Oxidative Stress-Related Biomarkers in Tissues of the Euryhaline Guppy Poecilia Vivipara Exposed In Situ to a Coastal Water Environment with a Long History of Metal Contamination
Bibliographic record
Abstract
We aimed to select reliable biomarkers of metal exposure in the eurhalyne guppy Poecilia vivipara. Individuals were exposed to three different sites in a coastal bay (i.e., Linguado Channel, Babitonga Bay, Southern Brazil), a coastal environment with a long history of metal contamination. Temperature, salinity, pH, dissolved oxygen, and dissolved organic carbon) were measured in seawater from the exposure sites. After exposure, fish were anesthetized and their tissues (i.e., gill and liver) were dissected to evaluate the Ag, Cd, Cr, Cu, Ni, Pb, and Zn concentrations, as well as a suite of biomarkers: antioxidant capacity against peroxyl radicals (ACAP), antioxidant enzyme activities (catalase and glutathione S-transferase), metallothionein-like protein (MTLP) concentration, and lipid peroxidation level (LPO). Seawater physicochemical conditions were similar in the exposure sites. Metal concentrations in tissues did not differ significantly between exposure sites. Principal component analysis indicated close correlations between Ni and ACAP, Ag/Cd and MTLPs, and Zn and LPO in the gills. In the liver, there was a close correlation between Pb and LPO. These findings highlight the importance and need for selecting relevant and suitable tissues and biomarkers for biomonitoring programs that aim to assess and monitor fish exposure to metal contamination in coastal waters. The findings also point to the need for future research focused on the response of oxidative stress‒related biomarkers to long-term in situ exposure of fish to coastal waters contaminated with metals and other inorganic and organic pollutants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".