Antioxidant activity levels and oxidative stress as blood markers of allergic response to drugs
Bibliographic record
Abstract
Antioxidant enzymes work together in human blood cells against toxic reactive oxygen species. Although their relationship with several pathophysiologic processes has been stated, not much is known about the connection between antioxidant defence and allergy. This study was designed to determine the enzymatic activities and the oxidative indices in the blood and serum proteins in patients suffering from allergy to drugs. We hypothesize that serum and blood reactions may serve as useful clinical marker for the allergic state. We used enzymatic antioxidant activities, thiobarbituric acid reactive substances, and carbonyl contents of proteins as suitable markers. We determined superoxide dismutases, glutathione peroxidase and catalase activities in each cell type. After antihistaminics plus steroids were given as part of a protocol treatment, enzymatic antioxidant activities, thiobarbituric acid reactive substance levels, and carbonyl contents were used as recovering markers for the disease. We found a relationship between antioxidant enzymatic activities, thiobarbituric acid reactive substance levels, and carbonyl contents for allergic reactions belonging to several type I and type IV allergies, as well as cross-reactive intolerance to nonsteroidal anti-inflammatory drugs and an anaphylactoid reaction to a radiocontrast media. A similar pattern also exists for analogous allergic manifestations and disease-like status.Key words: allergy, blood, catalase, glutathione peroxidase, superoxide dismutase.
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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 | 0.001 |
| 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".