We prospectively analyzed clinical and radiographic outcomes of three consecutive cohorts for a total of 227 implants at a minimum follow-up of 36 months. The aim of the study is to evaluate whether the use of the new instrumentation Microplasty (MP) improves component positioning and the reliability of the surgical technique, reducing the implant outliers from the recommended range and providing a more accurate resection, while avoiding insufficient or excessive tibial resection and clinical scores. Additionally, the discovered biomarkers were verified by the metabolic pathway analysis and content change analysis, which was remarkably consistent with the previous reports.
The proposed method proved to be practical for early detection of HLB, which tackled the shortcomings of low sensitivity in the conventional methods and avoid the problems such as lighting condition interference in spectrum/image recognition-based ML methods. Regularized logistic regression (LR-L2) and gradient-boosted decision tree (GBDT) outperformed with the highest average accuracy of 95.83% to not only classify healthy and infected plants but identify significant features. Six ML algorithms were selected to build the classifiers. In this study, a novel method combining ultra-high performance liquid chromatography/mass spectrometry (UHPLC/MS)-based nontargeted metabolomics and machine learning (ML) was developed for conducting the early detection of HLB for the first time.
Thus, it is very necessary to develop a practical method used for the early detection of HLB. The direct strategies for HLB identification, such as quantitative real-time polymerase chain reaction (qPCR) and chemical staining, are robust for the symptomatic plants but powerless for the asymptomatic ones at the early stage of affection. Huanglongbing (HLB), one of the most destructive citrus diseases, has brought about severe economic losses for the global citrus industry.
Early accurate detection of crop disease is extremely important for timely disease management.
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