Detection and Functional Evaluation of −262A/T and −188A/G Polymorphisms of SLAM Gene in Patients with Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: Signaling lymphocytic activation molecule (SLAM) has been related to the pathology of systemic lupus erythematosus (SLE) through regulation of T cell-dependent humoral immune responses. We investigated the functional associations of the -262A/T and -188A/G polymorphisms of SLAM in Chinese patients with SLE. METHODS: Genotyping of -262A/T (rs2295614) and -188A/G (rs2295613) in SLAM was carried out in 248 cases and 278 controls. Promoter activities of haplotypes on the SLAM gene were evaluated with the dual-luciferase reporter system. The mRNA expressions of SLAM on peripheral blood mononuclear cells (PBMC) of SLE patients with different genotypes were determined by real-time polymerase chain reaction. RESULTS: Frequencies of -262A allele and -188G allele were significantly higher in SLE patients than in controls. Haplotype analysis and multifactorial logistic regression analysis showed that individuals with the AG/AG haplotype had increased susceptibility to SLE (p = 0.002, OR 1.478, 95% CI 1.152-1.897). In response to PHA stimulation, the SLAM mRNA expression on PBMC of SLE patients was significantly higher in -262A-188G haplotype homozygotes compared with -262A-188G heterozygotes and individuals with other genotypes. CONCLUSION: Our findings suggest that -262A-188G haplotype in the SLAM gene promoter contributes to the risk of SLE by increasing the expression of SLAM.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".