Rat induced pluripotent stem cells protect H9C2 cells from cellular senescence via a paracrine mechanism.

OBJECTIVES: Cellular senescence may play an important role in the pathology of heart aging. We aimed to explore whether induced pluripotent stem cells (iPSCs) could inhibit cardiac cellular senescence via a paracrine mechanism. METHODS: We collected iPSC culture supernatant, with or without oxidative stress, as conditioned medium (CM) for the rat cardiomyocyte-derived cell line H9C2. Then we treated H9C2 cells, cultured with or without CM, with hypoxia/reoxygenation to induce cellular senescence and measured senescence-associated ß-galactosidase (SA-ß-gal) activity, G1 cell proportion and expression of the cell cycle regulators p16(INK4a), p21(Waf1/Cip1) and p53 at mRNA and protein levels in H9C2 cells. In addition, we used Luminex-based analysis to measure concentrations of trophic factors in iPSC-derived CM. RESULTS: We found that iPSC-derived CM reduced SA-ß-gal activity, attenuated G1 cell cycle arrest and reduced the expression of p16(INK4a), p21(Waf1/Cip1) and p53 in H9C2 cells. Furthermore, the CM contained more trophic factors, e.g. tissue inhibitor of metalloproteinase-1 and vascular endothelial growth factor, than H9C2-derived CM. CONCLUSIONS: Paracrine factors released from iPSCs prevent stress-induced senescence of H9C2 cells by inhibiting p53-p21 and p16-pRb pathways. This is the first report demonstrating that antisenescence effects of stem cell therapy may be a novel therapeutic strategy for age-related cardiovascular disease.

GT-LSTM: A spatio-temporal ensemble network for traffic flow prediction.

Traffic flow prediction plays an instrumental role in modern intelligent transportation systems. Numerous existing studies utilize inter-embedded fusion routes to extract the intrinsic patterns of traffic flow with a single temporal learning approach, which relies heavily on constructing graphs and has low training efficiency. Different from existing studies, this paper proposes a spatio-temporal ensemble network that aims to leverage the strengths of different sequential capturing approaches to obtain the intrinsic dependencies of traffic flow. Specifically, we propose a novel model named graph temporal convolutional long short-term memory network (GT-LSTM), which mainly consists of features splicing and patterns capturing. In features splicing, the spatial dependencies of traffic flow are captured by employing self-adaptive graph convolutional network (GCN), and a non-inter-embedded approach is designed to integrate the spatial and temporal states. Further, the aggregated spatio-temporal states are fed into patterns capturing, which can effectively exploit the advantages of temporal convolutional network (TCN) and bidirectional long short-term memory network (Bi-LSTM) to extract the intrinsic patterns of traffic flow. Extensive experiments conducted on four real-world datasets demonstrate that the proposed network obtains excellent performance in both forecasting accuracy and training efficiency.

A Post-Hoc Interpretable Ensemble Model to Feature Effect Analysis in Warfarin Dose Prediction for Chinese Patients.

To interprete the importance of clinical features and genotypes for warfarin daily dose prediction, we developed a post-hoc interpretable framework based on an ensemble predictive model. This framework includes permutation importance for global interpretation and local interpretable model-agnostic explanation (LIME) and shapley additive explanations (SHAP) for local explanation. The permutation importance globally ranks the importance of features on the whole data set. This can guide us to build a predictive model with less variables and the complexity of final predictive model can be reduced. LIME and SHAP together explain how the predictive model give the predicted dosage for specific samples. This help clinicians prescribe accurate doses to patients using more effective clinical variables. Results showed that both the permutation importance and SHAP demonstrated that VKORC1, age, serum creatinine (SCr), left atrium (LA) size, CYP2C9 and weight were the most important features on the whole data set. In specific samples, both SHAP and LIME discovered that in Chinese patients, wild-type VKORC1-AA, mutant-type CYP2C9*3, age over 60, abnormal LA size, SCr within the normal range, and using amiodarone definitely required dosage reduction, whereas mutant-type VKORC1-AG/GG, small age, SCr out of normal range, normal LA size, diabetes and heavy weight required dosage enhancementt.

DBCSMOTE: a clustering-based oversampling technique for data-imbalanced warfarin dose prediction.

