Beneficial effects of nontoxic ozone on H<sub>2</sub>O<sub>2</sub>-induced stress and inflammation
Bibliographic record
Abstract
In this study, the anti-oxidant and anti-inflammatory efficacy of ozone oxidative preconditioning (OOP) were investigated on hydrogen peroxide (H2O2)-induced human lung alveolar cells. In MTT and trypan blue viability tests, while 100 μmol/L H2O2 caused a 17.3% and 21.9% decrease in the number of living cells, respectively, ozone at 20 μmol/L regenerated cell proliferation and prevented 9.6% and 11.0% of cell loss, respectively. In addition, H2O2 decreased the transcription levels of catalase (CAT), glutathione peroxidase (GPx), and superoxide dismutase (SOD) 5.43-, 2.89-, and 5.33-fold, respectively, while it increased Bax, NF-κβ, TNF-α, and iNOS expression 1.57-, 1.32-, 1.40-, and 1.41-fold, respectively. Ozone pretreatment, however, increased CAT, GPx, and SOD transcription levels 7.08-, 5.17-, and 6.49-fold and decreased Bax, NF-κβ, TNF-α, and iNOS transcriptions by 1.25-, 0.76-, 3.63-, and 7.91-fold, respectively. Moreover, intracellular glutathione (GSH) level and SOD activity were decreased by 46.2% and 45.0% in the H2O2 treatment group, and OOP recovered 58.5% and 20.1% of the decreases caused by H2O2. H2O2 also increased nitrite levels 7.84-fold, and OOP reduced this increase by half. Consequently, OOP demonstrated potent anti-oxidant and anti-inflammatory effects on in vitro model of oxidative stress-induced lung injury.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".