Search

Labelling medical images is an arduous and costly task that necessitates clinical expertise and large numbers of qualified images. Insufficient samples can lead to underfitting during training and poor performance of supervised learning models. In this study, we aim to develop a SimCLR-based semi-supervised learning framework to classify colorectal neoplasia based on the NICE classification. First, the proposed framework was trained under self-supervised learning using a large unlabelled dataset; subsequently, it was fine-tuned on a limited labelled dataset based on the NICE classification. The model was evaluated on an independent dataset and compared with models based on supervised transfer learning and endoscopists using accuracy, Matthew's correlation coefficient (MCC), and Cohen's kappa. Finally, Grad-CAM and t-SNE were applied to visualize the models' interpretations. A ResNet-backboned SimCLR model (accuracy of 0.908, MCC of 0.862, and Cohen's kappa of 0.896) outperformed supervised transfer learning-based models (means: 0.803, 0.698, and 0.742) and junior endoscopists (0.816, 0.724, and 0.863), while performing only slightly worse than senior endoscopists (0.916, 0.875, and 0.944). Moreover, t-SNE showed a better clustering of ternary samples through self-supervised learning in SimCLR than through supervised transfer learning. Compared with traditional supervised learning, semi-supervised learning enables deep learning models to achieve improved performance with limited labelled endoscopic images. [ABSTRACT FROM AUTHOR]

Metabolic associated fatty liver disease (MAFLD) is the most common chronic liver disease worldwide, characterized by excess lipid deposition. Insulin resistance (IR) serves as a fundamental pathogenic factor in MAFLD. However, currently, there are no approved specific agents for its treatment. Farrerol, a novel compound with antioxidant and anti‐inflammatory effects, has garnered significant attention in recent years due to its hepatoprotective properties. Despite this, the precise underlying mechanisms of action remain unclear. In this study, a network pharmacology approach predicted protein tyrosine phosphatase non‐receptor type 1 (PTPN1) as a potential target for farrerol's action in the liver. Subsequently, the administration of farrerol improved insulin sensitivity and glucose tolerance in MAFLD mice. Furthermore, farrerol alleviated lipid accumulation by binding to PTPN1 and reducing the dephosphorylation of the insulin receptor (INSR) in HepG2 cells and MAFLD mice. Thus, the phosphoinositide 3‐kinase/serine/threonine‐protein kinases (PI3K/AKT) signalling pathway was active, leading to downstream protein reduction. Overall, the study demonstrates that farrerol alleviates insulin resistance and hepatic steatosis of MAFLD by targeting PTPN1. [ABSTRACT FROM AUTHOR]

Objective: The aim of this study was to investigate the relationship between circulating levels of B cell activating factor (BAFF) and the presence and severity of coronary artery disease (CAD) and acute myocardial infarction (AMI) in humans, as its biological functions in this context remain unclear. Methods: Serum BAFF levels were measured in a cohort of 723 patients undergoing angiography, including 204 patients without CAD (control group), 220 patients with stable CAD (CAD group), and 299 patients with AMI (AMI group). Logistic regression analyses were used to assess the association between BAFF and CAD or AMI. Results: Significantly elevated levels of BAFF were observed in patients with CAD and AMI compared to the control group. Furthermore, BAFF levels exhibited a positive correlation with the SYNTAX score (r = 0.3002, P < 0.0001) and the GRACE score (r = 0.5684, P < 0.0001). Logistic regression analysis demonstrated that increased BAFF levels were an independent risk factor for CAD (adjusted OR 1.305, 95% CI 1.078–1.580) and AMI (adjusted OR 2.874, 95% CI 1.708–4.838) after adjusting for confounding variables. Additionally, elevated BAFF levels were significantly associated with a high GRACE score (GRACE score 155 to 319, adjusted OR 4.297, 95% CI 1.841–10.030). BAFF exhibited a sensitivity of 75.0% and specificity of 71.4% in differentiating CAD patients with a high SYNTAX score, and a sensitivity of 75.5% and specificity of 72.8% in identifying AMI patients with a high GRACE score. Conclusion: Circulating BAFF levels serve as a valuable diagnostic marker for CAD and AMI. Elevated BAFF levels are associated with the presence and severity of these conditions, suggesting its potential as a clinically relevant biomarker in cardiovascular disease. [ABSTRACT FROM AUTHOR]

Background: Early drain removal (EDR) has been widely accepted, but not been routinely used in patients after pancreaticoduodenectomy (PD) and distal pancreatectomy (DP). This study aimed to evaluate the safety and benefits of EDR versus routine drain removal (RDR) after PD or DP. Methods: A systematic search was conducted on medical search engines from January 1, 2008 to November 1, 2023, for articles that compared EDR versus RDR after PD or DP. The primary outcome was clinically relevant postoperative pancreatic fistula (CR-POPF). Further analysis of studies including patients with low-drain fluid amylase (low-DFA) on postoperative day 1 and defining EDR timing as within 3 days was also performed. Results: Four randomized controlled trials (RCTs) and eleven non-RCTs with a total of 9465 patients were included in this analysis. For the primary outcome, the EDR group had a significantly lower rate of CR-POPF (OR 0.23; p < 0.001). For the secondary outcomes, a lower incidence was observed in delayed gastric emptying (OR 0.63, p = 0.02), Clavien–Dindo III–V complications (OR 0.48, p < 0.001), postoperative hemorrhage (OR 0.55, p = 0.02), reoperation (OR 0.57, p < 0.001), readmission (OR 0.70, p = 0.003) and length of stay (MD -2.04, p < 0.001) in EDR. Consistent outcomes were observed in the subgroup analysis of low-DFA patients and definite EDR timing, except for postoperative hemorrhage in EDR. Conclusion: EDR after PD or DP is beneficial and safe, reducing the incidence of CR-POPF and other postoperative complications. Further prospective studies and RCTs are required to validate this finding. [ABSTRACT FROM AUTHOR]

We aimed to develop and validate multimodal ICU patient prognosis models that combine clinical parameters data and chest X-ray (CXR) images. A total of 3798 subjects with clinical parameters and CXR images were extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database and an external hospital (the test set). The primary outcome was 30-day mortality after ICU admission. Automated machine learning (AutoML) and convolutional neural networks (CNNs) were used to construct single-modal models based on clinical parameters and CXR separately. An early fusion approach was used to integrate both modalities (clinical parameters and CXR) into a multimodal model named PrismICU. Compared to the single-modal models, i.e., the clinical parameter model (AUC = 0.80, F1-score = 0.43) and the CXR model (AUC = 0.76, F1-score = 0.45) and the scoring system APACHE II (AUC = 0.83, F1-score = 0.77), PrismICU (AUC = 0.95, F1 score = 0.95) showed improved performance in predicting the 30-day mortality in the validation set. In the test set, PrismICU (AUC = 0.82, F1-score = 0.61) was also better than the clinical parameters model (AUC = 0.72, F1-score = 0.50), CXR model (AUC = 0.71, F1-score = 0.36), and APACHE II (AUC = 0.62, F1-score = 0.50). PrismICU, which integrated clinical parameters data and CXR images, performed better than single-modal models and the existing scoring system. It supports the potential of multimodal models based on structured data and imaging in clinical management. [ABSTRACT FROM AUTHOR]

