hs-CRP is strongly associated with coronary heart disease (CHD): A data mining approach using decision tree algorithm

Integration of 16S rRNA sequencing and metabolomics to investigate the modulatory effect of ginsenoside Rb1 on atherosclerosis

Background

/aims: Atherosclerosis (AS) is the common pathological basis of a variety of cardiovascular diseases (CVD), and has become the main cause of human death worldwide, and the incidence is increasing and younger trend. Ginsenoside Rb1 (Rb1), an important monomer component of the traditional Chinese herb ginseng, known for its ability to improve blood lipid disorders and anti-inflammatory. In addition, Rb1 was proved to be an effective treatment for AS. However, the effect of Rb1 on AS remains to be elucidated. The aim of this study was to investigate the mechanisms of Rb1 in ameliorating AS induced by high-fat diet (HFD).

Materials and methods

In this study, we developed an experimental AS model in Sprague-Dawley rats by feeding HFD with intraperitoneal injection of vitamin D3. The potential therapeutic mechanism of Rb1 in AS rats was investigated by detecting the expression of inflammatory factors, microbiome 16S rRNA gene sequencing, short-chain fatty acids (SCFAs) targeted metabolomics and untargeted metabolomics.

Results

Rb1 could effectively alleviate the symptoms of AS and suppress the overexpression of inflammation-related factors. Meanwhile, Rb1 altered gut microbial composition and concentration of SCFAs characterized by Bacteroidetes, Actinobacteria, Lactobacillus, Prevotella, Oscillospira enrichment and Desulfovibrio depletion, accompanied by increased production of acetic acid and propionic acid. Moreover, untargeted metabolomics showed that Rb1 considerably improved faecal metabolite profiles, particularly arachidonic acid metabolism and primary bile acid biosynthesis.

Conclusion

Rb1 ameliorated the HFD-induced AS, and the mechanism is related to improving intestinal metabolic homeostasis and inhibiting systemic inflammation by regulating gut microbiota.

Stimulant use, HIV, and immune dysregulation among sexual minority men

BACKGROUND

Sexual minority men (SMM) report high rates of stimulant use (e.g., crystal methamphetamine, cocaine) and HIV infection. Stimulant use contributes to immune dysfunction, which enhances risk for HIV acquisition and pathogenesis. Research is needed to examine the independent and interactive relationships of stimulant use and HIV infection with systemic immune dysregulation among SMM, especially during the COVID-19 pandemic.

METHODS

From 2020-2022, 75 SMM in Miami, Florida with and without HIV completed an online survey and provided biospecimens to assess HIV status and viral load (VL), recent stimulant use, and soluble markers of immune activation and inflammation in plasma, including soluble CD14 (sCD14) and elevated high-sensitivity C-reactive protein (hs-CRP > 1.0mg/L). Sociodemographics and prior SARS-CoV-2 infection were compared across HIV status/stimulant use groups. Moderation models examined the independent and interactive associations of stimulant use and HIV status with sCD14 and elevated hs-CRP.

RESULTS

Thirty participants were persons living with HIV (PWH) (50% with stimulant use), and 45 were HIV-negative (44% with stimulant use). SARS-CoV-2 infection was not associated with stimulant use/HIV groups or immune outcomes. HIV-negative SMM without stimulant use had lower sCD14 compared to other SMM, as well as lower odds of elevated hs-CRP compared to PWH who used stimulants. Stimulant use showed independent associations with immune dysregulation that persisted after controlling for HIV status and VL, whereas HIV status was only independently associated with elevated hs-CRP in one model not controlling for VL.

CONCLUSIONS

Among SMM, stimulant use was independently associated with elevated immune activation and inflammation.

Qualitative Classification of Proximal Femoral Bone Using Geometric Features and Texture Analysis in Collected MRI Images for Bone Density Evaluation

The aim of this study was to use geometric features and texture analysis to discriminate between healthy and unhealthy femurs and to identify the most influential features. We scanned proximal femoral bone (PFB) of 284 Iranian cases (21 to 83 years old) using different dual-energy X-ray absorptiometry (DEXA) scanners and magnetic resonance imaging (MRI) machines. Subjects were labeled as "healthy" (T-score > -0.9) and "unhealthy" based on the results of DEXA scans. Based on the geometry and texture of the PFB in MRI, 204 features were retrieved. We used support vector machine (SVM) with different kernels, decision tree, and logistic regression algorithms as classifiers and the Genetic algorithm (GA) to select the best set of features and to maximize accuracy. There were 185 participants classified as healthy and 99 as unhealthy. The SVM with radial basis function kernels had the best performance (89.08%) and the most influential features were geometrical ones. Even though our findings show the high performance of this model, further investigation with more subjects is suggested. To our knowledge, this is the first study that investigates qualitative classification of PFBs based on MRI with reference to DEXA scans using machine learning methods and the GA.

EDST: a decision stump based ensemble algorithm for synergistic drug combination prediction

INTRODUCTION

There are countless possibilities for drug combinations, which makes it expensive and time-consuming to rely solely on clinical trials to determine the effects of each possible drug combination. In order to screen out the most effective drug combinations more quickly, scholars began to apply machine learning to drug combination prediction. However, most of them are of low interpretability. Consequently, even though they can sometimes produce high prediction accuracy, experts in the medical and biological fields can still not fully rely on their judgments because of the lack of knowledge about the decision-making process.

