Natural Killer Cell Cytotoxicity in Rheumatoid Arthritis Patients Treated with TNF Inhibitors
Bibliographic record
Abstract
Objective: Patients with low natural killer (NK) cell activity were shown to have a higher risk of cancer development. Increased cancer risk in rheumatoid arthritis (RA) patients treated with anti-tumor necrosis factor (anti-TNF) agents is still being debated. Therefore, we think it is important to show whether anti-TNF therapy has any effects on the NK cell cytotoxicity in these patients. Material and Methods: We included 60 rheumatoid arthritis patients, 31 treated with anti-TNF therapy, 29 treated with other disease modifying antirheumatic drugs (DMARDs), and 51 ankylosing spondylitis (AS) patients, 35 treated with anti-TNF therapy and 16 treated with nonsteroidal anti-inflammatory drugs or conventional DMARDs as a disease control into the study. The results of two healthy control groups each consisting of 32 age- and sex- matched healthy individuals were also included. Results: Median values of NK cytoxicity in patients with RA who were being treated with anti-TNF and patients treated with other DMARDs were 29% and 43%, respectively (p=0.203). When we reanalyzed the results independent of clinical diagnosis, patients using anti-TNF medications and patients treated with other medicine had NK cytotoxicity median values of 32% and 43%, respectively (p=0.277). Conclusion: We suppose that NK cell cytotoxicity may decrease in patients with RA when they use anti-TNF agents, and this may help partly to explain why these patients are more prone to development of cancer and severe infections. We think that this study will encourage new studies with higher number of patients to better clarify effects of anti-TNF treatment on NK cell functions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".