Background: Asymptomatic COVID-19 carriers with normal chest computed tomography (CT) scans have perpetuated the ongoing pandemic of this disease. This retrospective study aimed to use automated machine learning (AutoML) to develop a prediction model based on CT characteristics for the identification of asymptomatic carriers. Methods: Asymptomatic carriers were from Yangzhou Third People's Hospital from August 1st, 2020, to March 31st, 2021, and the control group included a healthy population from a nonepizootic area with two negative RT‒PCR results within 48 h. All CT images were preprocessed using MATLAB. Model development and validation were conducted in R with the H2O package. The models were built based on six algorithms, e.g., random forest and deep neural network (DNN), and a training set (n = 691). The models were improved by automatically adjusting hyperparameters for an internal validation set (n = 306). The performance of the obtained models was evaluated based on a dataset from Suzhou (n = 178) using the area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and F1 score. Results: A total of 1,175 images were preprocessed with high stability. Six models were developed, and the performance of the DNN model ranked first, with an AUC value of 0.898 for the test set. The sensitivity, specificity, PPV, NPV, F1 score and accuracy of the DNN model were 0.820, 0.854, 0.849, 0.826, 0.834 and 0.837, respectively. A plot of a local interpretable model-agnostic explanation demonstrated how different variables worked in identifying asymptomatic carriers. Conclusions: Our study demonstrates that AutoML models based on CT images can be used to identify asymptomatic carriers. The most promising model for clinical implementation is the DNN-algorithm-based model. [ABSTRACT FROM AUTHOR]

With the advances in endoscopic technologies and artificial intelligence, a large number of endoscopic imaging datasets have been made public to researchers around the world. This study aims to review and introduce these datasets. An extensive literature search was conducted to identify appropriate datasets in PubMed, and other targeted searches were conducted in GitHub, Kaggle, and Simula to identify datasets directly. We provided a brief introduction to each dataset and evaluated the characteristics of the datasets included. Moreover, two national datasets in progress were discussed. A total of 40 datasets of endoscopic images were included, of which 34 were accessible for use. Basic and detailed information on each dataset was reported. Of all the datasets, 16 focus on polyps, and 6 focus on small bowel lesions. Most datasets (n = 16) were constructed by colonoscopy only, followed by normal gastrointestinal endoscopy and capsule endoscopy (n = 9). This review may facilitate the usage of public dataset resources in endoscopic research. [ABSTRACT FROM AUTHOR]

Background: Chronic kidney disease (CKD) accelerates atherosclerosis, but the mechanisms remain unclear. Tyrosine sulfation has been recognized as a key post-translational modification (PTM) in regulation of various cellular processes, and the sulfated adhesion molecules and chemokine receptors have been shown to participate in the pathogenesis of atherosclerosis via enhancement of monocyte/macrophage function. The levels of inorganic sulfate, the essential substrate for the sulfation reaction, are dramatically increased in patients with CKD, which indicates a change of sulfation status in CKD patients. Thus, in the present study, we detected the sulfation status in CKD patients and probed into the impact of sulfation on CKD-related atherosclerosis by targeting tyrosine sulfation function. Results: PBMCs from individuals with CKD showed higher amounts of total sulfotyrosine and tyrosylprotein sulfotransferase (TPST) type 1 and 2 protein levels. The plasma level of O-sulfotyrosine, the metabolic end product of tyrosine sulfation, increased significantly in CKD patients. Statistically, O-sulfotyrosine and the coronary atherosclerosis severity SYNTAX score positively correlated. Mechanically, more sulfate-positive nucleated cells in peripheral blood and more abundant infiltration of sulfated macrophages in deteriorated vascular plaques in CKD ApoE null mice were noted. Knockout of TPST1 and TPST2 decreased atherosclerosis and peritoneal macrophage adherence and migration in CKD condition. The sulfation of the chemokine receptors, CCR2 and CCR5, was increased in PBMCs from CKD patients. Conclusions: CKD is associated with increased sulfation status. Increased sulfation contributes to monocyte/macrophage activation and might be involved in CKD-related atherosclerosis. Inhibition of sulfation may suppress CKD-related atherosclerosis and is worthy of further study. [ABSTRACT FROM AUTHOR]

Background: Recurrence of common bile duct stones (CBDs) commonly happens after endoscopic retrograde cholangiopancreatography (ERCP). The clinical prediction models for the recurrence of CBDs after ERCP are lacking. Aims: We aim to develop high-performance prediction models for the recurrence of CBDS after ERCP treatment using automated machine learning (AutoML) and to assess the AutoML models versus the traditional regression models. Methods: 473 patients with CBDs undergoing ERCP were recruited in the single-center retrospective cohort study. Samples were divided into Training Set (65%) and Validation Set (35%) randomly. Three modeling approaches, including fully automated machine learning (Fully automated), semi-automated machine learning (Semi-automated), and traditional regression were applied to fit prediction models. Models' discrimination, calibration, and clinical benefits were examined. The Shapley additive explanations (SHAP), partial dependence plot (PDP), and SHAP local explanation (SHAPLE) were proposed for the interpretation of the best model. Results: The area under roc curve (AUROC) of semi-automated gradient boost machine (GBM) model was 0.749 in Validation Set, better than the other fully/semi-automated models and the traditional regression models (highest AUROC = 0.736). The calibration and clinical application of AutoML models were adequate. Through the SHAP-PDP-SHAPLE pipeline, the roles of key variables of the semi-automated GBM model were visualized. Lastly, the best model was deployed online for clinical practitioners. Conclusion: The GBM model based on semi-AutoML is an optimal model to predict the recurrence of CBDs after ERCP treatment. In comparison with traditional regressions, AutoML algorithms present significant strengths in modeling, which show promise in future clinical practices. [ABSTRACT FROM AUTHOR]

Novel coronavirus disease 2019 (COVID-19) has rapidly spread throughout the world; however, it is difficult for clinicians to make early diagnoses. This study is to evaluate the feasibility of using deep learning (DL) models to identify asymptomatic COVID-19 patients based on chest CT images. In this retrospective study, six DL models (Xception, NASNet, ResNet, EfficientNet, ViT, and Swin), based on convolutional neural networks (CNNs) or transformer architectures, were trained to identify asymptomatic patients with COVID-19 on chest CT images. Data from Yangzhou were randomly split into a training set (n = 2140) and an internal-validation set (n = 360). Data from Suzhou was the external-test set (n = 200). Model performance was assessed by the metrics accuracy, recall, and specificity and was compared with the assessments of two radiologists. A total of 2700 chest CT images were collected in this study. In the validation dataset, the Swin model achieved the highest accuracy of 0.994, followed by the EfficientNet model (0.954). The recall and the precision of the Swin model were 0.989 and 1.000, respectively. In the test dataset, the Swin model was still the best and achieved the highest accuracy (0.980). All the DL models performed remarkably better than the two experts. Last, the time on the test set diagnosis spent by two experts—42 min, 17 s (junior); and 29 min, 43 s (senior)—was significantly higher than those of the DL models (all below 2 min). This study evaluated the feasibility of multiple DL models in distinguishing asymptomatic patients with COVID-19 from healthy subjects on chest CT images. It found that a transformer-based model, the Swin model, performed best. [ABSTRACT FROM AUTHOR]

