Bibliographic record
Abstract
Oxidative stress is known to be related to various diseases such as inflammation, carcinogenesis, arteriosclerosis and ischemia-reperfusion injury, and is also a major cause of aging. For the prevention of diseases and control of aging, evaluation and control of oxidative stress in vivo may become essential. We have developed the new Oxidative Stress Profile(OSP), a total diagnostic system which provides information about oxidative stress inside human body. The OSP system consists of a number of biomakers including oxidative damage markers, prooxidant factors, antioxidants and life style-related markers. The result is shown in a two dimensional plot form. We measured a combination of biomarkers for oxidative damage of biological components, serum antioxidants and analyzed oxidative stress. The result show that oxidative stress was elevated in diabetic patients in comparison with normal controls. Oxidative stress is also elevated in smokers in comparison with non-smokers. It is also interesting to find that oxidative stress can be greatly reduced by the improvement of life style such as diet. Oxidative Stress Profile system may become a powerful tool for the evaluation of oxidative stress in vivo, and may be useful in prevention of diseases, "mi-byo"(possible cause of diseases)-diagnosis and the control of aging.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.028 |
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".