The Toxic Effect of Drug Residues on the Germination of Cultivated Plants
Bibliographic record
Abstract
Over the past decades, industrial development has led to the identification of several new chemicals in the aquatic environment, which are pollutants of growing concern for water management.These compounds are referred to as "emerging contaminants (ECs)", micropollutants (MPs) or trace organic compounds (TrOCs) [1].In nature, these pollutants are synthetic or naturally occurring contaminants, most of which are of organic origin and typically occur in trace amounts.As emerging micropollutants, their detection in water is difficult and their long-term ecological and health effects are not yet known.In many cases, they have been shown to have known or suspected adverse effects on the aquatic environment or human health [2].Organic micropollutants cannot be fully removed by conventional wastewater treatment processes and therefore accumulate through biomagnification and are spread through the food chain [3].Because drugs are designed to perform different physiological and biochemical functions, they can penetrate biological barriers and persist stably in the human body.Antibiotics used in food (milk, meat, eggs, fruit, vegetables and fish) as growth promoters, therapeutic and preventive agents can pose ecological and health risks if released into the environment [4,5].Globally, pharmaceuticals and their metabolites have been detected in wastewater, groundwater and even drinking water [6].Contamination levels of antibiotics in wastewater can reach 10-100 mg/L, but the majority of reports have shown levels in the ng-µg/L range, and water in this form cannot be used in agriculture [7].The main objective of this study is to investigate the toxic effects of pharmaceuticals (tetracycline and ampicillin) on germination through standard tests such as germination, growth, morphological/anatomical changes of the germ.White mustard (Sinapis alba) and lettuce (Lactuca sativa) were used as model plants.Our experiment was performed on seeds (25 seeds) placed on filter paper in sterilized Petri-dishes.Samples were germinated for 72 hours in the dark at T= 20±2 o C on solutions of different initial concentrations of analytical grade and commercially used ampicillin and tetracycline (5-5 mL, 0-10 g/L).Our tests were carried out in 6 replicates.Seedling growth inhibition (SGI), relative germination, relative root growth and germination index were calculated.The morphology was studied and compared by microscopy.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".