Interleukin-17 SNPs and serum levels increase ulcerative colitis risk: A metaanalysis
Bibliographic record
Abstract
AIM:To investigate the associations of interleukin-17(IL-17)genetic polymorphisms and serum levels with ulcerative colitis(UC)risk.METHODS:Relevant articles were identified through a search of the following electronic databases,excluding language restriction:(1)the Cochrane Library Database(Issue 12,2013);(2)Web of Science(1945-2013);(3)PubMed(1966-2013);(4)CINAHL(1982-2013);(5)EMBASE(1980-2013);and(6)the Chinese Biomedical Database(1982-2013).Meta-analysis was conducted using STATA 12.0 software.Crude odds ratios and standardized mean differences(SMDs)with corresponding95%confidence intervals(CIs)were calculated.All of the included studies met all of the following five criteria:(1)the study design must be a clinical cohort or a case-control study;(2)the study must relate to the relationship between IL-17A/F genetic polymorphismsor serum IL-17 levels and the risk of UC;(3)all patients must meet the diagnostic criteria for UC;(4)the study must provide sufficient information about single nucleotide polymorphism frequencies or serum IL-17 levels;and(5)the genotype distribution of healthy controls must conform to the Hardy-Weinberg equilibrium(HWE).The Newcastle-Ottawa Scale(NOS)criteria were used to assess the methodological quality of the studies.The NOS criteria included three aspects:(1)subject selection:0-4;(2)comparability of subjects:0-2;and(3)clinical outcome:0-3.NOS scores ranged from 0 to 9,with a score≥7 indicating good quality.RESULTS:Of the initial 177 articles,only 16 case-control studies met all of the inclusion criteria.A total of1614 UC patients and 2863 healthy controls were included in this study.Fourteen studies were performed on Asian populations,and two studies on Caucasian populations.Results of the meta-analysis revealed that IL-17A and IL-17F genetic polymorphisms potentially increased UC risk under both allele and dominant models(P<0.001 for all).The results also showed that UC patients had higher serum IL-17 levels than healthy controls(SMD=5.95,95%CI:4.25-7.65,P<0.001).Furthermore,
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.043 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".