Recent evidence indicates that inhibition of histone deacetylase (HDAC) protects the heart against myocardial injury and stimulates endogenous angiomyogenesis. However, it remains unknown whether HDAC inhibition produces the protective effect in the diabetic heart. We sought to determine whether HDAC inhibition preserves cardiac performance and suppresses cardiac remodeling in diabetic cardiomyopathy. Adult ICR mice received an intraperitoneal injection of either streptozotocin (STZ, 200 mg/kg) to establish the diabetic model or vehicle to serve as control. Once hyperglycemia was confirmed, diabetic mice received sodium butyrate (1%), a specific HDAC inhibitor, in drinking water on a daily basis to inhibit HDAC activity. Mice were randomly divided into following groups, which includes Control, Control + Sodium butyrate (NaBu), STZ and STZ + Sodium butyrate (NaBu), respectively. Myocardial function was serially assessed at 7, 14, 21 weeks following treatments. Echocardiography demonstrated that cardiac function was depressed in diabetic mice, but HDAC inhibition resulted in a significant functional improvement in STZ-injected mice. Likewise, HDAC inhibition attenuates cardiac hypertrophy, as evidenced by a reduced heart/tibia ratio and areas of cardiomyocytes, which is associated with reduced interstitial fibrosis and decreases in active caspase-3 and apoptotic stainings, but also increased angiogenesis in diabetic myocardium. Notably, glucose transporters (GLUT) 1 and 4 were up-regulated following HDAC inhibition, which was accompanied with increases of GLUT1 acetylation and p38 phosphorylation. Furthermore, myocardial superoxide dismutase, an important antioxidant, was elevated following HDAC inhibition in the diabetic mice. HDAC inhibition plays a critical role in improving cardiac function and suppressing myocardial remodeling in diabetic heart.

The classification of benign and malignant based on ultrasound images is of great value because breast cancer is an enormous threat to womens health worldwide. Although both texture and morphological features are crucial representations of ultrasound breast tumor images, their straightforward combination brings little effect for improving the classification of benign and malignant since high-dimensional texture features are too aggressive so that drown out the effect of low-dimensional morphological features. For that, an efficient texture and morphological feature combing method is proposed to improve the classification of benign and malignant. Firstly, both texture (i.e., local binary patterns (LBP), histogram of oriented gradients (HOG), and gray-level co-occurrence matrixes (GLCM)) and morphological (i.e., shape complexities) features of breast ultrasound images are extracted. Secondly, a support vector machine (SVM) classifier working on texture features is trained, and a naive Bayes (NB) classifier acting on morphological features is designed, in order to exert the discriminative power of texture features and morphological features, respectively. Thirdly, the classification scores of the two classifiers (i.e., SVM and NB) are weighted fused to obtain the final classification result. The low-dimensional nonparameterized NB classifier is effectively control the parameter complexity of the entire classification system combine with the high-dimensional parametric SVM classifier. Consequently, texture and morphological features are efficiently combined. Comprehensive experimental analyses are presented, and the proposed method obtains a 91.11 accuracy, a 94.34 sensitivity, and an 86.49 specificity, which outperforms many related benign and malignant breast tumor classification methods.

Artificial intelligence (AI) has emerged as a crucial technique for extracting high‑throughput information from various sources, including medical images, pathological images, and genomics, transcriptomics, proteomics and metabolomics data. AI has been widely used in the field of diagnosis, for the differentiation of benign and malignant ovarian cancer (OC), and for prognostic assessment, with favorable results. Notably, AI‑based radiomics has proven to be a non‑invasive, convenient and economical approach, making it an essential asset in a gynecological setting. The present study reviews the application of AI in the diagnosis, differentiation and prognostic assessment of OC. It is suggested that AI‑based multi‑omics studies have the potential to improve the diagnostic and prognostic predictive ability in patients with OC, thereby facilitating the realization of precision medicine.

Objective. Chronic heart failure (CHF) refers to a state of persistent heart failure that can be stable, deteriorated, or decompensated. The mechanism and pathogenesis of myocardial remodeling remain unknown. Based on 16S rDNA sequencing and metabolomics technology, this study analyzed the gut microbiota and serum metabolome in elderly patients with CHF to provide new insights into the microbiota and metabolic phenotypes of CHF. Methods. Blood and fecal samples were collected from 25 elderly patients with CHF and 25 healthy subjects. The expression of inflammatory factors in blood was detected by ELISA. 16S rDNA sequencing was used to analyze the changes in microorganisms in the samples. The changes of small molecular metabolites in serum samples were analyzed by LC‐MS/MS. Spearman correlation coefficients were used to analyze the correlation between gut microbiota and serum metabolites. Results. Our results showed that the IL‐6, IL‐8, and TNF‐α levels were significantly increased, and the IL‐10 level was significantly decreased in the elderly patients with CHF compared with the healthy subjects. The diversity of the gut microbiota was decreased in the elderly patients with CHF. Moreover, Escherichia Shigella was negatively correlated with biocytin and RIBOFLAVIN. Haemophilus was negatively correlated with alpha‐lactose, cellobiose, isomaltose, lactose, melibiose, sucrose, trehalose, and turanose. Klebsiella was positively correlated with bilirubin and ethylsalicylate. Klebsiella was negatively correlated with citramalate, hexanoylcarnitine, inosine, isovalerylcarnitine, methylmalonate, and riboflavin. Conclusion. The gut microbiota is simplified by the disease, and serum small‐molecule metabolites evidently change in elderly patients with CHF. Serum and fecal biomarkers could be used for elderly patients with CHF screening.