Personalized medicine is a dynamic and rapidly developing approach in clinical practice that involves utilizing innovative technologies to make decisions in the screening, prevention, diagnosis, and treatment of disease. Chronic liver disease is a progressive deterioration of hepatic functions and a continuous process of inflammation, destruction, and regeneration of liver parenchyma, resulting in fibrosis and cirrhosis. Additionally, personalized medicine helps prevent diseases from occurring by providing patients with earlier intervention and treatment opportunities, thereby reducing the risk and recurrence rate of diseases. [Extracted from the article]

This study is to evaluate the feasibility of deep learning (DL) models in the multiclassification of reflux esophagitis (RE) endoscopic images, according to the Los Angeles (LA) classification for the first time. The images were divided into three groups, namely, normal, LA classification A + B, and LA C + D. The images from the HyperKvasir dataset and Suzhou hospital were divided into the training and validation datasets as a ratio of 4 : 1, while the images from Jintan hospital were the independent test set. The CNNs- or Transformer-architectures models (MobileNet, ResNet, Xception, EfficientNet, ViT, and ConvMixer) were transfer learning via Keras. The visualization of the models was proposed using Gradient-weighted Class Activation Mapping (Grad-CAM). Both in the validation set and the test set, the EfficientNet model showed the best performance as follows: accuracy (0.962 and 0.957), recall for LA A + B (0.970 and 0.925) and LA C + D (0.922 and 0.930), Marco-recall (0.946 and 0.928), Matthew's correlation coefficient (0.936 and 0.884), and Cohen's kappa (0.910 and 0.850), which was better than the other models and the endoscopists. According to the EfficientNet model, the Grad-CAM was plotted and highlighted the target lesions on the original images. This study developed a series of DL-based computer vision models with the interpretable Grad-CAM to evaluate the feasibility in the multiclassification of RE endoscopic images. It firstly suggests that DL-based classifiers show promise in the endoscopic diagnosis of esophagitis. [ABSTRACT FROM AUTHOR]

Objectives: The B cell activating factor (BAFF) is a B cell survival factor involved in atherosclerosis and ischemia-reperfusion (IR) injury. This study sought to investigate whether BAFF is a potential predictor of poor outcomes in patients with ST-segment elevation myocardial infarction (STEMI). Methods: We prospectively enrolled 299 patients with STEMI, and serum levels of BAFF were measured. All subjects were followed for three years. The primary endpoint was major adverse cardiovascular events (MACEs), including cardiovascular death, nonfatal reinfarction, hospitalization for heart failure (HF), and stroke. Multivariable Cox proportional hazards models were constructed to analyze the predictive value of BAFF for MACEs. Results: In multivariate analysis, BAFF was independently associated with risk of MACEs (adjusted HR 1.525, 95% CI 1.085–2.145; p = 0.015) and cardiovascular death (adjusted hazard ratio [HR] 3.632, 95% confidence interval [CI] 1.132–11.650, p = 0.030) after adjustment for traditional risk factors. Kaplan-Meier survival curves demonstrated that patients with BAFF levels above the cut-off value (1.46 ng/mL) were more likely to have MACEs (log-rank p < 0.0001) and cardiovascular death (log-rank p < 0.0001). In subgroup analysis, the impact of high BAFF on MACEs development was stronger in patients without dyslipidemia. Furthermore, the C-statistic and Integrated Discrimination Improvement (IDI) values for MACEs were improved with BAFF as an independent risk factor or when combined with cardiac troponin I. Conclusions: This study suggests that higher BAFF levels in the acute phase are an independent predictor of the incidence of MACEs in patients with STEMI. [ABSTRACT FROM AUTHOR]

Esophageal variceal (EV) bleeding is a severe medical emergency related to cirrhosis. Early identification of cirrhotic patients with at a high risk of EV bleeding is key to improving outcomes and optimizing medical resources. This study aimed to evaluate the feasibility of automated multimodal machine learning (MMML) for predicting EV bleeding by integrating endoscopic images and clinical structured data. This study mainly includes three steps: step 1, developing deep learning (DL) models using EV images by 12-month bleeding on TensorFlow (backbones include ResNet, Xception, EfficientNet, ViT and ConvMixer); step 2, training and internally validating MMML models integrating clinical structured data and DL model outputs to predict 12-month EV bleeding on an H2O-automated machine learning platform (algorithms include DL, XGBoost, GLM, GBM, RF, and stacking); and step 3, externally testing MMML models. Furthermore, existing clinical indices, e.g., the MELD score, Child‒Pugh score, APRI, and FIB-4, were also examined. Five DL models were transfer learning to the binary classification of EV endoscopic images at admission based on the occurrence or absence of bleeding events during the 12-month follow-up. An EfficientNet model achieved the highest accuracy of 0.868 in the validation set. Then, a series of MMML models, integrating clinical structured data and the output of the EfficientNet model, were automatedly trained to predict 12-month EV bleeding. A stacking model showed the highest accuracy (0.932), sensitivity (0.952), and F1-score (0.879) in the test dataset, which was also better than the existing indices. This study is the first to evaluate the feasibility of automated MMML in predicting 12-month EV bleeding based on endoscopic images and clinical variables. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the feasibility of automated machine learning (AutoML) in predicting 30-day mortality in non-cholestatic cirrhosis. Methods: A total of 932 cirrhotic patients were included from the First Affiliated Hospital of Soochow University between 2014 and 2020. Participants were divided into training and validation datasets at a ratio of 8.5:1.5. Models were developed on the H2O AutoML platform in the training dataset, and then were evaluated in the validation dataset by area under receiver operating characteristic curves (AUC). The best AutoML model was interpreted by SHapley Additive exPlanation (SHAP) Plot, Partial Dependence Plots (PDP), and Local Interpretable Model Agnostic Explanation (LIME). Results: The model, based on the extreme gradient boosting (XGBoost) algorithm, performed better (AUC 0.888) than the other AutoML models (logistic regression 0.673, gradient boost machine 0.886, random forest 0.866, deep learning 0.830, stacking 0.850), as well as the existing scorings (the model of end-stage liver disease [MELD] score 0.778, MELD-Na score 0.782, and albumin-bilirubin [ALBI] score 0.662). The most key variable in the XGBoost model was high-density lipoprotein cholesterol, followed by creatinine, white blood cell count, international normalized ratio, etc. Conclusion: The AutoML model based on the XGBoost algorithm presented better performance than the existing scoring systems for predicting 30-day mortality in patients with non-cholestatic cirrhosis. It shows the promise of AutoML in its future medical application. [ABSTRACT FROM AUTHOR]

