WeChat Mini Program
Old Version Features

Interleukin-18 Polymorphisms Deficiency Association with Asthma Risk: an Update Meta-analysis

Iranian Journal of Allergy, Asthma and Immunology(2019)

North Sichuan Med Coll

Cited 1|Views10
Abstract
Growing evidence indicated conflicting results that Interleukin-18 (IL-18) promoter polymorphisms rs1946518 (A-607C), rs187238 (G-137C) and rs549908 (A-105C) were associated with asthma risk. The aim of this study is to comprehensively evaluate the IL-18 polymorphisms and asthma by a systematic review and meta-analysis. A total of 12 studies testing the association between these polymorphisms and asthma were examined (8 studies for A-607C, 8 studies for G-137C, and 4 studies for A-105C) in the update meta-analysis, up to Dec 30, 2017. Summary odds ratios (ORs) and 95% confidence intervals (CI) were used to estimate the strength of association between each polymorphism and asthma using fixed- and random-effects models when appropriate. Heterogeneity and publication bias were evaluated. The meta-analysis results indicated that any allele frequencies of the IL-18 polymorphisms (A-607C, G-137C and A-105C) was not associated with asthma risk (p>0.05). And no statistically significant association was observed between genotype frequencies of these polymorphisms and asthma under different genetic models (p>0.05). Subgroup analysis results were similar to the main analysis by ethnicity, sample size, genotyping methods, matching criteria and quality score. There was no evidence of publication bias. The present meta-analysis suggests that IL-18 polymorphisms (A-607C, G-137C and A-105C) were unlikely to be associated with asthma risk.
More
Translated text
Key words
Asthma,Genetic susceptibility,Interleukin-18,Meta-analysis,Single nucleotide polymorphism
求助PDF
上传PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined