Cell Membrane-bound TLR2 and TLR4: Potential Predictors of Active Systemic Lupus Erythematosus and Lupus Nephritis
Bibliographic record
Abstract
To the Editor: Innate immune receptors have been found to be involved in the pathogenesis of systemic lupus erythematosus (SLE)1. The binding of nucleic acids to the endosomal Toll-like receptors (TLR) 7 and TLR9 is considered as a triggering mechanism for the production of antinuclear antibodies2,3. Also, the cell membrane-bound TLR (mbTLR) might contribute to enhance immune cell responses in SLE. Besides detecting microorganisms, these receptors engage molecules exposed upon apoptosis, such as the DNA-binding high mobility group protein B1, which is thought to facilitate self-DNA antigenicity4. The contribution of the mbTLR TLR2 and TLR4 to loss of tolerance and development of nephritis has been consistently found in SLE models conducted in transgenic mice5,6,7. However, there is little information about the activation of mbTLR during SLE flares in humans. We have studied TLR2 and TLR4 protein levels in peripheral blood mononuclear cells from patients with SLE (n = 35) and healthy controls (n = 11) using flow cytometry. Patients were receiving stable medication at the time of the study, and had no signs of active infection. Whereas no global differences in the levels of the mbTLR were noted between the cohorts, the density of TLR4 was significantly increased in the B cells of patients with active (n = 20) … Address correspondence to Dr. O. Sánchez-Pernaute, Division of Rheumatology, Jiménez Díaz Foundation Health Research Institute and University Hospital, Avda. Reyes Católicos 2, 28040 Madrid, Spain. E-mail: osanchez{at}fjd.es
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".