Simple Summary: Surgery is the main treatment to cure pancreatic cancer (PC). However, the 5-year survival rate of surgical resection is only 10–20%. The aim of our study was to develop a prediction model with the novel machine learning algorithm random survival forest (RSF) and to offer easy-to-use prediction tools, including risk stratification and individual prognosis. The study would benefit patients and physicians in postoperative management and facilitate personalized medicine. Accurate prediction for the prognosis of patients with pancreatic cancer (PC) is a emerge task nowadays. We aimed to develop survival models for postoperative PC patients, based on a novel algorithm, random survival forest (RSF), traditional Cox regression and neural networks (Deepsurv), using the Surveillance, Epidemiology, and End Results Program (SEER) database. A total of 3988 patients were included in this study. Eight clinicopathological features were selected using least absolute shrinkage and selection operator (LASSO) regression analysis and were utilized to develop the RSF model. The model was evaluated based on three dimensions: discrimination, calibration, and clinical benefit. It found that the RSF model predicted the cancer-specific survival (CSS) of the postoperative PC patients with a c-index of 0.723, which was higher than the models built by Cox regression (0.670) and Deepsurv (0.700). The Brier scores at 1, 3, and 5 years (0.188, 0.177, and 0.131) of the RSF model demonstrated the model's favorable calibration and the decision curve analysis illustrated the model's value of clinical implement. Moreover, the roles of the key variables were visualized in the Shapley Additive Explanations plotting. Lastly, the prediction model demonstrates value in risk stratification and individual prognosis. In this study, a high-performance prediction model for PC postoperative prognosis was developed, based on RSF The model presented significant strengths in the risk stratification and individual prognosis prediction. [ABSTRACT FROM AUTHOR]

Background: Machine learning (ML) algorithms are widely applied in building models of medicine due to their powerful studying and generalizing ability. This study aims to explore different ML models for early identification of severe acute pancreatitis (SAP) among patients hospitalized for acute pancreatitis. Methods: This retrospective study enrolled patients with acute pancreatitis (AP) from multiple centers. Data from the First Affiliated Hospital and Changshu No. 1 Hospital of Soochow University were adopted for training and internal validation, and data from the Second Affiliated Hospital of Soochow University were adopted for external validation from January 2017 to December 2021. The diagnosis of AP and SAP was based on the 2012 revised Atlanta classification of acute pancreatitis. Models were built using traditional logistic regression (LR) and automated machine learning (AutoML) analysis with five types of algorithms. The performance of models was evaluated by the receiver operating characteristic (ROC) curve, the calibration curve, and the decision curve analysis (DCA) based on LR and feature importance, SHapley Additive exPlanation (SHAP) Plot, and Local Interpretable Model Agnostic Explanation (LIME) based on AutoML. Results: A total of 1,012 patients were included in this study to develop the AutoML models in the training/validation dataset. An independent dataset of 212 patients was used to test the models. The model developed by the gradient boost machine (GBM) outperformed other models with an area under the ROC curve (AUC) of 0.937 in the validation set and an AUC of 0.945 in the test set. Furthermore, the GBM model achieved the highest sensitivity value of 0.583 among these AutoML models. The model developed by eXtreme Gradient Boosting (XGBoost) achieved the highest specificity value of 0.980 and the highest accuracy of 0.958 in the test set. Conclusions: The AutoML model based on the GBM algorithm for early prediction of SAP showed evident clinical practicability. [ABSTRACT FROM AUTHOR]

Background This study aims to explore a deep learning (DL) algorithm for developing a prognostic model and perform survival analyses in SBT patients. Methods The demographic and clinical features of patients with SBTs were extracted from the Surveillance, Epidemiology and End Results (SEER) database. We randomly split the samples into the training set and the validation set at 7:3. Cox proportional hazards (Cox-PH) analysis and the DeepSurv algorithm were used to develop models. The performance of the Cox-PH and DeepSurv models was evaluated using receiver operating characteristic curves, calibration curves, C-statistics and decision-curve analysis (DCA). A Kaplan–Meier (K–M) survival analysis was performed for further explanation on prognostic effect of the Cox-PH model. Results The multivariate analysis demonstrated that seven variables were associated with cancer-specific survival (CSS) (all p < 0.05). The DeepSurv model showed better performance than the Cox-PH model (C-index: 0.871 vs. 0.866). The calibration curves and DCA revealed that the two models had good discrimination and calibration. Moreover, patients with ileac malignancy and N2 stage disease were not responding to surgery according to the K–M analysis. Conclusions This study reported a DeepSurv model that performed well in CSS in SBT patients. It might offer insights into future research to explore more DL algorithms in cohort studies. [ABSTRACT FROM AUTHOR]

Chronic kidney disease (CKD) is associated with an increased risk of the progression of coronary artery disease (CAD). However, there are few data on the relationship between CAD severity and the duration of CKD. This study assessed the predictive value of the duration of kidney dysfunction in CKD patients with CAD severity. In 145 patients (63.4% male, n = 92; mean age, 68.8 ± 12.8 years) with CKD, severity of CAD was assessed by coronary angiography and quantified by SYNTAX scores, and duration of kidney dysfunction was either assessed by checking historical biochemical parameters of individuals or was based on enquiries. Patients with high SYNTAX scores (≥ 22) had a greater prevalence of cardiovascular risk factors including age, gender, history of heart failure and smoking. In CKD patients, SYNTAX scores were positively correlated to duration of CKD and serum uric acid (UA), and negatively correlated to high-density lipoprotein-cholesterol (HDL-C) and ApoA1 levels. Univariate binary logistic regression and multivariate logistic analyses showed that SYNTAX scores correlated significantly with CKD duration, UA, and HDL-C. Receiver-operating characteristic analysis was used to explore a time point when coronary angiography application was economical and effective and yielded a Youden index of 6.5 years. Together, our results demonstrated that the duration of kidney dysfunction was an independent correlate of the severity of CAD in patients with CKD. Our findings suggest that coronary angiography should be considered for CKD patients with renal insufficiency having lasted for more than 6.5 years. [ABSTRACT FROM AUTHOR]

Background and Aims Liver expressed antimicrobial peptide 2 (LEAP2) is recently identified as a regulator in energy metabolism. This study aims to 1) investigate the role of leap2 in hepatic steatosis in C57BL/6 mice; 2) evaluate the association between circulating LEAP2 levels and liver fat contents in a hospital based case-control study. Methods The rodent experiment: western blotting and qPCR were performed to evaluate leap2 levels, lipid metabolism pathways and insulin signaling. shRNA was used to knockdown leap2. The clinical study: commercial ELISA kits were used to measure circulating LEAP2 levels (validated by western blotting). Liver fat content was estimated using MRI-derived proton density fat fraction and FibroScan-derived controlled attenuation parameter. Results The rodent experiment found the hepatic expression and secreted levels of leap2 were increased in mice with diet-induced steatosis. Leap2 knockdown ameliorated steatosis via lipolytic/lipogenic pathway and improved insulin sensitivity via IRS/AKT signaling. The clinical study reported increased circulating levels of LEAP2 in the subjects with steatosis. Moreover, LEAP2 correlated positively with age, body mass index, waist-to-hip ratio, liver fat content, fasting insulin and HOMA-IR, whereas inversely with acyl-ghrelin. Furthermore, the circulating levels of LEAP2 are dependent on liver fat content, acyl-ghrelin and fasting glucose. Lastly, circulating LEAP2 is an independent predictor of NAFLD. Conclusions The study suggests LEAP2 is associated with hepatic steatosis, which may involve lipolytic/lipogenic pathway and insulin signaling. [ABSTRACT FROM AUTHOR]

