Association Between Polymorphisms in Cytokine Gene and Viral Infections in Renal and Liver Transplant Recipients: A Systematic Review
Bibliographic record
Abstract
PURPOSE: Although transplantations are associated with an increased risk of post-transplantation infections, they greatly improve life expectancy and patients' quality of life. Cytokine genes play an important role in the success of transplants due to their immunological functions. A systematic review was conducted to evaluate cytokine gene polymorphisms and risk of cytomegalovirus (CMV), hepatitis B virus (HBV) and hepatitis C virus (HCV) infections in kidney and liver transplant recipients. METHODS: A systematic search was conducted using PubMed, EMBASE, Medline and Google Scholar from their inception until January 28, 2019 using appropriate key words. Review articles, case reports or series, studies conducted on non-human subjects and published in languages other than English were excluded. Data were abstracted using a standardized form. The quality of the studies included was assessed using "Risk of Bias Assessment tool for Non-randomized Studies (RoBANS)". RESULTS: Thirty-one studies met our inclusion criteria; populations studied were diverse with a sample ranging from 20 to 1,671. Nineteen studies evaluated Interleukin (IL)-28B polymorphism, while six studies evaluated interferon lambda (IFN-λ) gene polymorphisms and their impact on CMV, HCV, and HBV progression. Polymorphisms in IL-10 gene were investigated in six studies. Polymorphisms in IL-12B and IL-1B gene were associated with a higher risk of developing CMV infections while polymorphisms in IL-28B were associated with a lower incidence of CMV infection in renal transplant recipients. Similarly, polymorphisms in IL-28B were associated with higher liver dysfunction from HBV infection in liver transplant recipients. Studies included had low risk of bias. CONCLUSIONS: Cytokine gene polymorphisms IL-12B and IL-1B were found to be associated with an increased risk of infection in kidney transplants and IL-28B in liver transplant recipients. However, the small number and heterogeneity of studies limits the generalization of our results. Further research may lead to finding these associations in larger studies which perhaps improve the use of genetic testing and targeted antiviral therapy. This will further reduce the risk of viral infections associated with cytokine gene polymorphisms in post renal and liver transplant recipients.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".