Harnessing Bacterial Endophytes for Promotion of Plant Growth and Biotechnological Applications: An Overview
Bibliographic record
Abstract
Endophytic bacteria colonize plants and live inside them for part of or throughout their life without causing any harm or disease to their hosts. The symbiotic relationship improves the physiology, fitness, and metabolite profile of the plants, while the plants provide food and shelter for the bacteria. The bacteria-induced alterations of the plants offer many possibilities for biotechnological, medicinal, and agricultural applications. The endophytes promote plant growth and fitness through the production of phytohormones or biofertilizers, or by alleviating abiotic and biotic stress tolerance. Strengthening of the plant immune system and suppression of disease are associated with the production of novel antibiotics, secondary metabolites, siderophores, and fertilizers such as nitrogenous or other industrially interesting chemical compounds. Endophytic bacteria can be used for phytoremediation of environmental pollutants or the control of fungal diseases by the production of lytic enzymes such as chitinases and cellulases, and their huge host range allows a broad spectrum of applications to agriculturally and pharmaceutically interesting plant species. More recently, endophytic bacteria have also been used to produce nanoparticles for medical and industrial applications. This review highlights the biotechnological possibilities for bacterial endophyte applications and proposes future goals for their application.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".