Although the drug-eluting stent (DES) has become the standard for percutaneous coronary intervention (PCI)-based revascularization, concerns remain regarding the use of DES, mainly due to its permanent rigid constraint to vessels. A drug-eluting bioresorbable stent (BRS) was thus developed as an alternative to DES, which can be absorbed entirely after its therapeutic period. Magnesium (Mg)-based BRSs have attracted a great deal of attention due to their suitable mechanical properties, innovative chemical features, and well-proven biocompatibility. However, the primary disadvantage of Mg-based BRSs is the rapid degradation rate, resulting in the early loss of structural support long before the recovery of vascular function. Recently, a new type of patented Mg–Nd–Zn-Zr alloy (JDBM) was developed at Shanghai Jiao Tong University to reduce the degradation rate compared to commercial Mg alloys. In the present investigation, a poly(d,l-lactic acid)-coated and rapamycin eluting (PDLLA/RAPA) JDBM BRS was prepared, and its biosafety and efficacy for coronary artery stenosis were evaluated via in vitro and in vivo experiments. The degree of smooth muscle cell adhesion to the PDLLA/RAPA coated alloy and the rapamycin pharmacokinetics of JDBM BRS were first assessed in vitro. JDBM BRS and commercial DES FIREHAWK were then implanted in the coronary arteries of a porcine model. Neointimal hyperplasia was evaluated at 30, 90, and 180 days, and re-endothelialization was evaluated at 30 days. Furthermore, Micro-CT and optical coherence tomography (OCT) analyses were performed 180 days after stent implantation to evaluate the technical feasibility, biocompatibility, and degradation characteristics of JDBM BRS in vivo. The results show the ability of a PDLLA/RAPA coated JDBM to inhibit smooth muscle cell adhesion and moderate the drug release rate of JDBM BRS in vitro. In vivo, low local and systemic risks of JDBM BRS were demonstrated in the porcine model, with preserved mechanical integrity after 6 months of implantation. We also showed that this novel BRS was associated with a similar efficacy profile compared with standard DES and high anti-restenosis performance. These findings may confer long term advantages for using this BRS over a traditional DES. [ABSTRACT FROM AUTHOR]

Background: Gastroesophageal reflux disease (GERD) is a common gastrointestinal disorder. Proton pump inhibitors (PPIs) are first-line drugs for GERD. For those who fail to respond to PPIs, adding prokinetics to PPIs is recommended and several trials have been conducted to evaluate the efficacy of prokinetic–PPI combination therapy. Methods: A systematic literature search was performed using PubMed and the Cochrane Library databases before February 2019 for randomized controlled trials (RCTs), which compared the efficacy of prokinetics plus PPI treatment with that of PPI monotherapy. Relevant studies were examined and data were extracted independently by two investigators. The risk ratios (RRs) with 95% CIs were used to evaluate the responder rate, and standard mean differences (SMDs) or mean differences (MDs) with 95% CIs were used for symptom score changes. Statistical heterogeneity was evaluated by the I2 statistic. Either a fixed-effect or a random-effect model was established for calculating the pooled data. Results: A total of 14 studies, comprising 1,437 patients were ultimately included in the meta-analysis. The pooled analysis showed that compared to PPI monotherapy, addition of prokinetics to PPI did not elevate the rate of endoscopic responders (RR = 0.996, 95% CI 0.929 − 1.068, p = 0.917), but improved symptom response (RR = 1.185, 95% CI 1.042 − 1.348, p = 0.010). Additionally, the combined therapy achieved a greater symptom relief than monotherapy both in FSSG and GERD-Q subgroups (MD = − 2.978, 95% CI − 3.319 to − 2.638, p < 0.001; MD = − 0.723, 95% CI − 0.968 to − 0.478, p < 0.001). Conclusions: Adding prokinetics to PPIs achieves symptomatic improvement compared to PPI monotherapy, thus can enhance life quality of GERD patients. However, the combined treatment seems to have no significant effect on mucosal healing. [ABSTRACT FROM AUTHOR]

Background: Mitsugumin 53 or Tripartite motif 72 (MG53/TRIM72), a myokine/cardiokine belonging to the tripartite motif family, can protect the heart from ischemic injury and regulate lipid metabolism in rodents. However, its biological function in humans remains unclear. This study sought to investigate the relationship between circulating MG53 levels and coronary artery disease (CAD). Methods: The concentration of MG53 was measured by enzyme-linked immunosorbent assay (ELISA) in serum samples from 639 patients who underwent angiography, including 205 controls, 222 patients with stable CAD, and 212 patients with acute myocardial infarction (AMI). Logistic and linear regression analyses were used to analyze the relationship between MG53 and CAD. Results: MG53 levels were increased in patients with stable CAD and were highest in patients with AMI. Additionally, patients with comorbidities, such as chronic kidney disease (CKD) and diabetes also had a higher concentration of MG53. We found that MG53 is a significant diagnostic marker of CAD and AMI, as analyzed by logistic regression models. Multivariate linear regression models revealed that serum MG53 was significantly corelated positively with SYNTAX scores. Global Registry of Acute Coronary Events (GRACE) scores also correlated with serum MG53 levels, indicating that MG53 levels were associated with the severity of CAD and AMI after adjusting for multiple risk factors and clinical biomarkers. Conclusion: MG53 is a valuable diagnostic marker whose serum levels correlate with the presence and severity of stable CAD and AMI, and may represent a novel biomarker for diagnosing CAD and indicating the severity of CAD. [ABSTRACT FROM AUTHOR]

Large-scale photovoltaic (PV) generation's uncertainties significantly affect power system planning and operations. Thus, a stochastic PV power simulation method, which can accurately capture such uncertainties, is urgently needed to provide a foundation for further uncertain studies on power systems with PV stations. This paper proposes a nested Markov chain Monte Carlo (MCMC) method incorporated with atmospheric process decomposition (APD) for PV power simulation. First, an imaginary clear-sky model matching the local actual clear-sky atmosphere is designed to convert PV power to an attenuation coefficient (AC). Second, a nested AC Markov chain (MC) is proposed based on APD to distinguish the macroscale and meso-microscale ACs while consider their coupling relationship. Third, an improved MCMC method is developed to simulate this MC's each layer in a nested manner for stochastically synthesizing AC time series; this method can improve synthesizing accuracy thanks to the adoption of an optimal state number decision-making model to ensure the MCMC model's quality and a 3D transition probability matrix to capture the dynamics of transition probabilities with respect to state duration. Finally, synthetic AC time series are reconverted to PV power time series. The results validate the proposed method's accuracy over previous ones in reproducing PV power characteristics. [ABSTRACT FROM AUTHOR]

