Toxic effects of <i>Heterosigma akashiw</i>o do not appear to be mediated by hydrogen peroxide
Bibliographic record
Abstract
The ichthyotoxic red tide organism Heterosigma akashiwo (Raphidophyceae) has been associated with fish kill events within the aquaculture industry for many years. The precise toxicological mechanism involved in these fish kills is unclear; however, much research attention has focused on the production of reactive oxygen species (ROS) by these toxic algae. In this study, we investigated the production of hydrogen peroxide (H2O2) by isolates of H. akashiwo and the nontoxic chlorophyte Tetraselmis apiculata. Subsequently, we tested those concentrations of H2O2 on vertebrate cell lines and the invertebrate Artemia salina (brine shrimp) to investigate mortality. Net production rates for the H. akashiwo isolates ranged from 0.46 to 7.89 pmol H2O2 min−1 (104 cells)−1 while obtaining maximum concentrations between 0.14 and 0.91 µM H2O2. Conversely, T. apiculata produced only 0.03 pmol H2O2 min±1 (104 cells with a maximum level on 0.04 µM. However, toxic effects on UMRߚ106 and HEKߚ293 cells were only induced by acute and protracted exposure to concentrations of H2O2 >= 0.1 mM. Additionally, significant mortality of A. salina in the presence or absence of ferric and ferrous iron was induced by H2O2 levels >= 1 mM. Iron is a redox metal that reduces H2O2 to hydroxy radicals. These data collectively indicate that production of H2O2 by multiple isolates of H. akashiwo is orders of magnitude less than that required for mortality of either the vertebrate cell lines or the invertebrate A. salina. Other nonichthyotoxic roles for extracellular ROS are proposed.
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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".