Liver injury occurs frequently during sepsis, which leads to high mortality and morbidity. A previous study has suggested that salvianolic acid B (SalB) is protective against sepsis-induced lung injury. However, whether SalB is able to protect against sepsis-induced liver injury remains unclear. The present study aimed to investigate the effects of SalB on sepsis-induced liver injury and its potential underlying mechanisms. Sepsis was induced in mice using a cecal ligation and puncture (CLP) method. The mice were treated with SalB (30 mg/kg intraperitoneally) at 0.5, 2 and 8 h after CLP induction. Pathological alterations of the liver were assessed using hematoxylin and eosin staining. The serum levels of alanine transaminase (ALT), aspartate aminotransferase (AST), tumor necrosis factor (TNF)-α and interleukin (IL)-6 were measured. The hepatic mRNA levels of TNF-α, IL-6, Bax and Bcl-2 were also detected. The results suggested that treatment with SalB ameliorated sepsis-induced liver injury in the mice, as supported by the mitigated pathologic changes and lowered serum aminotransferase levels. SalB also decreased the levels of the inflammatory cytokines TNF-α and IL-6 in the serum and the liver of the CLP model mice. In addition, SalB significantly downregulated Bax expression and upregulated Bcl-2 expression, and upregulated the expression levels of SIRT1 and PGC-1α. However, when sirtuin 1 (SIRT1) small interfering RNA was co-administered with SalB, the protective effects of SalB were attenuated and the expression levels of SIRT1 and PGC-1α were reduced. In summary, these results indicate that SalB mitigates sepsis-induced liver injury via reduction of the inflammatory response and hepatic apoptosis, and the underlying mechanism may be associated with the activation of SIRT1/PGC-1α signaling.

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Purpose of Review One in five people will develop heart failure (HF), and 50% of HF patients die in 5 years. The HF diagnosis, readmission, and mortality prediction are essential to develop personalized prevention and treatment plans. This review summarizes recent findings and approaches of machine learning models for HF diagnostic and outcome prediction using electronic health record (EHR) data. Recent Findings A set of machine learning models have been developed for HF diagnostic and outcome prediction using diverse variables derived from EHR data, including demographic, medical note, laboratory, and image data, and achieved expert-comparable prediction results. Summary Machine learning models can facilitate the identification of HF patients, as well as accurate patient-specific assessment of their risk for readmission and mortality. Additionally, novel machine learning techniques for integration of diverse data and improvement of model predictive accuracy in imbalanced data sets are critical for further development of these promising modeling methodologies.

BMD identified on images from dual-energy X-ray absorptiometry were strongly related to multidetector computed tomography measures of CAC. This low-cost, minimal radiation technique used widely for OP screening is a promising marker of generalized coronary atherosclerosis.