Detection, Diagnosis, and Preventive Management of the Bacterial Plant Pathogen Pseudomonas syringae .
Piao YangLijing ZhaoYu Gary GaoYe XiaPublished in: Plants (Basel, Switzerland) (2023)
Plant diseases caused by the pathogen Pseudomonas syringae are serious problems for various plant species worldwide. Accurate detection and diagnosis of P. syringae infections are critical for the effective management of these plant diseases. In this review, we summarize the current methods for the detection and diagnosis of P. syringae , including traditional techniques such as culture isolation and microscopy, and relatively newer techniques such as PCR and ELISA. It should be noted that each method has its advantages and disadvantages, and the choice of each method depends on the specific requirements, resources of each laboratory, and field settings. We also discuss the future trends in this field, such as the need for more sensitive and specific methods to detect the pathogens at low concentrations and the methods that can be used to diagnose P. syringae infections that are co-existing with other pathogens. Modern technologies such as genomics and proteomics could lead to the development of new methods of highly accurate detection and diagnosis based on the analysis of genetic and protein markers of the pathogens. Furthermore, using machine learning algorithms to analyze large data sets could yield new insights into the biology of P. syringae and novel diagnostic strategies. This review could enhance our understanding of P. syringae and help foster the development of more effective management techniques of the diseases caused by related pathogens.
Keyphrases
- label free
- real time pcr
- loop mediated isothermal amplification
- gram negative
- antimicrobial resistance
- machine learning
- escherichia coli
- plant growth
- mental health
- small molecule
- staphylococcus aureus
- mass spectrometry
- optical coherence tomography
- electronic health record
- multidrug resistant
- biofilm formation
- high throughput
- cystic fibrosis
- big data
- genome wide
- artificial intelligence
- quantum dots
- pseudomonas aeruginosa
- single cell