Evaluation of Uric Acid, Total Antioxidant and Lipid Peroxidation Parameters in Serum and Saliva of Patients with Oral Lichen Planus
Bibliographic record
Abstract
BACKGROUND: Oral lichen planus (OLP) is a chronic and inflammation mucosal disease. Reactive Oxygen Species (ROS) and oxidative stress damage might be the cause. Malondialdehyd (MDA), one parameter of lipid peroxidation is appropriate for DNA damage in OLP disease. OBJECTIVE: To evaluate antioxidants and oxidative stress parameters in patients with oral lichen planus (OLP) diseases and healthy control group with compare on serum and salivary samples. MATERIAL & METHODS: The research population included 22 patients with OLP which recently diagnosed and 22 healthy controls matched for periodontal status. Total antioxidant activity (TAA), Malondialdehyde (MDA) or lipid peroxidation product and uric acid (UA) were evaluated in both serum and saliva. The t-tests were used for differences between the two groups in normal distributed variables and also Spearman’s rho correlation coefficient for assessing association between serum and salivary fluids. RESULTS: TAA levels in OLP patients showed significant result and lower than healthy control group (p=0.39). Also, results in the saliva MDA concentration, was significantly higher in OLP patients than controls. In correlation test, inverse and significant correlation was observed between the MDA and UA values(r=0. 682, P=0.0001) and a significant correlation was found between serum TAA and UA values. CONCLUSION: This study showed that OLP groups have higher cellular lipid peroxidation in compare to healthy controls and low level of TAA than controls. Patients with OLP are believed to be more at risk of antioxidant-oxidative stress imbalance.
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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".