Effect of TiO2 Nanoparticles on Antioxidant Enzymes Activity and Biochemical Biomarkers in Pinto Bean (Phaseolus vulgaris L.)
Bibliographic record
Abstract
Tests were done on the effects of titanium dioxide spray on Pinto bean (Phaseolus vulgaris L. c.v ‘c.o.s.16’). The study was conducted as a factorial experiment in a randomized complete block design with four replications for two years (2014 - 2015). Treatments consisted of two factors; the first factor was stage of plant growth that spraying was applied (rapid vegetative growth, flowering and pod filling); and the second factor was that of different concentrations of titanium dioxide nanoparticles (TiO2) that consisted of spray with water (control), nano titanium dioxide at concentrations of 0.01%, 0.02%, 0.03% and 0.05%. Activity of guaiacol peroxidase (GPX), activity of superoxide dismutase (SOD), activity of catalase (CAT), activity of peroxidase (POD), malonyldialdehyde (MDA) Content and 8-deoxy-2-hydroxyguanosine (8-OHDG) content were assayed. Results showed that effect of nano TiO2 was significant on activity of superoxide dismutase (SOD), activity of catalase (CAT), activity of peroxidase (POD), malonyldialdehyde (MDA) Content and 8-deoxy-2-hydroxyguanosine (8-OHDG) content. Results of combined analysis of variance showed that the effect year significantly affected on SOD and 8-OH-2-DG (P ≤ 0.05). The effect of different amounts of titanium dioxide nanoparticles (TiO2) significantly affected (P ≤ 0.05) on MDA and 8-OH-2-DG. The effects of different amounts of titanium dioxide nanoparticles and year were significant on SOD, POD, MDA and the amount of 8-deoxy-2-hydroxyguanosine in P ≤ 0.05. None of the physiological traits were affected by spraying of nano titanium dioxide. The effects of TiO2 nanoparticles times of spraying and year were significant on SOD, CAT and 8-deoxy-2-hydroxyguanosine (P ≤ 0.05). Interaction effects of nano TiO2 concentrations × nano TiO2 spraying times did not have a significant impact on SOD, CAT, POD, GPX, MDA and 8-OH-2-DG. Although, all trait were affected by interaction effects of year × nano TiO2 concentrations × nano TiO2 spraying times with the exception of GPX (P ≤ 0.05).
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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) | 0.000 | 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".