Relationship Between Disease Activity, Levels of IFN-a, IL-4, IL-6, and Anti-NMDA to Cognitive Dysfunction (MoCA-INA Score) in Systemic Lupus Erythematosus (SLE) Patients with Cognitive Dysfunction.
Bibliographic record
Abstract
BACKGROUND: Neuropsychiatric Systemic Lupus Erythematosus (NPSLE) is a condition that impacts the patients' brain with SLE, and cognitive dysfunction (CD) is the most common manifestation. Subsequently, the CD hurts the life quality of SLE patients and creates impaired social function. Furthermore, the Montreal Cognitive Assessment (MoCA-INA) is a screening instrument to evaluate cognitive function. In the context of lupus, cytokines, and autoantibodies act as biomarkers in SLE disease control activities. PURPOSE: The aim of this study was to analyze the correlation between disease activity, IFN-a, IL-4, IL-6 and Anti-NMDA on CD (MoCA-INA Score) in SLE patients with CD. METHODS: This analytical observational study was performed with a cross-sectional design and included a sample of 56 SLE patients. The independent variables were the degree of the disease activity, and levels of IFN-a, IL-4, IL-6, and anti-NMDA. The dependent variable consisted of the degree of CD (MoCA-INA score), while the confounding variables were age, DM, gender, hypertension, obesity, and dyslipidemia. Subsequently, the CD was described as a MoCA-INA score <26, and disease activity was estimated based on the SLEDAI score. RESULTS: Increased IL-6 levels were correlated with decreased MoCA-INA scores (p=0.003; r= -0.387). Younger age was found to be associated with more severe CD (p=0.006) Conclusion:In conclusion, IL-6 levels can be used as a predictor severity of CD in SLE patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.001 | 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".