RELATED WORK

Decision trees and their ensemble algorithms are considered to be suitable methods for pharmaceutical applications due to their excellent performance and good interpretability. We review existing decision trees or decision tree ensemble algorithms in the medical field and point out their shortcomings.

METHOD

This study proposes a decision stump (DS)-based solution to extract interpretable knowledge from data sets. In this method, a set of DSs is first generated to selectively form a decision tree (DST). Different from the traditional decision tree, our algorithm not only enables a partial exchange of information between base classifiers by introducing a stump exchange method but also uses a modified Gini index to evaluate stump performance so that the generation of each node is evaluated by a global view to maintain high generalization ability. Furthermore, these trees are combined to construct an ensemble of DST (EDST).

EXPERIMENT

The two-drug combination data sets are collected from two cell lines with three classes (additive, antagonistic and synergistic effects) to test our method. Experimental results show that both our DST and EDST perform better than other methods. Besides, the rules generated by our methods are more compact and more accurate than other rule-based algorithms. Finally, we also analyze the extracted knowledge by the model in the field of bioinformatics.

CONCLUSION

The novel decision tree ensemble model can effectively predict the effect of drug combination datasets and easily obtain the decision-making process.

Diabetes mellitus early warning and factor analysis using ensemble Bayesian networks with SMOTE-ENN and Boruta

Diabetes mellitus (DM) has become the third chronic non-infectious disease affecting patients after tumor, cardiovascular and cerebrovascular diseases, becoming one of the major public health issues worldwide. Detection of early warning risk factors for DM is key to the prevention of DM, which has been the focus of some previous studies. Therefore, from the perspective of residents' self-management and prevention, this study constructed Bayesian networks (BNs) combining feature screening and multiple resampling techniques for DM monitoring data with a class imbalance in Shanxi Province, China, to detect risk factors in chronic disease monitoring programs and predict the risk of DM. First, univariate analysis and Boruta feature selection algorithm were employed to conduct the preliminary screening of all included risk factors. Then, three resampling techniques, SMOTE, Borderline-SMOTE (BL-SMOTE) and SMOTE-ENN, were adopted to deal with data imbalance. Finally, BNs developed by three algorithms (Tabu, Hill-climbing and MMHC) were constructed using the processed data to find the warning factors that strongly correlate with DM. The results showed that the accuracy of DM classification is significantly improved by the BNs constructed by processed data. In particular, the BNs combined with the SMOTE-ENN resampling improved the most, and the BNs constructed by the Tabu algorithm obtained the best classification performance compared with the hill-climbing and MMHC algorithms. The best-performing joint Boruta-SMOTE-ENN-Tabu model showed that the risk factors of DM included family history, age, central obesity, hyperlipidemia, salt reduction, occupation, heart rate, and BMI.

Comparison of decision tree with common machine learning models for prediction of biguanide and sulfonylurea poisoning in the United States: an analysis of the National Poison Data System

BACKGROUND

Biguanides and sulfonylurea are two classes of anti-diabetic medications that have commonly been prescribed all around the world. Diagnosis of biguanide and sulfonylurea exposures is based on history taking and physical examination; thus, physicians might misdiagnose these two different clinical settings. We aimed to conduct a study to develop a model based on decision tree analysis to help physicians better diagnose these poisoning cases.

METHODS

The National Poison Data System was used for this six-year retrospective cohort study.The decision tree model, common machine learning models multi layers perceptron, stochastic gradient descent (SGD), Adaboosting classiefier, linear support vector machine and ensembling methods including bagging, voting and stacking methods were used. The confusion matrix, precision, recall, specificity, f1-score, and accuracy were reported to evaluate the model's performance.

RESULTS

Of 6183 participants, 3336 patients (54.0%) were identified as biguanides exposures, and the remaining were those with sulfonylureas exposures. The decision tree model showed that the most important clinical findings defining biguanide and sulfonylurea exposures were hypoglycemia, abdominal pain, acidosis, diaphoresis, tremor, vomiting, diarrhea, age, and reasons for exposure. The specificity, precision, recall, f1-score, and accuracy of all models were greater than 86%, 89%, 88%, and 88%, respectively. The lowest values belong to SGD model. The decision tree model has a sensitivity (recall) of 93.3%, specificity of 92.8%, precision of 93.4%, f1_score of 93.3%, and accuracy of 93.3%.

CONCLUSION

Our results indicated that machine learning methods including decision tree and ensembling methods provide a precise prediction model to diagnose biguanides and sulfonylureas exposure.

Risk Factors for Cardiometabolic Disease in Professional Firefighters

OBJECTIVE

Firefighters are plagued with cardiometabolic disease (CMD). Obesity, poor cardiorespiratory and muscular fitness, and blood lipids (low-density lipoprotein cholesterol, triglycerides, low high-density lipoprotein cholesterol) are risk factors for CMD. However, markers of oxidative stress, inflammation, and insulin resistance can provide further insight regarding CMD risk.

METHODS

This study investigated the relationships between fitness metrics (cardiorespiratory and muscular fitness, percent body fat, waist circumference), blood lipids, blood pressure, and years of experience as a firefighter to blood markers of insulin resistance: Homeostatic Model Assessment for Insulin Resistance (HOMA-IR), oxidative stress: advanced oxidation protein products (AOPPs), and inflammation: C-reactive protein.

RESULTS

Waist circumference and blood concentrations of triglycerides were significantly related to AOPPs and HOMA-IR. Cardiorespiratory fitness was inversely related to AOPPs, HOMA-IR and C-reactive protein.

