The Enzymatic Antioxidants Activities Changes in Water Plants Tissues Exposed to Chlorpyrifos Stress
Bibliographic record
Abstract
Water pollution is an immense environmental problem, and plant protection products are part of it. The organophosphorus insecticides, chlorpyrifos as an example, were used for years, and their high concentration could negatively influence ecosystems. Some of the plants, such as macrophytes, were exposed to a variety of stress factors. To live on, the macrophytes developed an efficient antioxidative system consisting of enzymatic and non-enzymatic antioxidants. The remediation process of polluted water ecosystems caused by plant protection products in our climate zone can be intensified if it is provided by autochthonic macrophytes. The results of our studies are part of the research that allows optimizing the phytoremediation process without irreversible effect on investigated species. The influence of various concentrations of chlorpyrifos on the enzymatic system in Canadian waterweed (Elodea canadensis Michx.), needle spikerush (Eleocharis acicularis L.), and water mint (Mentha aquatica L.) were studied. The differences in values of guaiacol peroxidase (GPX) and glutathione S-transferase (GST) activities were determined in leaves and roots. Research indicated an increase in both enzyme activities in plants exposed to toxic compounds. The highest concentration of chlorpyrifos affected the highest activities of enzymes. The water mint roots responded with the highest value of glutathione S-transferase activity during cultivation in polluted environment. It was therefore concluded that an aqueous plant exposed to the toxic insecticide created a defensive mechanism by enzymatic antioxidant systems that correlated to the pollutant concentration and plant species.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 teacher head, 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".