Paraoxonase-1 activity and oxidative status in patients with knee osteoarthritis and their relationship with radiological and clinical parameters
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to investigate serum paraoxonase-1 (PON1) activity and oxidative/anti-oxidative status in knee osteoarthritis (OA), and evaluate their relationship using radiological and clinical parameters. MATERIALS AND METHODS: The study population comprised 127 patients with knee OA and 107 healthy volunteers. Patients with knee OA were divided into four subgroups according to the Kellgren-Lawrence (K&L) grading scale. In addition, each patient was clinically evaluated by the Western Ontario and McMaster University Osteoarthritis Index (WOMAC). Serum PON1 activity was measured spectrophotometrically. Oxidative status was assessed by measuring serum lipid hydroperoxide (LOOH) and total oxidant status (TOS). Anti-oxidative status was assessed by measuring serum free sulfydryl groups (-SH = total thiol) and total antioxidant capacity (TAC). Oxidative stress index (OSI) was calculated. Lipid parameters were determined by routine laboratory methods. RESULTS: Serum PON1 activity was significantly lower in the knee OA group compared to the control group (p < 0.001), whereas serum LOOH, TOS, and OSI levels of the knee OA group were significantly higher than those of the controls (p < 0.001 for all). However, TAC and -SH levels did not differ between the two groups (p > 0.05). The lowest and highest mean serum PON1 activities were detected in patients with grades 4 and 1, respectively (ANOVA p < 0.001). In multiple regression analysis, WOMAC score was independently associated with serum PON1 activity (β = -0.248, p = 0.027). CONCLUSIONS: Decreased serum PON1 activity and elevated LOOH, TOS, and OSI levels may be associated with knee OA, and serum PON1 activity may be a useful adjunctive indicator of the severity of knee OA for follow-up.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| 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".