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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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.

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.

Objective: Although many clinical metrics are associated with proximity to decompensation in heart failure (HF), none are individually accurate enough to risk-stratify HF patients on a patient-by-patient basis. The dire consequences of this inaccuracy in risk stratification have profoundly lowered the clinical threshold for application of high-risk surgical intervention, such as ventricular assist device placement. Machine learning can detect non-intuitive classifier patterns that allow for innovative combination of patient feature predictive capability. A machine learning-based clinical tool to identify proximity to catastrophic HF deterioration on a patient-specific basis would enable more efficient direction of high-risk surgical intervention to those patients who have the most to gain from it, while sparing others. Synthetic electronic health record (EHR) data are statistically indistinguishable from the original protected health information, and can be analyzed as if they were original data but without any privacy concerns. We demonstrate that synthetic EHR data can be easily accessed and analyzed and are amenable to machine learning analyses.Methods: We developed synthetic data from EHR data of 26,575 HF patients admitted to a single institution during the decade ending on 12/31/2018. Twenty-seven clinically-relevant features were synthesized and utilized in supervised deep learning and machine learning algorithms (i.e., deep neural networks [DNN], random forest [RF], and logistic regression [LR]) to explore their ability to predict 1-year mortality by five-fold cross validation methods. We conducted analyses leveraging features from prior to/at and after/at the time of HF diagnosis.Results: The area under the receiver operating curve (AUC) was used to evaluate the performance of the three models: the mean AUC was 0.80 for DNN, 0.72 for RF, and 0.74 for LR. Age, creatinine, body mass index, and blood pressure levels were especially important features in predicting death within 1-year among HF patients.Conclusions: Machine learning models have considerable potential to improve accuracy in mortality prediction, such that high-risk surgical intervention can be applied only in those patients who stand to benefit from it. Access to EHR-based synthetic data derivatives eliminates risk of exposure of EHR data, speeds time-to-insight, and facilitates data sharing. As more clinical, imaging, and contractile features with proven predictive capability are added to these models, the development of a clinical tool to assist in timing of intervention in surgical candidates may be possible.

Periprocedural myocardial injury is a prognostically important complication of percutaneous coronary intervention (PCI). However, it still remains unclear whether and how intensive atorvastatin therapy attenuates the unfavorable inflammatory responses of monocytes associated with PCI. The aim of the study was to investigate the impact of intensive atorvastatin therapy on inflammatory responses of monocytes in Chinese patients with unstable angina who received PCI in order to explore the potential anti-inflammatory mechanism. Ninety-six patients with unstable angina were randomly assigned to atorvastatin 80 mg (intensive) or atorvastatin 20 mg (conventional) treatment at a 1:1 ratio. Creatine kinase MB (CK-MB), cTnI, hs-CRP, and IL-6 were assessed, and circulating CD14(+) monocytes were simultaneously obtained using CD14 MicroBeads 2 h before and 24 h after PCI. Plasma levels of CK-MB, cTnI, hs-CRP, and IL-6 were higher in the conventional dose group versus those in the intensive dose group following PCI. Furthermore, intensive atorvastatin treatment markedly reduced the expressions and responses of Toll-like receptor 2 (TLR2), TLR4, and CCR2 of CD14(+) monocytes versus the conventional dose group and significantly increased the activated peroxisome-proliferator-activated receptor (PPAR) γ in the CD14(+) monocytes post-PCI. Notably, the changes in responses of TLR2, TLR4, and CCR2 of CD14(+) monocytes between the two groups were all reversed by PPARγ antagonist and augmented by PPARγ agonist. In conclusion, a single high (80 mg) loading dose of atorvastatin reduced the inflammatory response in Chinese patients with unstable angina following PCI. The anti-inflammatory role of intensive atorvastatin was possibly due to attenuation of inflammatory response in monocytes via PPARγ activation.

Mycoplasma pneumoniae pneumonia (MPP) is a common disease in children. Qingfei Tongluo formula (QTF) has been used for the treatment of MPP clinically, but the chemical constituents and mechanism involved remain unclear. This study aimed to analyze the main chemical constituents and to explore the possible mechanism of action associated with QTF treatment of MPP. Liquid chromatography-mass spectrometry was employed to identify the compounds contained in the QTF extract. A BALB/c mouse model of MP infection was established. After treatment with QTF (0.85 and 1.70 g/kg) for 3 days, hematoxylin and eosin staining was performed in lung tissues for histological examination. Inflammatory cytokines were detected by ELISA. Western blot analysis was used for detecting phosphorylated proteins involved in MAPK and nuclear factor (NF)-κB signaling pathways. In the mouse model, a large amount of pulmonary interstitial infiltration of lymphocytes and plasmacytes were seen as well as bronchus and vasodilation congestion. Following QTF treatment, inflammation was alleviated significantly compared with the model group. Inflammatory cytokines [interleukin (IL)-6, transforming growth factor-β1, IL-8, IL-1β and tumor necrosis factor-α] in bronchoalveolar lavage fluid were decreased dramatically. In addition, we found that QTF inhibited activation of phosphorylation of JNK, ERK and NF-κB. In conclusion, QTF alleviates MPP inflammation possibly via inhibitory activation of MAPK/NF-κB pathways, which can act as a new agent for MPP treatment.

Objective Liver cirrhosis is a leading cause of death and effects millions of people in the United States. Early mortality prediction among patients with cirrhosis might give healthcare providers more opportunity to effectively treat the condition. We hypothesized that laboratory test results and other related diagnoses would be associated with mortality in this population. Our another assumption was that a deep learning model could outperform the current Model for End Stage Liver disease (MELD) score in predicting mortality. Materials and methods We utilized electronic health record data from 34,575 patients with a diagnosis of cirrhosis from a large medical center to study associations with mortality. Three time-windows of mortality (365 days, 180 days and 90 days) and two cases with different number of variables (all 41 available variables and 4 variables in MELD-NA) were studied. Missing values were imputed using multiple imputation for continuous variables and mode for categorical variables. Deep learning and machine learning algorithms, i.e., deep neural networks (DNN), random forest (RF) and logistic regression (LR) were employed to study the associations between baseline features such as laboratory measurements and diagnoses for each time window by 5-fold cross validation method. Metrics such as area under the receiver operating curve (AUC), overall accuracy, sensitivity, and specificity were used to evaluate models. Results Performance of models comprising all variables outperformed those with 4 MELD-NA variables for all prediction cases and the DNN model outperformed the LR and RF models. For example, the DNN model achieved an AUC of 0.88, 0.86, and 0.85 for 90, 180, and 365-day mortality respectively as compared to the MELD score, which resulted in corresponding AUCs of 0.81, 0.79, and 0.76 for the same instances. The DNN and LR models had a significantly better f1 score compared to MELD at all time points examined. Conclusion Other variables such as alkaline phosphatase, alanine aminotransferase, and hemoglobin were also top informative features besides the 4 MELD-Na variables. Machine learning and deep learning models outperformed the current standard of risk prediction among patients with cirrhosis. Advanced informatics techniques showed promise for risk prediction in patients with cirrhosis.