16 research outputs found

    Applications of the titanium catalyzed cyclocarbonylation towards natural product syntheses

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    Recently an efficient methodology, a hetero Pauson-Khand reaction, based on titanium-catalyzed cyclocarbonylation of tethered enals for the general preparation of ¥ã-butyrolactone rings, which are typically embedded in polycyclic systems of many natural products was reported. To demonstrate this new strategy, the total syntheses of the natural products asteriscanolide and ginkgolide were investigated. The first part of this work is dedicated to synthetic efforts toward the total synthesis of asteriscanolide. Approaches highlighted by the [2,3]-Wittig rearrangement, the thermal silyloxy-Cope rearrangement, and a titanium-catalyzed cyclocarbonylation, which is the pivotal step to afford the ¥ã-butyrolactone ring. This study firmly established the utility of cyclocarbonylation methodology for the synthesis of complex, polycyclic organic molecules and demonstrates a useful new approach to the stereocontrolled construction of polycyclic, cyclooctanoid natural products. As part of continuing efforts in applying the cyclocarbonylation strategy towards the total synthesis of natural products, ginkgolides were chosen as second target molecules. The precursor for a hetero Pauson-Khand reaction was efficiently synthesized. It was hoped that the titanium mediated reductive cyclization of the precursor would introduce a butenolide moiety of the target model. Unfortunately, the reductive elimination step was unsuccessful. At this point, further tasks to effect reductive elimination on this system remain, in order to advance towards the target molecule

    Chronic Disease Prediction Model Using Integration of DBSCAN, SMOTE-ENN, and Random Forest

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    Heart disease (HD) is number one chronic disease and becomes a major cause of worldwide disability and death. Aside of HD, type 2 diabetes (T2D) is also as the most deathful diseases that causes serious issues if untreated and undetected. HD and T2D predictions are the most effective measures to control the HD and T2D. Thus, early HD and T2D predictions are important to help individuals in preventing the occurrence of the worst cases. This study proposes a chronic disease prediction model for HD and T2D prediction. The proposed study utilized random forest combined with DBSCAN as outlier detection method and SMOTE-ENN as data balancing method. Two HD datasets (Statlog and Cleveland) and one T2D dataset (NHIS Korea) were used for building the model and comparing the results with other existing machine learning (ML) algorithms, including GNB, LR, MLP, DT, and SVM. To measure the performance of the model, k-fold (10) cross-validation and several performance metrics including accuracy, precision, f-measure, and recall are applied in this study. The results show the model that we proposed outperforms other classification models, as well as previous studies, with accuracy rates 97.63%, 97.69%, and 94.85% for Statlog HD dataset, Cleveland HD dataset and NHIS T2D dataset, respectively. By utilizing the proposed model, it could increase the expectation in preventing the occurrence of the worst case and helping individuals in taking fast and precise actions when status of HD and T2D are detected
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