optimum balance between regulatory and effector T during HBV and HCV infection is crucial for preventing liver damage, further studies should be directed on the development of Tregs during HBV and HCV infection as well as their involvement in immunomodulatory strategies for combating HBV and HCV.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".