Analysis of CD16+/- Monocytes and CD4+ T Cells to Identify Novel Gene Signatures and Develop a Diagnostic Tool for SLE
Bibliographic record
Abstract
Systemic lupus erythematosus (SLE) is an incurable chronic autoimmune disease that causes widespread inflammation and organ damage.Due to the lack of a single test to diagnose SLE, doctors use multiple general methods to diagnose the disease.This study evaluated gene expression in CD16 + monocytes, CD16 -monocytes, and CD4 + T lymphocyte cells to identify signatures unique to SLE, to improve diagnostic processes.Gene expression profiles from individuals diagnosed with SLE and females aged 24-29 controls were obtained from the Gene Expression Omnibus, and 54,675 gene probes were compared between healthy and SLE patients.The top five gene probes with increased differential expression between healthy and SLE patients were associated with the ATP6V0C, UBA1, TGFB1, STAT1, and NFYC genes.Quantile-quantile plots confirmed statistical appropriateness for genetic analysis.Further evaluation determined that the ATP6V0C, UBA1, STAT1, NFYC, and TGFB1 genes associated with the CD16 -monocyte cell type represent a novel gene expression signature for SLE identification.Gene expression ranges were established for these probes, serving as a diagnostic tool for SLE.This tool can detect SLE in a single blood sample, which may improve diagnostic outcomes and reduce healthcare costs.
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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 | 0.001 | 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".