Oxidative lipid, protein, and DNA damage as oxidative stress markers in vascular complications of diabetes mellitus
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to determine the effects of diabetic complications on oxidation of proteins, lipids, and DNA and to investigate the relationship between oxidative damage markers and clinical parameters. METHODS: The study group consisted of 69 type 2 diabetic patients (20 patients without complication, 49 patients with complication) who attended internal medicine outpatient clinics of Istanbul Education and Research Hospital and 19 healthy control subjects. In serum samples of both diabetic patients and healthy subjects, 8-hydroxy-2'deoxyguanosine (8-OHdG), as a marker of oxidative DNA damage, N(ε)-(hexanoyl)lysine (HEL) and 15-F2t-iso-prostaglandin (15-F2t-IsoP). as products of lipooxidative damage, advanced oxidation protein products (AOPP), as markers of protein damage, and paraoxonase1 (PON1) as antioxidant were studied. RESULTS: 15-F2t-IsoP (p < 0.005) and AOPP (p < 0.001) levels were significantly higher in diabetic group than control group while there were no significant differences in levels of 8-OHdG and HEL between the two groups. AOPP (p < 0.001) and 8-OHdG (p < 0.001) were significantly higher in diabetic group with complications compared to diabetic group without complications. CONCLUSIONS: Increased formation of free radicals and oxidative stress, under conditions of hyperglycaemia, is one of the probable causes for evolution of complications in diabetes mellitus. Our study supports the hypothesis that oxidant/antioxidant balance is disturbed in diabetic patients.
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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 | 0.001 | 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".