Egr-1 mediates the suppressive effect of IL-1 on PPARg expression in human OA chondrocytes
Bibliographic record
Abstract
Rheumatology has pioneered in the study of autoantibodies by showing that they are not only involved in pathogenesis but are also highly useful as diagnostic biomarkers.The diagnostic biomarker aspect of autoimmunity has gained increasing importance in cancer and many of the insights gained in Rheumatology have contributed to understanding the significance of autoantibodies in cancer.Features of autoantibodies in rheumatic disorders: In rheumatic diseases no individual autoantibody-antigen system has sufficient combination of sensitivity and specificity to serve as a useful diagnostic biomarker.Instead, several antigen-antibody systems constructed as profiles of biomarkers are highly effective in distinguishing one disorder from another.In lupus, anti-double strand DNA and anti-Sm distinguishes it from scleroderma, where the profile is anti-DNA topoisomerase 1 and anti-centromere proteins.The autoantigensare cell components involved in universal and basic gene expression pathways, such as Sm in precursor mRNA splicing and DNA topoisomerase 1 in DNA replication and transcription [1].Features of autoantibodies in cancer: Autoantibodies in cancer target intracellular molecules referred to as TAAs (tumor-associated antigens).As in rheumatic disorders, no individual autoantibody-antigen system has sensitivity and specificity to serve as a stand-alone diagnostic marker [2].Most tumors show multiple antibody specificities and with panels of TAAanti-TAAs (analogous toprofiles) the cumulative sensitivity and specificity reaches diagnostic significance.Different tumorigenesis pathways are
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".