CONCLUSION

These findings demonstrate the importance of high cardiorespiratory fitness and low waist circumference to reduce markers of CMD.

Metabolic profile and potential mechanisms of Wendan decoction on coronary heart disease by ultra-high-performance quadrupole time of flight-mass spectrometry combined with network pharmacology analysis

Wendan decoction, a well-known classical traditional Chinese medicine prescription, has been widely used in the clinical application of coronary heart disease for thousands of years. However, due to a lack of research on the overall metabolism of Wendan decoction, the bioavailable components responsible for the therapeutic effects remain unclear, hindering the revelation of its mechanisms against coronary heart disease. Consequently, an efficient joint research pattern combined with characterization of the metabolic profile and network pharmacology analysis was proposed. As a result, a total of 172 Wendan decoction-related xenobiotics (57 prototypes and 115 metabolites) were detected based on the exploration of the typical metabolic pathways of representative pure compounds in vivo, describing their multi-component metabolic characteristics comprehensively. Subsequently, an integrated network of "herbs-bioavailable compounds-coronary heart disease targets-pathways-therapeutic effects" was constructed, and its seven compounds were finally screened out as the key components acting on five main targets of coronary heart disease. Overall, this work not only provided a crucial biological foundation for interpreting the effective components and action mechanisms of Wendan decoction on coronary heart disease but also showed a reference value for revealing the bioactive components of traditional Chinese medicine prescriptions.

Impact of mulberry consumption on cardiometabolic risk factors: A systematic review and meta-analysis of randomized-controlled trials

OBJECTIVE

The current study aimed to comprehensively evaluate the potential effects of mulberry consumption on cardiometabolic risk factors in adults.

METHODS

Relevant articles published up to January 2021 were systematically retrieved from SCOPUS, PubMed/MEDLINE, EMBASE, and Web of Science databases. We included all randomized controlled trials (RCTs) investigating the impact of mulberry consumption on various cardiometabolic risk factors.

RESULTS

The quantitative meta-analysis of 12 eligible RCTs demonstrated a significant reducing effect of mulberry consumption on haemoglobin A1c (HbA1c) (weighted mean difference [WMD]: -0. 55, 95% CI: -1.08, -0.02, p = 0.044), serum total cholesterol (TC) (WMD: -13.13 mg/dl, 95% CI: -19.06, -7.20, p < 0.001), low-density lipoprotein levels (LDL-C) (WMD: -8.84 mg/dl, 95% CI: -13.26, -4.42, p < 0.001), triglycerides (TG) (WMD: -19.67 mg/dl, 95% CI: -30.13, -9.22, p < 0.001) and C-reactive protein (CRP) (WMD: -1.60, mg/L, 95% CI: -3.07, -0.12, p = 0.034). Also, >300 mg daily intake of mulberry exhibited a favourable effect on serum high-density lipoprotein levels (HDL-C). However, there were no significant differences between mulberry intervention and control groups for other factors.

CONCLUSION

The current systematic review and meta-analysis revealed that incorporating mulberry into the diet may favourably affect several cardiometabolic risk factors.

Acute coronary syndrome risk prediction based on gradient boosted tree feature selection and recursive feature elimination: A dataset-specific modeling study

Acute coronary syndrome (ACS) is a serious cardiovascular disease that can lead to cardiac arrest if not diagnosed promptly. However, in the actual diagnosis and treatment of ACS, there will be a large number of redundant related features that interfere with the judgment of professionals. Further, existing methods have difficulty identifying high-quality ACS features from these data, and the interpretability work is insufficient. In response to this problem, this paper uses a hybrid feature selection method based on gradient boosting trees and recursive feature elimination with cross-validation (RFECV) to reduce ACS feature redundancy and uses interpretable feature learning for feature selection to retain the most discriminative features. While reducing the feature set search space, this method can balance model simplicity and learning performance to select the best feature subset. We leverage the interpretability of gradient boosting trees to aid in understanding key features of ACS, linking the eigenvalue meaning of instances to model risk predictions to provide interpretability for the classifier. The data set used in this paper is patient records after percutaneous coronary intervention (PCI) in a tertiary hospital in Fujian Province, China from 2016 to 2021. In this paper, we experimentally explored the impact of our method on ACS risk prediction. We extracted 25 key variables from 430 complex ACS medical features, with a feature reduction rate of 94.19%, and identified 5 key ACS factors. Compared with different baseline methods (Logistic Regression, Random Forest, Gradient Boosting, Extreme Gradient Boosting, Multilayer Perceptron, and 1D Convolutional Networks), the results show that our method achieves the highest Accuracy of 98.8%.

Decision Tree Modeling for Osteoporosis Screening in Postmenopausal Thai Women

Osteoporosis is still a serious public health issue in Thailand, particularly in postmenopausal women; meanwhile, new effective screening tools are required for rapid diagnosis. This study constructs and confirms an osteoporosis screening tool-based decision tree (DT) model. Four DT algorithms, namely, classification and regression tree; chi-squared automatic interaction detection (CHAID); quick, unbiased, efficient statistical tree; and C4.5, were implemented on 356 patients, of whom 266 were abnormal and 90 normal. The investigation revealed that the DT algorithms have insignificantly different performances regarding the accuracy, sensitivity, specificity, and area under the curve. Each algorithm possesses its characteristic performance. The optimal model is selected according to the performance of blind data testing and compared with traditional screening tools: Osteoporosis Self-Assessment for Asians and the Khon Kaen Osteoporosis Study. The Decision Tree for Postmenopausal Osteoporosis Screening (DTPOS) tool was developed from the best performance of CHAID’s algorithms. The age of 58 years and weight at a cutoff of 57.8 kg were the essential predictors of our tool. DTPOS provides a sensitivity of 92.3% and a positive predictive value of 82.8%, which might be used to rule in subjects at risk of osteopenia and osteoporosis in a community-based screening as it is simple to conduct.

