Antibiotic resistance genes proliferation under anaerobic degradation of polylactic acid and polyhydroxy butyrate bioplastics
Bibliographic record
Abstract
As the global concern over plastic pollution grows, efforts are underway to find environmentally friendly alternatives to traditional plastics. Bioplastics are being extensively researched and developed as a possible solution. This study compared the impact of two bioplastics, polylactic acid (PLA) and polyhydroxy butyrate (PHB), on the proliferation of antibiotic resistance genes (ARGs) and mobile genetic elements (MGEs) during anaerobic digestion (AD). Both bioplastics (250-500 particles) could be degraded to a certain extent over 79 days, as indicated by higher methane production than the control without bioplastic particles. The PHB 500 reactor showed the highest methane yield along with the highest biodegradation efficiency (91 %) than other reactors amended with PHB and PLA particles. The highest ARG and MGE abundances were also observed in PLA 500, and the lowest ARG abundance was in PLA 250. Conversely, PHB reactors showed a relatively lower ARG abundance than the control. The correlation analysis suggested that most ARGs were positively correlated with PLA and negatively correlated with PHB (except for tetA, tetB, and tetX). Moreover, a correlation between MGEs and ARGs in PLA and PHB reactors was revealed by correlation analysis. These results show that AD responds differently to the different types/levels of bioplastics, which can ultimately influence the behavior of ARG proliferation. Thus, bioplastics may also pose a potential risk for spreading antibiotic resistance. These findings can be used as a basis for setting environmental standards for bioplastics and creating monitoring and control measures to prevent potential negative impacts on public health.
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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 | 0.000 | 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 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".