Cytokine Levels in Patients with β-Thalassemia Major and Healthy Individuals: a Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Cytokine levels in patients with β-thalassemia major (β-TM) have been assessed in several studies. Accordingly, a wide variety of immune disturbances has been shown in patients with thalassemia. Recurrent transfusions cause iron overload, which induces an increase in the production of cytokines. However, no systematic approach or meta-analysis has been done to provide a clear feature of cytokine levels in β-TM. The present meta-analysis aimed to summarize the existing evidence regarding different levels of cytokines in patients with Β-TM compared to healthy controls. METHODS: This study was performed according to the PRISMA checklist. A systematic search was done in Web of Science (ISI), Scopus, and PubMed databases. The quality of the included studies was assessed based on the New-castle-Ottawa Scale. Meta-analysis was run via STATA 13 software. The standardized mean difference was considered the effect size for comparing the continuous variables. RESULTS: This meta-analysis included 16 studies conducted on 805 β-TM patients and 624 healthy individuals (with the mean age of 16.10 ± 4.33 and 16.22 ± 3.78, respectively). The results showed significantly higher levels of Tumor Necrosis Factor-alpha (TNF-α), Interleukin-6 (IL-6), and IL-10 in patients with β-TM compared to the healthy controls. CONCLUSION: The results indicated that the levels of both inflammatory and anti-inflammatory cytokines were higher in patients with β-TM compared to the healthy population, which could be associated with higher levels of oxidative markers in these patients. Further studies are suggested to evaluate the difference in cytokine levels among different types of thalassemia.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.033 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 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".