Role of High-Sensitivity C-reactive Protein (Hs-CRP) in Non-communicable Diseases: A Review

Non-communicable diseases like cardiovascular diseases, cerebrovascular diseases, diabetes mellitus, and cancer are very common causes of death worldwide. Therefore, the need to search for novel, affordable, and easily accessible biomarkers and risk factors for non-communicable diseases continues, which can predict the future risk of having these diseases with greater accuracy and precision. In this context, among available biomarkers, high-sensitivity C-reactive protein (Hs-CRP) is considered to be the best-suited marker. Various drug intervention trials demonstrated positive results in reducing Hs-CRP in individuals with raised levels. Numerous pharmacological and non-pharmacologic interventions in the form of lifestyle modifications, exercise, and cessation of smoking are being investigated to study their effect on reducing serum C-reactive protein (CRP) levels.

This review article discusses the role of Hs-CRP and its isoforms in the pathogenesis of various disease conditions, factors affecting its serum concentration, its prognostic value, and its comparison with other risk factors. Further, its clinical significance in chronic inflammatory and degenerative diseases of the nervous system and other common non-communicable diseases, including recent advances in the management of various diseases, has also been discussed.

Predictive Significance of High-Sensitivity C-Reactive Protein Combined with Homocysteine for Coronary Heart Disease in Patients with Anxiety Disorders

Background

Currently, there are few studies on biomarkers for predicting coronary heart disease (CHD) with anxiety disorders.

Objective

To explore risk factors and investigate the predictive value of common clinical peripheral blood indicators, such as high-sensitivity C-reactive protein (hs-CRP) and homocysteine (Hcy) for CHD patients with anxiety disorders.

Methods

One hundred fifty-three hospitalized patients with chest pain as the main symptom and a Hamilton Anxiety Scale score > 14 were recruited from October 2020 to September 2021 in the hospital. Then, they were divided into an anxiety disorder with CHD group (observation group, n = 64) and a simple anxiety disorder group (control group, n = 89), according to coronary angiography (CAG) findings. Patients' demographic and clinical messages were collected and compared. Diabetes mellitus and hypertension, body mass index (BMI), and peripheral blood interleukin-6 (IL-6), high-sensitivity C-reactive protein (hs-CRP), homocysteine (Hcy), fibrinogen, D-dimer, cortisol, and norepinephrine expression levels were compared. Binary logistic regression analysis screened independent risk factors of CHD patients with anxiety disorders. The effectiveness of independent risk factors in predicting CHD with anxiety disorders was analyzed using receiver operating characteristic (ROC) curves.

Results

IL-6, hs-CRP, and Hcy levels of anxiety disorder in the CHD group were significantly higher than those in the simple anxiety disorder group. Binary multiple logistic regression analysis indicated that IL-6, hs-CRP, and Hcy were independent risk factors for CHD in patients with anxiety disorders. hs-CRP and Hcy levels were positively correlated with the Gensini score. ROC curve analysis indicated that the detection of hs-CRP or Hcy alone or the combined detection of the 2 had clinical predictive value for CHD in patients with anxiety disorders, and the area under the curve (AUC) of the combined detection of the 2 was significantly larger than that of any single factor alone (vs. hs-CRP, P = 0.045; vs. Hcy, P = 0.045).

Conclusion

IL-6, hs-CRP, and Hcy are related to CHD with anxiety disorders. Serum levels of the combined detection of hs-CRP and Hcy have a high clinical predictive value for CHD in patients with anxiety disorders.

All-Cause Death Prediction Method for CHD Based on Graph Convolutional Networks

Coronary heart disease (CHD) has become one of the most serious public health issues due to its high morbidity and mortality rates. Most of the existing coronary heart disease risk prediction models manually extract features based on shallow machine learning methods. It only focuses on the differences between local patient features and ignores the interaction modeling between global patients. Its accuracy is still insufficient for individualized patient management strategies. In this paper, we propose CHD prediction as a graph node classification task for the first time, where nodes can represent individuals in potentially diseased populations and graphs intuitively represent associations between populations. We used an adaptive multi-channel graph convolutional neural network (AM-GCN) model to extract graph embeddings from topology, node features, and their combinations through graph convolution. Then, the adaptive importance weights of the extracted embeddings are learned by using an attention mechanism. For different situations, we model the relationship of the CHD population with the population graph and the K-nearest neighbor graph method. Our experimental evaluation explored the impact of the independent components of the model on the CHD disease prediction performance and compared it to different baselines. The experimental results show that our new model exhibits the best experimental results on the CHD dataset, with a 1.3% improvement in accuracy, a 5.1% improvement in AUC, and a 4.6% improvement in F1-score compared to the nongraph model.

