Timely stroke diagnosis and intervention are necessary considering its high prevalence. Previous studies have mainly focused on stroke prediction with balanced data. Thus, this study aimed to develop machine learning models for predicting stroke with imbalanced data in an elderly population in China. Data were obtained from a prospective cohort that included 1131 participants (56 stroke patients and 1075 non-stroke participants) in 2012 and 2014, respectively. Data balancing techniques including random over-sampling (ROS), random under-sampling (RUS), and synthetic minority over-sampling technique (SMOTE) were used to process the imbalanced data in this study. Machine learning methods such as regularized logistic regression (RLR), support vector machine (SVM), and random forest (RF) were used to predict stroke with demographic, lifestyle, and clinical variables. Accuracy, sensitivity, specificity, and areas under the receiver operating characteristic curves (AUCs) were used for performance comparison. The top five variables for stroke prediction were selected for each machine learning method based on the SMOTE-balanced data set. The total prevalence of stroke was high in 2014 (4.95%), with men experiencing much higher prevalence than women (6.76% vs. 3.25%). The three machine learning methods performed poorly in the imbalanced data set with extremely low sensitivity (approximately 0.00) and AUC (approximately 0.50). After using data balancing techniques, the sensitivity and AUC considerably improved with moderate accuracy and specificity, and the maximum values for sensitivity and AUC reached 0.78 (95% CI, 0.73–0.83) for RF and 0.72 (95% CI, 0.71–0.73) for RLR. Using AUCs for RLR, SVM, and RF in the imbalanced data set as references, a significant improvement was observed in the AUCs of all three machine learning methods (p < 0.05) in the balanced data sets. Considering RLR in each data set as a reference, only RF in the imbalanced data set and SVM in the ROS-balanced data set were superior to RLR in terms of AUC. Sex, hypertension, and uric acid were common predictors in all three machine learning methods. Blood glucose level was included in both RLR and RF. Drinking, age and high-sensitivity C-reactive protein level, and low-density lipoprotein cholesterol level were also included in RLR, SVM, and RF, respectively. Our study suggests that machine learning methods with data balancing techniques are effective tools for stroke prediction with imbalanced data.

Cancer stem cells (CSCs) are subpopulations of cells with stem cell characteristics that produce both cancerous and non-tumorigenic cells in tumor tissues. The literature reports that CSCs are closely related to the development of hepatocellular carcinoma (HCC) and promote the malignant features of HCC such as high invasion, drug resistance, easy recurrence, easy metastasis, and poor prognosis. This review discusses the origin, molecular, and biological features, functions, and applications of CSCs in HCC in recent years; the goal is to clarify the importance of CSCs in treatment and explore their potential value in HCC-targeted therapy.

Hepatocellular carcinoma (HCC) is a highly lethal malignancy characterized by poor prognosis and a low 5-year survival rate. Drug treatment is proving to be effective in anti-HCC. However, only a small number of HCC patients exhibit sensitive responses, and drug resistance occurs frequently in advanced patients. Autophagy, an evolutionary process responsible for the degradation of cellular substances, is closely associated with the acquisition and maintenance of drug resistance for HCC. This review focuses on autophagic proteins and explores the intricate relationship between autophagy and cancer stem cells, tumor-derived exosomes, and noncoding RNA. Clinical trials involved in autophagy inhibition combined with anticancer drugs are also concerned.