The risk of atopic dermatitis may be affected by IL-1B +3954 C/T and IL-18 -137G/C polymorphisms: evidence from a meta-analysis
Bibliographic record
Abstract
Introduction: Whether Th1-related cytokine polymorphisms influence the risk of atopic dermatitis (AD) remain inconclusive. Aim: The authors performed a meta-analysis to robustly explore relationships between Th1-related cytokine polymorphisms and the risk of AD by merging the results of eligible publications. Material and methods: The authors strictly adhere to the PRISMA guidelines in study design and implementation. A thorough literature search in Medline, Embase, Wanfang, VIP and CNKI was performed by the authors to identify eligible publications. Relationships between TNF-/IL-1/IL-6/IL-18 polymorphisms and the risk of AD were estimated with odds ratio and its 95% confidence interval. The statistically significant p value was set at 0.05. The quality of eligible publications was assessed by the Newcastle-Ottawa scale (NOS). Results: In total twenty-one publications with a NOS score of 7-8 were selected for merged quantitative analyses. We have noticed that genotypic frequencies of IL-1B +3954 C/T and IL-18 -137G/C polymorphisms among cases with AD and population-based controls differed significantly. Moreover, we have found that genotypic frequency of IL-1B +3954 C/T polymorphism among cases with AD and population-based controls of Caucasian origin differed significantly, and genotypic frequency of IL-18 -137G/C polymorphism among cases with AD and population-based controls of both Caucasian and Asian origins also differed significantly. However, we did not observe such genotypic distribution differences for TNF- -238 G/A, TNF- -308 G/A, IL-1A -889 C/T, IL-1B -511 C/T and IL6 -174 G/C polymorphisms. Conclusions: The present meta-analysis shows that IL-1B +3954 C/T and IL-18 -137G/C polymorphisms may affect the risk of AD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".