Research on Application of Data Mining Algorithm in Cardiac Medical Diagnosis System

Heart disease is a very common high-incidence disease. Due to the wide variety of pathology of heart disease, how to improve the medical diagnosis of heart disease and carry out earlier intervention and treatment is a problem that needs to be solved urgently. The paper adds the decision tree algorithm and its comparison and proposes an optimized classification algorithm Co-SVM. Based on the establishment of a heart disease diagnosis classifier based on data mining algorithms, it is aimed at exploring which of these four algorithms is more suitable for heart disease diagnosis problems and optimizing them. A brief description of the cause, influencing factors, and acquired data of heart disease can be seen from the accuracy and scientificity of the data, which further enhances the authenticity and reliability of the clinical diagnosis model of heart disease. At the same time, the ultrasound diagnosis technology of heart disease is introduced, and the important role of ultrasound diagnosis technology in the medical diagnosis of heart disease is discussed. This thesis uses the heart disease clinical data set to establish a heart disease diagnosis classifier based on the decision tree algorithm, neural network algorithm, support vector machine algorithm, and Co-SVM algorithm. Through experimental comparison and analysis, the optimal classification is selected according to the obtained results. The algorithm is Co-SVM algorithm. The experimental results show that the proposed Co-SVM algorithm has a higher accuracy rate than the other three classic algorithms, and the effectiveness of the Co-SVM algorithm is verified by the evaluation results of multiple algorithms. By applying the Co-SVM algorithm in the medical diagnosis system, it is helpful to assist doctors in making more accurate and precise diagnosis of the condition.

Research on the Risk of Social Stability of Enterprise Credit Supervision Mechanism Based on Big Data

The study aims to establish a platform-based enterprise credit supervision mechanism, and combined with big data, accurately evaluate the credit assets of enterprises under the influence of social stability risk, and improve the ability of enterprises to deal with risks. Using descriptive statistical methods, the study shows that most local enterprises exist in the form of micro loans, which promotes the development of local economy to a certain extent, but it is a vicious cycle of economic development; The overall prediction accuracy of the single enterprise risk assessment model under the influence of social stability risk is 65%. Compared with the single algorithm, the prediction accuracy of the integrated algorithm model is significantly improved, and the prediction accuracy can reach 83.5%, the standard deviation of data prediction is small, and the stability of the model is high.

The Influence of E-book Teaching on the Motivation and Effectiveness of Learning Law by Using Data Mining Analysis

This paper studies the motivation of learning law, compares the teaching effectiveness of two different teaching methods, e-book teaching and traditional teaching, and analyses the influence of e-book teaching on the effectiveness of law by using big data analysis. From the perspective of law student psychology, e-book teaching can attract students' attention, stimulate students' interest in learning, deepen knowledge impression while learning, expand knowledge, and ultimately improve the performance of practical assessment. With a small sample size, there may be some deficiencies in the research results' representativeness. To stimulate the learning motivation of law as well as some other theoretical disciplines in colleges and universities has particular referential significance and provides ideas for the reform of teaching mode at colleges and universities. This paper uses a decision tree algorithm in data mining for the analysis and finds out the influencing factors of law students' learning motivation and effectiveness in the learning process from students' perspective.

Evaluation of Classification for Project Features with Machine Learning Algorithms

Due to the asymmetry of project features, it is difficult for project managers to make a reliable prediction of the decision-making process. Big data research can establish more predictions through the results of accurate classification. Machine learning (ML) has been widely applied for big data analytic and processing, which includes model symmetry/asymmetry of various prediction problems. The purpose of this study is to achieve symmetry in the developed decision-making solution based on the optimal classification results. Defects are important metrics of construction management performance. Accordingly, the use of suitable algorithms to comprehend the characteristics of these defects and train and test massive data on defects can conduct the effectual classification of project features. This research used 499 defective classes and related features from the Public Works Bid Management System (PWBMS). In this article, ML algorithms, such as support vector machine (SVM), artificial neural network (ANN), decision tree (DT), and Bayesian network (BN), were employed to predict the relationship between three target variables (engineering level, project cost, and construction progress) and defects. To formulate and subsequently cross-validate an optimal classification model, 1015 projects were considered in this work. Assessment indicators showed that the accuracy of ANN for classifying the engineering level is 93.20%, and the accuracy values of SVM for classifying the project cost and construction progress are 85.32% and 79.01%, respectively. In general, the SVM yielded better classification results from these project features. This research was based on an ML algorithm evaluation system for buildings as a classification model for project features with the goal of aiding project managers to comprehend defects.

Application of the Truncated Zero-Inflated Double Poisson for Determining of the Effecting Factors on the Number of Coronary Artery Stenosis

Background

Risk factors of coronary heart disease have been discussed in the literature; however, conventional statistical models are not appropriate when the outcome of interest is number of vessels with obstructive coronary artery disease. In this paper, a novel statistical model is discussed to investigate the risk factors of number of vessels with obstructive coronary artery disease.

Methods

This cross-sectional study was conducted on 633 elderly cardiovascular patients at Ghaem Hospital, Mashhad, Iran from September 2011 to May 2013. Clinical outcome is number of vessels with obstructive coronary artery disease (=0, 1, 2, 3), and predictor variables are baseline demographics and clinical features. A right-truncated zero-inflated double Poisson regression model is performed which can accommodate both underdispersion and excess zeros in the outcome. The goodness-of-fit of the proposed model is compared with conventional regression models.

