Machine learning models for predicting the risk factor of carotid plaque in cardiovascular disease

IntroductionCardiovascular disease (CVD) is a group of diseases involving the heart or blood vessels and represents a leading cause of death and disability worldwide. Carotid plaque is an important risk factor for CVD that can reflect the severity of atherosclerosis. Accordingly, developing a prediction model for carotid plaque formation is essential to assist in the early prevention and management of CVD.MethodsIn this study, eight machine learning algorithms were established, and their performance in predicting carotid plaque risk was compared. Physical examination data were collected from 4,659 patients and used for model training and validation. The eight predictive models based on machine learning algorithms were optimized using the above dataset and 10-fold cross-validation. The Shapley Additive Explanations (SHAP) tool was used to compute and visualize feature importance. Then, the performance of the models was evaluated according to the area under the receiver operating characteristic curve (AUC), feature importance, accuracy and specificity.ResultsThe experimental results indicated that the XGBoost algorithm outperformed the other machine learning algorithms, with an AUC, accuracy and specificity of 0.808, 0.749 and 0.762, respectively. Moreover, age, smoke, alcohol drink and BMI were the top four predictors of carotid plaque formation. It is feasible to predict carotid plaque risk using machine learning algorithms.ConclusionsThis study indicates that our models can be applied to routine chronic disease management procedures to enable more preemptive, broad-based screening for carotid plaque and improve the prognosis of CVD patients

Identification and Characterization of the Glucose-6-Phosphate Dehydrogenase Gene Family in the Para Rubber Tree, Hevea brasiliensis

As the key enzyme in the pentose phosphate pathway (PPP), glucose-6-phosphate dehydrogenase (G6PDH) provides NADPH and intermediary metabolites for rubber biosynthesis, and plays an important role in plant development and stress responses. In this study, four Hevea brasiliensis (para rubber tree) HbG6PDH genes were identified and cloned using a genome-wide scanning approach. On the basis of phylogeny analysis and subcellular localization predictions, HbG6PDH3 is a cytosolic isoform, while the other three genes (HbG6PDH1, 2 and 4) are plastidic molecular variants. Enzyme activity assay and expression pattern analysis showed HbG6PDHs to be involved in latex regeneration, and to influence rubber production positively in the rubber tree. The cytosolic HbG6PDH3 is the predominant isoform in latex, implying a principal role for this isoform in controlling carbon flow and NADPH production in the pentose phosphate pathway during latex regeneration. The expression pattern of the plastidic HbG6PDH4 correlates well with the degree of tapping panel dryness, a physiological disorder that stops the flow of latex from affected rubber trees. In addition, the four HbG6PDHs responded to temperature change and drought stress in the root, bark and leaves, implicating their possible roles in maintaining redox balance and defending against oxidative stress

Regulation of MIR Genes in Response to Abiotic Stress in Hevea brasiliensis

Increasing demand for natural rubber (NR) calls for an increase in latex yield and also an extension of rubber plantations in marginal zones. Both harvesting and abiotic stresses lead to tapping panel dryness through the production of reactive oxygen species. Many microRNAs regulated during abiotic stress modulate growth and development. The objective of this paper was to study the regulation of microRNAs in response to different types of abiotic stress and hormone treatments in Hevea. Regulation of MIR genes differs depending on the tissue and abiotic stress applied. A negative co-regulation between HbMIR398b with its chloroplastic HbCuZnSOD target messenger is observed in response to salinity. The involvement of MIR gene regulation during latex harvesting and tapping panel dryness (TPD) occurrence is further discussed