Neurophysiological phenotypes are uncoupled from toll-like receptor (TLR)-mediated systemic disease in a model of Systemic Lupus Erythematosus (SLE)
Bibliographic record
Abstract
Abstract SLE is a multisystemic autoimmune disease with diverse clinical presentations, including neuropsychiatric symptoms (NPSLE). Prior work implicates uncontrolled MyD88-dependent TLR activation, increased interferon regulatory factor 5 (IRF5) activity and elevated type I interferon (IFN) levels in disease. Immune complexes containing self-nucleic acids can activate endosomal TLRs 7/8/9, which require signaling adaptor MyD88 to modulate downstream gene expression through transcription factor 1) IRF7 to upregulate IFN, which can subsequently act on the IFN-α/β receptor (IFNAR) or 2) IRF5 to upregulate proinflammatory genes. SLE- and NPSLE-prone mice with conditional deletion of signaling mediators MyD88, IRF5 and IFNAR in dendritic cells and tissue resident-macrophages were generated. MyD88 and IRF5 deletion ameliorated systemic disease phenotypes including splenomegaly, lymphadenopathy and glomerulonephritis, but NPSLE-associated phenotypes in the brain, including expansion of total and disease-associated microglia, macrophages and extravascular T cells, persisted. In contrast, IFNAR deletion worsened systemic disease, yet reversed several NPSLE-associated phenotypes. These discoveries suggest an uncoupling of systemic and neurophysiological phenotypes in SLE. Namely, neurophysiological phenotypes manifest independently from the TLR-dependent pathway that drives systemic disease and are instead in part linked to aberrant signaling through IFNAR via mechanisms yet unexplored.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".