Results

Out of 633 cardiovascular patients, 327 were male (51.7%). Mean age was ~65 ± 7 years (for individuals with zero, one ,and two coronary artery stenosis) and ~66 ± 7 years (for individuals with three coronary artery stenosis). BMI (0.04 ± 0.01, p = 0.011) and female gender (0.19 ± 0.09, p = 0.032) were significant associated with the count part of the model, and only BMI (−0.47 ± 0.2, p = 0.011) was significantly predictive of logit part of the model. The goodness-of-fit measurements indicate that the proposed model outperforms the conventional regression models.

Conclusion

The proposal regression model shows a better fit compared to the standard regression analysis in modeling number of vessels with obstructive coronary artery disease. Hence, using truncated zero-inflated double Poisson regression model—as an alternative model—is advised to study the risk factors of number of involved vessels of coronary artery stenosis.

A hybrid method for heart disease diagnosis utilizing feature selection based ensemble classifier model generation

Heart disease is one of the most complicated diseases, and it affects a large number of individuals throughout the world. In healthcare, particularly cardiology, early and accurate detection of cardiac disease is critical. The Heart Disease Data Set-UCI repository collects data on heart disease. The search space and complexity of the classification models are increased by this raw dataset, which contains redundant and inconsistent data. We need to eliminate the redundant and unnecessary elements from the data to improve classification accuracy. As a consequence, feature selection approaches might be useful for reducing the cost of diagnosis by identifying the most important qualities. This research developed an ensemble classification model based on a feature selection approach in which selected features play a role in classification. Accordingly, a classification approach was introduced using ensemble learning with a genetic algorithm, feature selection, and biomedical test values to diagnose heart disease. Based on the results, it is deduced that the benefits of using the feature selection method vary depending on the utilized machine learning technique. However, the best-proposed model based on the combination of genetic algorithm and the ensemble learning model has achieved an accuracy of 97.57% on the considered datasets. The suggested diagnosis system achieved better accuracy than previously proposed methods and can easily be implemented in healthcare to identify heart disease.

A Systematic Review on Prediction Techniques for Cardiac Disease

Mortality rate can be lowered with early prediction of cardiac diseases, which is one of the major issue in healthcare industry. In comparison of traditional methods, intelligent systems have potential to predict these diseases accurately at early stage even with complex data. Various intelligent DSS are presented by researchers for predicting this disease. To study the trends of these intelligent systems, to find the effective techniques for predicting cardiac disease and to find the future directions are the objective of this study. Therefore this paper presents a systematic review on state-of-art techniques based on ML, NN and FL. For analysis, we follow PRISMA statement and considered the studies presented from 2010 to 2020 from different databases. Analysis concluded that ML based techniques are broadly used for feature selection and classification and have the potential for the prediction of cardiac diseases. The future directions are to evaluate the rarely used prediction techniques and finding the way of improving them for model generalization with better prediction accuracy.

Correlation between Changes in Serum RBP4, hs-CRP, and IL-27 Levels and Rosuvastatin in the Treatment of Coronary Heart Disease

Objective

To investigate the correlation between changes in serum RBP4, hs-CRP, and IL-27 levels and rosuvastatin in the treatment of coronary heart disease (CHD).

Methods

One hundred and twenty patients with CHD admitted in our hospital were selected as the research object, including 60 patients with acute coronary syndrome as the ACS group, and 60 patients with stable angina as the SA group. Another 60 patients without CHD who were examined in our hospital at the same time were included in the non-CHD group. The patients with CHD were further divided into the control group (CG) (n = 42, with routine treatment) and the study group (SG) (n = 78, with routine treatment and rosuvastatin) to measure serum RBP4, hs-CRP, and IL-27 levels and analyze the correlation between each index and rosuvastatin in the treatment of CHD.

Results

After retrospective analysis, no significant difference was found among the ACS group, the SA group, and the non-CHD group (P > 0.05). As for serum RBP4, hs-CRP, and IL-27 levels, ACS group > SA group > non-CHD group, with obvious differences among groups (P < 0.05). After Spearman correlation analysis, a positive correlation was observed between Gensini score and serum RBP4, hs-CRP, and IL-27 levels in patients with CHD (P < 0.05). After treatment, serum RBP4, hs-CRP, and IL-27 levels were gradually reduced. At 4 weeks after treatment, serum RBP4, hs-CRP, and IL-27 levels of the CG and the SG were decreased conspicuously, and compared with the control, each index of the SG was obviously lower (P < 0.05).

Conclusion

Serum RBP4, hs-CRP, and IL-27 play an important role in the occurrence and development of CHD, with a positive correlation to the Gensini score, which can indicate the severity of cardiovascular disease to a certain extent. Meanwhile, rosuvastatin can remarkably reduce serum RBP4, hs-CRP, and IL-27 levels, which is of significance for prognosis.

A survey on data mining techniques used in medicine

Data mining is the process of analyzing a massive amount of data to identify meaningful patterns and detect relations, which can lead to future trend prediction and appropriate decision making. Data mining applications are significant in marketing, banking, medicine, etc. In this paper, we present an overview of data mining applications in medicine to provide a clear view of the challenges and previous works in this area for researchers. Data mining techniques such as Decision Tree, Random Forest, K-means Clustering, Support Vector Machine, Logistic Regression, Neural Network, Naive Bayes, and association rule mining are used for diagnosing, prognosis, classifying, constructing predictive models, and analyzing risk factors of various diseases. The main objective of the paper is to analyze and compare different data mining techniques used in the medical applications. We present a summary of the results and provide comparison analysis of the data mining methods employed by the reviewed articles.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40200-021-00884-2.

