TYPE I INTERFERON GENES SIGNATURE AND GALECTINS-1,3,9: ARE THERE A RELATIONSHIPS?
Bibliographic record
Abstract
PV032 / #459 Poster Topic: AS04 - Biomarkers Background/Purpose To find out whether galectins-1,3,9 levels are related to the type I interferon genes signature (IFNGS) in patients with systemic lupus erythematosus (SLE). Methods A total of 43 patients (38 women and 5 men) with SLE (31[23-41] years old) were enrolled in the study. The median SLE duration was 24 [1-96] months, SLEDAI-2K was 8 [4-14]. SLE pts were treated with glucocorticoids (GC) (72,12%), hydroxychloroquine (69,8%), immunosuppressive drugs (35,9%) and biological agents (4,7%). Type I interferon status was assessed as «low» or «high» by the average expression of interferon-inducible 3 selected genes (MX1, RSAD2, EPSTI1) using real-time polymerase chain reaction. IFNGS was considered «high» if the average genes expression value in patients exceeded that in donors (20 healthy donors comparable in sex and age with the SLE patients). Serum galectins-1,3,9 levels were determined by enzyme—linked immunosorbent assay (Cloud-Clone Corp., China). Results «High» IFNGS was detected in 33 of 43 patients with SLE. Patients with «high» and «low» INFGS did not differ in age, gender, disease duration, SLE activity according to SLEDAI-2K (p>0,05 for all). Galectins-1 and 3 levels were comparable in patients with «high» and «low» IFNGS, but the galectin-9 levels were higher in patients with elevated gene expression (Table 1). Table 1. Galectins levels depending on IFNGS status in patients with SLE Conclusions The serum concentrations of galectin-9, but not galectins-1 and 3, depend on interferon-inducible genes expression in SLE patients. Increased galectin-9 may be a surrogate serological biomarker of elevated type I interferon status.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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".