Background: The impact of primary tumor resection (PTR) on the prognosis of unresectable metastatic colorectal cancer (mCRC) patients remains debatable. We aimed to develop several prognostic nomograms which could be useful in predicting whether patients might benefit from PTR or not. Methods: Patients diagnosed as mCRC without resected metastasis were identified from the Surveillance Epidemiology and End Results database and randomly assigned into two groups: a training cohort (6369 patients) and a validation cohort (2774 patients). Univariate and multivariable Cox analyses were performed to identify the independent predictors and construct nomograms that could independently predict the overall survival (OS) of unresectable mCRC patients in PTR and non-PTR groups, respectively. The performance of these nomograms was assessed by the concordance index (C-index), calibration curves, and decision curve analysis (DCA). Results: Based on the result of univariate and multivariable Cox analyses, two nomograms were respectively constructed to predict the 1-year OS rates of unresectable mCRC patients when receiving PTR and not. The first one included age, gender, tumor grade, proximal colon, N stage, CEA, chemotherapy, radiotherapy, histology type, brain metastasis, liver metastasis, lung metastasis, and bone metastasis. The second nomogram included age, race, tumor grade, primary site, CEA, chemotherapy, brain metastasis, and bone metastasis. These nomograms showed favorable sensitivity with the C-index range of 0.700–0.725. The calibration curves and DCAs also exhibited adequate fit and ideal net benefits in prognosis prediction and clinical application. Conclusions: These practical prognosis nomograms could assist clinicians in making appropriate treatment decisions to effectively manage the disease. [ABSTRACT FROM AUTHOR]

BACKGROUND: Recent evidence support that netrin-1 involves in colorectal carcinogenesis. OBJECTIVE: This study was to evaluate the performance of serum netrin-1 for detection of colorectal cancer (CRC) in both clinical/screening sets. METHODS: A total of 115 consecutive patients with CRC and matched healthy controls were included in Clinical Set. Fifty subjects with CRC, 50 subjects with advanced adenoma (AA), and 150 matched control participants free of neoplasia were included in Screening Set. RESULTS: In Clinical set, subjects with CRC presented higher levels of serum netrin-1 (513.9 ± 22.6 pg/mL) than controls (347.8 ± 20.3 pg/mL, p < 0.0001). Similar in Screening set, serum netrin-1 was higher in CRC (644.5 ± 37.0 pg/mL, both p < 0.0001), compared with controls (407.7 ± 14.8 pg/mL) and AA (416.5 ± 18.5 pg/mL). However, there was no difference between controls and AA (p = 0.752). Compared with the low netrin-1 group, the high group presented increased risk of CRC (Clinical set: OR = 4.300, p < 0.001; Screening set: OR = 7.731, p < 0.001). ROC curve of netrin-1 was developed to detect CRC (Clinical set: AUC 0.703; Screening set: AUC 0.759). CONCLUSIONS: It suggests netrin-1 as a potential biomarker for CRC detection. [ABSTRACT FROM AUTHOR]

Background: Diabetes mellitus (DM) plays an important role in restenosis and late in-stent thrombosis (ST). The current study using optical coherence tomography (OCT) aims to compare target lesion neointima in patients with or without diabetes after zotarolimus-eluting stent (ZES) treatment.Methods: OCT images of 90,212 struts and quantitative coronary angiography (QCA) in 62 patients (32 with DM and 30 without DM) with 69 de novo coronary lesions (34 DM and 35 non-DM) both after ZES implantation and 12 ± 1 month angiographic follow-up were recorded. Patient characteristics, lesion characteristics, clinical outcomes, and OCT findings including neointimal thickness, coverage, malapposition, and intimal morphology were analyzed.Results: Baseline patient characteristics and lesion characteristics data were similar between the two groups. Higher neointimal thickness (0.14 ± 0.09 mm vs. 0.09 ± 0.04 mm, p = 0.021), more neovascularization (3.03 ± 6.24 vs. 0.52 ± 1.87, p = 0.017) and higher incidence of layered signal pattern (12.19 ± 19.91% vs. 4.28 ± 9.02%, p = 0.049) were observed in diabetic lesions comparing with non-diabetic lesions. No differences were found in malapposition, uncovered percentage, and thrombus between the two groups (all p > 0.05). Occurrence of clinical adverse events was also similar during the follow-up period (p > 0.05).Conclusion: Although more neointimal proliferation and more neovascularization were found in diabetic coronary lesions when compared with non-diabetic lesions, treatment with ZES showed similar stent malapposition rate at 1-year follow-up. The data indicated that ZES treatment could possibly be effective in treating diabetic coronary lesions.Trial Registration: ClinicalTrials.gov identifier, NCT01747356. [ABSTRACT FROM AUTHOR]

Dexmedetomidine (DEX), a highly specific and selective α2 adrenergic receptor agonist, has been demonstrated to possess potential cardioprotective effects. However, the mechanisms underlying this process remain to be fully illuminated. In the present study, a myocardial infarction (MI) animal model was generated by permanently ligating the left anterior descending coronary artery in mice. Cardiac function and collagen content were evaluated by transthoracic echocardiography and picrosirius red staining, respectively. Apoptosis was determined by the relative expression levels of Bax and Bcl-2 and the myocardial caspase-3 activity. Additionally, nicotinamide adenine dinucleotide phosphate oxidase (NOX)-derived oxidative stress was evaluated by the relative expression of Nox2 and Nox4, along with the myocardial contents of malondialdehyde (MDA) and superoxide dismutase (SOD) activity. It was demonstrated that intraperitoneal DEX treatment (20 µg/kg/day) improved the systolic function of the left ventricle, and decreased the fibrotic changes in post-myocardial infarction mice, which was paralleled by a decrease in the levels of apoptosis. Subsequent experiments indicated that the restoration of redox signaling was achieved by DEX administration, and the over-activation of NOXs, including Nox2 and Nox4, was markedly inhibited. In conclusion, this present study suggested that DEX was cardioprotective and limited the excess production of NOX-derived ROS in ischemic heart disease, implying that DEX is a promising novel drug, especially for patients who have suffered MI. [ABSTRACT FROM AUTHOR]

Due to the inherent uncertainties of wind power, its large‐scale integration strongly impacts the planning and operation of power systems. To investigate these impacts, a stochastic model is required to more accurately capture the wind power's characteristics. This study proposes an improved Markov chain (MC)‐based time series (TS) modelling method for the stochastic generation of synthetic wind power TS. First, a self‐adaptive state division strategy is proposed to objectively classify historical data into several typical states. This strategy combines a state optimisation clustering model with a random‐variable‐modelling‐oriented filter parameter optimisation method. Then, a three‐dimensional state transition probability matrix (STPM) is proposed and constructed to generate synthetic wind power state TS. In contrast to the previous STPMs, the proposed STPM can capture the changing pattern of the transition probability against the state duration. Finally, the fluctuation quantity and noise are separately and sequentially added to the generated state TS, as an improvement over previous fluctuation characteristic addition methods, to obtain the final synthetic wind power TS. The results show that the proposed method outperforms previous MC‐based TS modelling methods in reproducing historical characteristics, such as the transition and fluctuation characteristics, and does not increase the STPM construction algorithm's time complexity. [ABSTRACT FROM AUTHOR]

Probabilistic optimal power flow (POPF) is an important tool in power system planning and operation. One limitation of conventional POPF is that only the probability information of random variables is obtained as a reference for related analyses. Frequency and duration information often plays an important role in power system assessment. In this study, a frequency and duration analysis method for POPF with wind farms (WFs) is proposed, which is based on Markov chains by improving the traditional probability‐frequency distribution function (PFDF) method. The main advantage of the proposed method is that highly accurate solutions can be obtained with less computation. Random input variables, including intermittent loads and WF power outputs associated with both wind speed uncertainties and wind turbine (WT) failures, are modeled using the corresponding PFDFs. With the proposed method, not only probability information but also frequency and duration information of random POPF outputs are efficiently and analytically computed through the operations of PFDFs of random inputs. Moreover, an optimization method for determining the clustering number of random states is proposed to improve the credibility of stochastic process modeling of Markov‐chain‐based random variables. The test on the modified IEEE‐RTS79 system with WFs demonstrates the rapidity and validity of the proposed method. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

