The association between rs2476601 polymorphism in PTPN22 gene and risk of alopecia areata
Bibliographic record
Abstract
BACKGROUND: The single nucleotide polymorphism (SNP) rs2476601 of the protein tyrosine phosphatase, nonreceptor type 22 (PTPN22) gene has been presented to implicate in the pathogenesis of alopecia areata (AA) in a few association investigations with limited sample size and inconsistent conclusions. METHODS: The aim of the current meta-analysis was to assess and synthesize the presently available data on the connection between rs2476601 and AA vulnerability. Six electronic databases, including EMBASE, PubMed, Web of Science, the Cochrane Library, Wanfang data, and the China National Knowledge Infrastructure database (CNKI), were systematically retrieved for relevant observational studies published previous to November 2018. Total odds ratios (ORs) and corresponding 95% confidence intervals (95% CIs) were analyzed to evaluate the correlation between PTPN22 polymorphism and AA. Risk of bias was estimated according to the Newcastle-Ottawa Scale (NOS). Sensitivity analyses were carried out using the RevMan 5.3 software. RESULTS: In general, 5 case-control studies including 1129 AA patients and 1702 healthy control individuals were obtained for this meta-analysis. The pooled results suggested that rs2476601 SNP was significantly associated with AA susceptibility under allelic model (C vs T, OR = 0.77, 95% CI, 0.64-0.92, P = .003) and recessive model (CC vs CT + TT, OR = 0.73, 95% CI, 0.60-0.88, P = .001). CONCLUSION: On the basis of the results of the current research, the rs2476601 polymorphism of PTPN22 gene is significantly correlated with AA susceptibility. The C-allele and CC-genotype carriers at this locus have a lower risk of AA.
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 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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.014 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".