Prediction of coronary heart disease in rural Chinese adults: a cross sectional study

Background

Coronary heart disease (CHD) is a common cardiovascular disease with high morbidity and mortality in China. The CHD risk prediction model has a great value in early prevention and diagnosis.

Methods

In this study, CHD risk prediction models among rural residents in Xinxiang County were constructed using Random Forest (RF), Support Vector Machine (SVM), and the least absolute shrinkage and selection operator (LASSO) regression algorithms with identified 16 influencing factors.

Results

Results demonstrated that the CHD model using the RF classifier performed best both on the training set and test set, with the highest area under the curve (AUC = 1 and 0.9711), accuracy (one and 0.9389), sensitivity (one and 0.8725), specificity (one and 0.9771), precision (one and 0.9563), F1-score (one and 0.9125), and Matthews correlation coefficient (MCC = one and 0.8678), followed by the SVM (AUC = 0.9860 and 0.9589) and the LASSO classifier (AUC = 0.9733 and 0.9587). Besides, the RF model also had an increase in the net reclassification index (NRI) and integrated discrimination improvement (IDI) values, and achieved a greater net benefit in the decision curve analysis (DCA) compared with the SVM and LASSO models.

Conclusion

The CHD risk prediction model constructed by the RF algorithm in this study is conducive to the early diagnosis of CHD in rural residents of Xinxiang County, Henan Province.

Factors determining the serum 25-hydroxyvitamin D response to vitamin D supplementation: Data mining approach

Vitamin D supplementation has been shown to prevent vitamin D deficiency, but various factors can affect the response to supplementation. Data mining is a statistical method for pulling out information from large databases. We aimed to evaluate the factors influencing serum 25-hydroxyvitamin D levels in response to supplementation of vitamin D using a random forest (RF) model. Data were extracted from the survey of ultraviolet intake by nutritional approach study. Vitamin D levels were measured at baseline and at the end of study to evaluate the responsiveness. We examined the relationship between 76 potential influencing factors on vitamin D response using RF. We found several features that were highly correlated to the serum vitamin D response to supplementation by RF including anthropometric factors (body mass index [BMI], free fat mass [FFM], fat percentage, waist-to-hip ratio [WHR]), liver function tests (serum gamma-glutamyl transferase [GGT], total bilirubin, total protein), hematological parameters (mean corpuscular volume [MCV], mean corpuscular hemoglobin concentration [MCHC], hematocrit), and measurement of insulin sensitivity (homeostatic model assessment of insulin resistance). BMI, total bilirubin, FFM, and GGT were found to have a positive relationship and homeostatic model assessment for insulin resistance, MCV, MCHC, fat percentage, total protein, and WHR were found to have a negative correlation to vitamin D concentration in response to supplementation. The accuracy of RF in predicting the response was 93% compared to logistic regression, for which the accuracy was 40%, in the evaluation of the correlation of the components of the data set to serum vitamin D.

A matrix metalloproteinase-generated neoepitope of CRP can identify knee and multi-joint inflammation in osteoarthritis

OBJECTIVE

To compare C-reactive protein (CRP) and matrix metalloproteinase-generated neoepitope of CRP (CRPM) as biomarkers of inflammation and radiographic severity in patients with knee osteoarthritis.

METHODS

Participants with symptomatic osteoarthritis (n=25) of at least one knee underwent knee radiographic imaging and radionuclide etarfolatide imaging to quantify inflammation of the knees and other appendicular joints. For purposes of statistical analysis, semi-quantitative etarfolatide and radiographic imaging scores were summed across the knees; etarfolatide scores were also summed across all joints to provide a multi-joint synovitis measure. Multiple inflammation and collagen-related biomarkers were measured by ELISA including CRP, CRPM, MMP-generated neoepitopes of type I collagen and type III collagen in serum (n=25), and CD163 in serum (n=25) and synovial fluid (n=18).

RESULTS

BMI was associated with CRP (p=0.001), but not CRPM (p=0.753). Adjusting for BMI, CRP was associated with radiographic knee osteophyte score (p=0.002), while CRPM was associated with synovitis of the knee (p=0.017), synovitis of multiple joints (p=0.008), and macrophage marker CD163 in serum (p=0.009) and synovial fluid (p=0.03). CRP correlated with MMP-generated neoepitope of type I collagen in serum (p=0.045), and CRPM correlated with MMP-generated neoepitope of type III collagen in serum (p<0.0001). No biomarkers correlated with age, knee pain, or WOMAC pain.

CONCLUSIONS

To our knowledge, this is the first time that CRPM has been shown to be associated with knee and multi-joint inflammation based on objective imaging (etarfolatide) and biomarker (CD163) measures. These results demonstrate the capability of biomarker measurements to reflect complex biological processes and for neoepitope markers to more distinctly reflect acute processes than their precursor proteins. CRPM is a promising biomarker of local and systemic inflammation in knee OA that is associated with cartilage degradation and is independent of BMI. CRPM is a potential molecular biomarker alternative to etarfolatide imaging for quantitative assessment of joint inflammation.