This paper proposes a frequency and duration method for the adequacy assessment of generation systems with wind farms (WFs). Based on Markov chains, reliability models for WFs, conventional generators, and load are developed and represented as the corresponding probability-frequency distribution functions (PFDFs). The failures of wind turbines and the uncertainties and correlations of wind speeds are considered and incorporated into the reliability modeling of WFs. With the proposed method, the implementation of the reliability modeling of large WFs becomes simple. Moreover, for a generation system with WFs, not only probability-based reliability indexes, such as the loss of load probability and the expected energy not supplied, but also frequency-based reliability indexes, such as the loss of load frequency and the loss of load duration, can be calculated analytically and efficiently using the PFDFs. An optimal decision-making model for determining the clustering number of random states is proposed to improve the credibility of stochastic process modeling of Markov-chain-based random variables. The tests on two modified IEEE-RTS79 grids with WFs demonstrate the rapidity and validity of the proposed method. [ABSTRACT FROM AUTHOR]

Background: In recent decades, the patterns and trends of gastrointestinal (GI) cancer epidemics in Chinese population have been changing.Aims: To present the epidemiological trends and geographic distributions of four major GI cancers (esophageal cancer, stomach cancer, liver cancer and colorectal cancer) in China from 2010 to 2014.Methods: It used standardized data extracted from the National Central Cancer Registry database.Results: The age-standardized incidence rates (ASIR) of esophageal cancer decreased from 16.7 to 12.2 per 100,000 and the age-standardized mortality rates (ASMR) decreased from 12.0 to 8.8 per 100,000. The ASIR and the ASMR of stomach cancer dropped from 23.7 to 19.5 per 100,000 and from 16.6 to 13.3 per 100,000. The ASIR of liver cancer fell from 21.4 to 17.8 per 100,000 and its ASMR fell from 18.4 per 100,000 to 15.3 per 100,000. The ASIR of colorectal cancer increased from 16.1 to 17.5 per 100,000, whereas the ASMR fluctuated between 7.6 and 7.9 per 100,000. Moreover, the incidence and mortality of each cancer differed between males and females, urban and rural residence, as well as various regions.Conclusion: From 2010 to 2014, esophageal cancer, stomach cancer and liver cancer showed downward trend, while the ASIR of colorectal cancer slightly rose and its ASMR presented stable. [ABSTRACT FROM AUTHOR]

Background: Alpha-fetoprotein (AFP) is the most commonly applied biomarker for diagnosis of hepatocellular carcinoma (HCC), but the low sensitivity and specificity limit its clinical application. Cytoskeleton-associated protein 4 (CKAP4) is a novel oncogenic protein involved in the development and progression of HCC. This study aimed to evaluate whether measurement of circulating CKAP4 could improve diagnostic accuracy for HCC. Methods: We analyzed data for patients with HCC, chronic hepatitis B infection, and cirrhosis and healthy controls (n=100 in each group), recruited from two centers between July 2013 and December 2015. Circulating levels of CKAP4 were measured with commercial enzyme-linked immunosorbent assay kits. Receiver operating characteristics were used to evaluate diagnostic accuracy. Results: Serum concentrations of CKAP4 were significantly elevated in the HCC group, in comparison with the three control groups (all P<0.001). The combined biomarker panel (AFP and CKAP4), created by binary logistic regression, presented better performance (area under the curve [AUC] 0.936, 95% CI [0.908–0.965], sensitivity 0.800, specificity 0.963) than AFP (AUC 0.875 [0.835–0.914], sensitivity 0.930, specificity 0.430, P=0.001) or CKAP4 (AUC 0.821 [0.776–0.866], sensitivity 0.790, specificity 0.670, P<0.001) alone to identify HCC, even though CKAP4 alone was not better than AFP (P=0.093). Furthermore, the combined panel also presented a better performance even in identifying early HCC (AUC 0.922 [0.833–0.961]). Conclusion: Serum CKAP4 is a novel biomarker for HCC, and it could complement AFP in improving diagnostic accuracy. [ABSTRACT FROM AUTHOR]

Background: Recent evidence indicates a pivotal role for fetuin B, one of the cystatin superfamily of cysteine protease inhibitors, in the pathogenesis of metabolic diseases. This study investigated whether serum fetuin B levels are associated with the presence of coronary artery disease. Methods: Serum fetuin B levels were assessed in 87 patients with coronary artery disease (41 with acute coronary syndromes and 46 with stable angina pectoris) and 87 healthy controls using an enzyme-linked immunosorbent assay. The association of serum fetuin B levels with cardiac risk factors was analyzed. Results: Serum fetuin B levels were significantly higher in patients with coronary artery disease than those in healthy controls (90.7 ± 32.1 vs. 110.0 ± 32.7 μg/ml, P < 0.001), extremely elevated in group with acute coronary syndromes (115.0 ± 35.2 μg/ml). Pearson correlation analysis showed that serum fetuin B levels were positively associated with the levels of total cholesterol ( r = 0.276, P < 0.001), low-density lipoprotein cholesterol ( r = 0.363, P < 0.001), and fasting blood glucose ( r = 0.159, P < 0.05). In addition, multiple logistic regression analyses revealed that fetuin B was independently associated with the presence of coronary artery disease (OR, 1.019; 95% CI, 1.009 to 1.029; P < 0.001) and acute coronary syndromes (OR, 1.017; 95% CI, 1.006 to 1.028; P < 0.01). Conclusions: Our data revealed that high fetuin B levels are associated with the presence of coronary artery disease and acute coronary syndromes, and that fetuin B may serve as a potential biomarker for coronary artery disease. [ABSTRACT FROM AUTHOR]

Fetuin is an endogenous inhibitor of the insulin receptor tyrosine kinase. Recent studies supported the possible role of fetuin B in metabolic diseases. This study is to evaluate the role of serum fetuin B in nonalcoholic fatty liver disease (NAFLD). A hospital-based case-control study of 184 subjects was conducted. Serum level of fetuin B was measured by enzyme-linked immunosorbent assay. The serum level of fetuin B in the control (91.0 ± 36.9 μg/ml) was lower than it in NAFLD (108.7 ± 38.5 μg/ml, P < 0.001). The percentage of NAFLD increased (42.9%, 58.7% and 60.2%; P < 0.001; linear-by-linear association: P < 0.001), as fetuin B concentration elevated in its tertiles, after adjustment of body mass index (BMI). Furthermore, compared with the 1st tertile, the 3rd tertile of fetuin B indicated an association with the presence of NAFLD (adjusted odds ratio = 2.087, 95% confidence interval [1.016 - 3.937], P = 0.023), after controlling age, sex, BMI, diabetes, hypertension and hypertriglyceridemia. Lastly, fetuin B correlated with diastolic blood pressure, serum alanine transaminase and triglycerides, among the controls. It suggested a potential association between serum fetuin B and the presence of NAFLD. [ABSTRACT FROM AUTHOR]

