The detoxification effects of He-Ne laser irradiation on cytotoxicity of cadmium sulfide nanoparticles (CdSNPs) in tall fescue seedlings
Bibliographic record
Abstract
Because of their wide applications in manufacturing life science and medicine, nanoparticles (NPs) are becoming potential limiting factors for agricultural plant growth and productivity. Thus, development of some low cost and low risk methods for detoxifying or alleviating cytotoxicity of NPs on crops would be a proactive attractive solution. In this study, we investigated the ameliorating effect of He-Ne laser illumination of seedlings on cytotoxicity of cadmium sulfide nanoparticles (CdSNPs) in tall fescue seedlings. Physiological parameters and biochemical characteristics of 20-d-old seedlings with different times of laser illumination and graded doses of CdSNPs treatment, either alone or in combination, were measured. Cadmiun sulfide nanoparticles exhibited pronounced inhibitory effect on shoot and root growth, respectively. Accumulation of CdSNPs in plant root cells resulted in oxidative stress as detected by the formation of reactive oxygen species (ROS), which aggravate the cytotoxicity of CdSNPs on seedlings. Treatment of seedlings with CdSNPs alone also reduced antioxidant concentration, phytochelatins (PCs) content, enzymatic activities of phytochelatin synthase (PCS), and antioxidant enzymes, such as CAT, APX, GR, and POD, except SOD. However, He-Ne laser illumination prior to CdSNPs exposure alleviated the inhibitory effect on seedling growth and development through activation of tall fescue antioxidant systems due to induction of antioxidants biosynthesis and antioxidant enzyme activities. Furthermore, PCs and GSH induced by laser was also demonstrated to be involved in decreasing the cytotoxicity of CdSNPs through binding free cadmium ions release from CdSNPs. The interactive mechanisms between He-Ne lasers, plant antioxidant systems, and PCs biosynthesis pathways need be further investigated.
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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".