BACKGROUND: Vitamin K antagonist (warfarin) is the most classical and widely used oral anticoagulant with assuring anticoagulant effect, wide clinical indications and low price. Warfarin dosage requirements of different patients vary largely. For warfarin daily dosage prediction, the data imbalance in dataset leads to inaccurate prediction on the patients of rare genotype, who usually have large stable dosage requirement. To balance the dataset of patients treated with warfarin and improve the predictive accuracy, an appropriate partition of majority and minority groups, together with an oversampling method, is required. METHOD: To solve the data-imbalance problem mentioned above, we developed a clustering-based oversampling technique denoted as DBCSMOTE, which combines density-based spatial clustering of application with noise (DBCSCAN) and synthetic minority oversampling technique (SMOTE). DBCSMOTE automatically finds the minority groups by acquiring the association between samples in terms of the clinical features/genotypes and the warfarin dosage, and creates an extended dataset by adding the new synthetic samples of majority and minority groups. Meanwhile, two ensemble models, boosted regression tree (BRT) and random forest (RF), which are built on the extended dataset generateed by DBCSMOTE, accomplish the task of warfarin daily dosage prediction. RESULTS: DBCSMOTE and the comparison methods were tested on the datasets derived from our Hospital and International Warfarin Pharmacogenetics Consortium (IWPC). As the results, DBCSMOTE-BRT obtained the highest R-squared (R2) of 0.424 and the smallest mean squared error (mse) of 1.08. In terms of the percentage of patients whose predicted dose of warfarin is within 20% of the actual stable therapeutic dose (20%-p), DBCSMOTE-BRT can achieve the largest value of 47.8% among predictive models. The more important thing is that DBCSMOTE saved about 68% computational time to achieve the same or better performance than the Evolutionary SMOTE, which was the best oversampling method in warfarin dose prediction by far. Meanwhile, in warfarin dose prediction, it is discovered that DBCSMOTE is more effective in  integrating BRT than RF  for warfarin dose prediction. CONCLUSION: Our finding is that the genotypes, CYP2C9 and VKORC1, no doubt contribute to the predictive accuracy. It was also discovered left atrium diameter, glutamic pyruvic transaminase and serum creatinine included in the model actually improved the predictive accuracy; When congestive heart failure, diabetes mellitus and valve replacement were absent in DBCSMOTE-BRT/RF, the predictive accuracy of DBCSMOTE-BRT/RF decreased. The oversampling ratio and number of minority clusters have a large impact on the effect of oversampling. According to our test, the predictive accuracy was high when the number of minority clusters was 6 ~ 8. The oversampling ratio for small minority clusters should be large (> 1.2) and for large minority clusters should be small (< 0.2). If the dataset becomes larger, the DBCSMOTE would be re-optimized and its BRT/RF model should be re-trained. DBCSMOTE-BRT/RF outperformed the current commonly-used tool called Warfarindosing. As compared to Evolutionary SMOTE-BRT and RF  models, DBCSMOTE-BRT and RF models take only a small computational time to achieve the same or higher performance in many cases. In terms of predictive accuracy, RF is not as good as BRT. However, RF still has a powerful ability in generating a highly accurate model as the dataset increases; the software "WarfarinSeer v2.0" is a test version, which packed DBCSMOTE-BRT/RF. It could be a convenient tool for clinical application in warfarin treatment.

An Ensemble Model With Clustering Assumption for Warfarin Dose Prediction in Chinese Patients.

The prediction of daily stable warfarin dosage for a specific patient is difficult. To improve the predictive accuracy and to build a highly accurate predictive model, we developed an ensemble learning method, called evolutionary fuzzy c-mean (EFCM) clustering algorithm with support vector regression (SVR). A dataset of 517 Han Chinese patients was collected from the data of The First Affiliated Hospital of Soochow University and dataset of International Warfarin Pharmacogenetics Consortium for training and testing. In EFCM+SVR, we adopted SVR to build a generalized base model (SVR model). To achieve an accurate prediction on patients with large dosage, we proposed an EFCM clustering algorithm that can be used to cluster the training set and designed a clustering model on clusters and centroids. The SVR and clustering models were integrated into an ensemble model by stepwise functions. In the experiment, three artificial neural networks, SVR, two ensemble models, and three regression models were used as comparators to the EFCM+SVR model, which obtained the smallest mean absolute error (0.67 mg/d) in warfarin dose prediction and the largest R-squared (43.9%). The model achieved satisfactory prediction in terms of the percentage of patients whose predicted dose of warfarin was within 15% and 20% of the actual stable therapeutic dose (15%-p of 36% and 20%-p of 46.6%).

Evolutionary Ensemble Learning Algorithm to Modeling of Warfarin Dose Prediction for Chinese.

An evolutionary ensemble modeling (EEM) method is developed to improve the accuracy of warfarin dose prediction. In EEM, genetic programming (GP) evolves diverse base models, and the genetic algorithm optimizes the parameters of the GP. The EEM model is assembled by using the prepared base models through a technique called "bagging." In the experiment, a dataset of 289 Chinese patients, which was provided by the First Affiliated Hospital of Soochow University, is used for training, validation, and testing. The EEM model with selected feature groups is benchmarked with four machine-learning methods and three conventional regression models. Results show that the EEM model with the M2+G group, namely age, height, weight, gender, CYP2C9, VKORC1, and amiodarone, presents the largest coefficients of determination (R2), the highest percentage of the predicted dose within 20% of the actual dose (20%-p), the smallest mean absolute error, mean squared error, and root-mean-squared error on the test set, and the least decrease in R2 from the training set to the test set. In conclusion, the EEM method with M2+G delivers superior performance and can, therefore, be a suitable prediction model of warfarin dose for clinical applications.