Late stent thrombosis (LST) following drug-eluting stent (DES) implantation in patients with coronary artery disease (CAD) is often associated with delayed vascular healing, resulting from vascular inflammation and hypersensitivity to durable polymers and drugs. Therefore, DES design, materials, and coatings have been technologically revolutionized. Herein, we designed a novel abluminal groove-filled biodegradable polymer sirolimus-eluting stent (AGF-BP-SES), with a sirolimus content of only about one-third of traditional DES. The mechanical performances of AGF-BP-SES during compression and expansion were investigated. The pharmacokinetic (PK) profile of sirolimus was studied in the swine model. The in vivo efficacy of AGF-BP-SES was compared with that of Xience PRIME stent. The results showed that AGF-BP-SES exhibited mechanical properties similar to traditional DES, including the rebound ratio of radial contraction/direction, rebound ratio of axial contraction/direction, and inhomogeneity of compression/expansion. Despite utilizing a reduced dose of sirolimus, AGF-BP-SES delivered sirolimus to the coronary artery in a controlled and efficient manner. The stent maintained a safe and effective local drug concentration without local or systemic risks. In the swine model, histopathological indicators predicted safety and biocompatibility of AGF-BP-SES. In conclusion, AGF-BP-SES maintained similar mechanical properties as other stents while reducing the drug-loading capacity, and showed a favorable safety and efficacy profile of the targeted DES. Graphical Abstract: [ABSTRACT FROM AUTHOR]

Background: Due to the changes in lifestyle and dietary behaviors, the incidence of colorectal cancer (CRC) has been rapidly increasing in China.Aims: This study is to present the trends of CRC in China over the past decade.Methods: It used a series of nationally representative data, including the National Central Cancer Registry of China, the GLOBOCAN project and the Global Burden of Disease.Results: The age-standardized rate of CRC incidence increased from 12.8 in 2003 to 16.8 per 100,000 in 2011, while the mortality rose from 5.9 to 7.8 per 100,000. The age group most affected by incident CRC cases were those aged 60-74 years old, whereas CRC death was most associated with those >74 years. Furthermore, the east coast of China presented a higher mortality rate (>15 and 10-14.9 per 100,000 in men and women) than central and west China (5-14.9 and 5-9.9 per 100,000). Compared with other countries worldwide, China indicated lower rates of incidence (14.2 per 100,000), mortality (7.4 per 100,000), and 5-year prevalence (52.7 per 100,000) than most developed countries. However, China had a higher case-fatality ratio (14.0 %) and mortality/incidence ratio (52.1 %). Lastly, disability-adjusted life years attributed to CRC in China was 224.2 per 100,000.Conclusions: It presents a steady increase in CRC in China over the past decade. It also reveals the domestic diversity of age, gender, and geography and finds the differences between China and developed countries, which may yield insights for national programs and policies. [ABSTRACT FROM AUTHOR]

Renal fibrosis is a significant threat to public health globally. Diverse primary aetiologies eventually result in chronic kidney disease ( CKD) and immune cells influence this process. The roles of monocytes/macrophages, T cells, and mast cells have been carefully examined, whilst only a few studies have focused on the effect of B cells. We investigated B-cell function in tubulointerstitial fibrosis induced by unilateral ureteral obstruction ( UUO), using genetic B-cell-deficient μMT mice or CD20 antibody-mediated B-cell-depleted mice. Obstructed kidneys of μMT and anti- CD20-treated mice showed lower levels of monocyte/macrophage infiltration and collagen deposition compared to wild-type mice. Mechanistically, anti- CD20 attenuated UUO-induced alterations of renal tumour necrosis factor-α ( TNF-α), vascular cell adhesion molecule 1 ( VCAM-1) pro-inflammatory genes, and CC chemokine ligand-2 ( CCL2) essential for monocyte recruitment; B cells were one of the main sources of CCL2 in post- UUO kidneys. Neutralization of CCL2 reduced monocyte/macrophage influx and fibrotic changes in obstructed kidneys. Therefore, early-stage accumulation of B cells in the kidney accelerated monocyte/macrophage mobilization and infiltration, aggravating the fibrosis resulting from acutely induced kidney nephropathy. © 2016 The Authors. Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland. [ABSTRACT FROM AUTHOR]

Myocardial ischemia/reperfusion (I/R) injury interferes with the restoration of blood flow to ischemic myocardium. Oxidative stress-elicited apoptosis has been reported to contribute to I/R injury. All-trans retinoic acid (ATRA) has anti-apoptotic activity as previously reported. Here, we investigated the effects and the mechanism of action of ATRA on myocardial I/R injury both in vivo and in vitro. In vivo, ATRA reduced the size of the infarcted area (17.81±1.05% vs. 24.41±1.03%, P<0.05) and rescued cardiac function loss (ejection fraction 46.42±6.76% vs. 37.18±4.63%, P<0.05) after I/R injury. Flow-cytometric analysis and TUNEL assay demonstrated that the protective role of ATRA on myocardial I/R injury was related to its anti-apoptotic effects. The anti-apoptotic effects of ATRA were associated with partial inhibition of reactive oxygen species (ROS) production and significantly less phosphorylation of mitogen-activated protein kinases (MAPKs) including p38, JNK, and ERK. Western blot analysis also revealed that ATRA pre-treatment increased a disintegrin and metalloproteinase domain-containing protein 10 (ADAM10) expression (0.65 ± 0.20 vs. 0.41±0.02 in vivo) and reduced the level of receptor for advanced glycation end-products (RAGE) (0.38 ± 0.17 vs. 0.52 ± 0.11 in vivo). Concomitantly, the protective role of ATRA on I/R injury was not observed in RAGE-KO mice. The current results indicated that ATRA could prevent myocardial injury and reduced cardiomyocyte apoptosis after I/R effectively. One possible mechanism underlying these effects is that ATRA could increase ADAM10 expression and thus cleave RAGE, which is the main receptor up-stream of MAPKs in myocardial I/R injury, resulting in the down-regulation of MAPK signaling and protective role on myocardial I/R injury. [ABSTRACT FROM AUTHOR]

Uremic toxins such as p-cresyl sulfate ( PCS) are associated with increased mortality for chronic kidney disease ( CKD) patients, but in vivo PCS toxicity studies are limited due to the lack of a standard animal model. To establish such a model, we measured the pharmacokinetics of PCS in mice with variable renal function. Male Balb/c mice subjected to 5/6 nephrectomy ( CRF), unilateral nephrectomy ( UNX), or no surgery (controls) were given PCS (po, 50 mg/kg). Blood samples were collected over time and plasma PCS concentrations were measured. Over 4 h, PCS was significantly higher in the plasma of CRF mice (63.28 ± 2.76 mg/L), compared to UNX mice (3.11 ± 0.64 mg/L) and controls (0.39 ± 0.12 mg/L). The PCS half-life was greatest in CRF mice (12.07 ± 0.12 h), compared to 0.79 ± 0.04 h in UNX mice and 0.48 ± 0.02 h in control mice. However, the potential presence of additional uremic toxins along with PCS in CRF mice and rapid PCS clearance in control mice suggest that the UNX mouse would be a better PCS model to study toxicity. [ABSTRACT FROM AUTHOR]