Reactive Oxygen Species in the Adverse Outcome Pathway Framework: Toward Creation of Harmonized Consensus Key Events
Bibliographic record
Abstract
Reactive oxygen species (ROS) and reactive nitrogen species (RNS) are formed as a result of natural cellular processes, intracellular signaling, or as adverse responses associated with diseases or exposure to oxidizing chemical and non-chemical stressors. The action of ROS and RNS, collectively referred to as reactive oxygen and nitrogen species (RONS), has recently become highly relevant in a number of adverse outcome pathways (AOPs) that capture, organize, evaluate and portray causal relationships pertinent to adversity or disease progression. RONS can potentially act as a key event (KE) in the cascade of responses leading to an adverse outcome (AO) within such AOPs, but are also known to modulate responses of events along the AOP continuum without being an AOP event itself. A substantial discussion has therefore been undertaken in a series of workshops named "Mystery or ROS" to elucidate the role of RONS in disease and adverse effects associated with exposure to stressors such as nanoparticles, chemical, and ionizing and non-ionizing radiation. This review introduces the background for RONS production, reflects on the direct and indirect effects of RONS, addresses the diversity of terminology used in different fields of research, and provides guidance for developing a harmonized approach for defining a common event terminology within the AOP developer community.
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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 | 0.022 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".