Transforming Growth Factor-β Level: Indicator for Severity of Disease and Organ Damage in Patients with Systemic Lupus Erythematosus: Figure 1.
Bibliographic record
Abstract
Systemic lupus erythematosus (SLE) is a T cell-dependent disorder of generalized autoimmunity characterized by B cell hyperactivity with numerous autoantibodies. Studies in both experimental animal models of lupus and patients with SLE have revealed a number of cytokine pathways that are important in the disease process. An imbalance between pro- and antiinflammatory cytokines might be responsible for the pathogenesis and development of SLE. For example, serum levels of interferon-α (IFN-α), tumor necrosis factor-α (TNF-α), IFN-γ, interleukin 1 (IL-1), IL-6, IL-18, and B cell activating factor are increased in patients with SLE in comparison with healthy individuals and have been shown to correlate with disease activity. Anticytokine and anticytokine receptor therapy have shown a significant decrease in disease activity in SLE and other autoimmune diseases, further suggesting that enhanced proinflammatory cytokines are associated with disease development. Conversely, a decreased ability of T cells to produce immunosuppressive cytokines such as transforming growth factor-β (TGF-β) has been reported1. In contrast, IL-10 is another immunosuppressive cytokine whose level is actually elevated in active SLE. Although IL-10 can suppress T helper cell and dendritic cell responses, it has a strong stimulatory effect on B cells. This feature of IL-10 makes it deleterious in SLE development. Despite the previous study documenting a decreased ability of SLE T cells to produce TGF-β1, questions concerning the value of serum TGF-β in SLE pathogenesis and development remain unresolved. Many cells such as lymphocytes, monocytes, and natural killer (NK) cells can produce TGF-β and its biological half-life in serum is very short due to quick degradation to its inactive form. In fact, early studies revealed no significant differences of levels of bioactive TGF-β in serum between healthy control subjects and patients with inactive and active SLE2. However, in their study in this issue … Address correspondence to Dr. Zheng. E-mail: szheng{at}usc.edu
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".