Antibiotic resistance in plant growth promoting bacteria: A comprehensive review and future perspectives to mitigate potential gene invasion risks
Bibliographic record
Abstract
Plant growth-promoting bacteria (PGPB) are endowed with several attributes that can be beneficial for host plants. They opened myriad doors toward green technology approach to reduce the use of chemical inputs, improve soil fertility, and promote plants' health. However, many of these PGPB harbor antibiotic resistance genes (ARGs). Less attention has been given to multi-resistant bacterial bioinoculants which may transfer their ARGs to native soil microbial communities and other environmental reservoirs including animals, waters, and humans. Therefore, large-scale inoculation of crops by ARGs-harboring bacteria could worsen the evolution and dissemination of antibiotic resistance and aggravate the negative impacts on such ecosystem and ultimately public health. Their introduction into the soil could serve as ARGs invasion which may inter into the food chain. In this review, we underscore the antibiotic resistance of plant-associated bacteria, criticize the lack of consideration for this phenomenon in the screening and application processes, and provide some recommendations as well as a regulation framework relating to the development of bacteria-based biofertilizers to aid maximizing their value and applications in crop improvement while reducing the risks of ARGs invasion.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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 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".