A Correlation Between Monocyte to Lymphocyte Ratio and Long-Term Prognosis in Patients With Coronary Artery Disease After PCI

Monocyte to lymphocyte ratio (MLR) has been confirmed as a novel marker of poor prognosis in patients with coronary heart disease (CAD). However, the prognosis value of MLR for patients with CAD after percutaneous coronary intervention (PCI) needs further studies. In present study, we aimed to investigate the correlation between MLR and long-term prognosis in patients with CAD after PCI. A total of 3,461 patients with CAD after PCI at the First Affiliated Hospital of Zhengzhou University were included in the analysis. According to the cutoff value of MLR, all of the patients were divided into 2 groups: the low-MLR group (<0.34, n = 2338) and the high-MLR group (≥0.34, n = 1123). Kaplan–Meier curve was performed to compare the long-term outcome. Multivariate COX regression analysis was used to assess the independent predictors for all-cause mortality, cardiac mortality and MACCEs. Multivariate COX regression analysis showed that the high MLR group had significantly increased all-cause mortality (ACM) [hazard ratio (HR) = 1.366, 95% confidence interval (CI): 1.366-3.650, p = 0.001] and cardiac mortality (CM) (HR = 2.379, 95%CI: 1.611-3,511, p < 0.001) compared to the low MLR group. And high MLR was also found to be highly associated with major adverse cardiovascular and cerebrovascular events (MACCEs) (HR = 1.227, 95%CI: 1.003-1.500, p = 0.047) in patients with CAD undergoing PCI. MLR was an independent predictor of ACM, CM and MACCEs in CAD patients who underwent PCI.

Dietary protein is the strong predictor of coronary artery disease; a data mining approach

BACKGROUNDS

Coronary artery disease (CAD) is the major cause of mortality and morbidity globally. Diet is known to contribute to CAD risk, and the dietary intake of specific macro- or micro-nutrients might be potential predictors of CAD risk. Machine learning methods may be helpful in the analysis of the contribution of several parameters in dietary including macro- and micro-nutrients to CAD risk. Here we aimed to determine the most important dietary factors for predicting CAD.

METHODS

A total of 273 cases with more than 50% obstruction in at least one coronary artery and 443 healthy controls who completed a food frequency questionnaire (FFQ) were entered into the study. All dietary intakes were adjusted for energy intake. The QUEST method was applied to determine the diagnosis pattern of CAD.

RESULTS

A total of 34 dietary variables obtained from the FFQ were entered into the initial study analysis, of these variables 23 were significantly associated with CAD according to t-tests. Of these 23 dietary input variables, adjusted protein, manganese, biotin, zinc and cholesterol remained in the model. According to our tree, only protein intake could identify the patients with coronary artery stenosis according to angiography from healthy participant up to 80%. The dietary intake of manganese was the second most important variable. The accuracy of the tree was 84.36% for the training dataset and 82.94% for the testing dataset.

CONCLUSION

Among several dietary macro- and micro-nutrients, a combination of protein, manganese, biotin, zinc and cholesterol could predict the presence of CAD in individuals undergoing angiography.

Potential value and impact of data mining and machine learning in clinical diagnostics

Data mining involves the use of mathematical sciences, statistics, artificial intelligence, and machine learning to determine the relationships between variables from a large sample of data. It has previously been shown that data mining can improve the prediction and diagnostic precision of type 2 diabetes mellitus. A few studies have applied machine learning to assess hypertension and metabolic syndrome-related biomarkers, as well as refine the assessment of cardiovascular disease risk. Machine learning methods have also been applied to assess new biomarkers and survival outcomes in patients with renal diseases to predict the development of chronic kidney disease, disease progression, and renal graft survival. In the latter, random forest methods were found to be the best for the prediction of chronic kidney disease. Some studies have investigated the prognosis of nonalcoholic fatty liver disease and acute liver failure, as well as therapy response prediction in patients with viral disorders, using decision tree models. Machine learning techniques, such as Sparse High-Order Interaction Model with Rejection Option, have been used for diagnosing Alzheimer's disease. Data mining techniques have also been applied to identify the risk factors for serious mental illness, such as depression and dementia, and help to diagnose and predict the quality of life of such patients. In relation to child health, some studies have determined the best algorithms for predicting obesity and malnutrition. Machine learning has determined the important risk factors for preterm birth and low birth weight. Published studies of patients with cancer and bacterial diseases are limited and should perhaps be addressed more comprehensively in future studies. Herein, we provide an in-depth review of studies in which biochemical biomarker data were analyzed using machine learning methods to assess the risk of several common diseases, in order to summarize the potential applications of data mining methods in clinical diagnosis. Data mining techniques have now been increasingly applied to clinical diagnostics, and they have the potential to support this field.

Profile-based assessment of diseases affective factors using fuzzy association rule mining approach: A case study in heart diseases

The existing data mining solutions to identify risk factors associated with diseases are burdened with quite a few shortcomings. They usually use crisp partitions for numerical features and also do not use patient-specific profiles. These shortcomings create limitations for solving real problems. Discretizing a numerical feature through crisp partitions can also generate substantial partitioning errors, particularly for features whose values are closer to crisp boundaries. Since the normal range of each numerical feature varies according to the age, gender, and medical conditions of the patients, then ignoring these differences can undermine the accuracy of the extracted itemsets and rules. This paper presents a profile-based fuzzy association rule mining (PB-FARM) approach for the assessment of risk factors highly correlated with diseases. The proposed approach has three phases. Phase I involves creating profiles for patients based on their age, gender, and medical conditions, to determine a normal range of each numerical feature. Then fuzzy partitioning is done for all features (namely, numerical and categorical), and consequently, a structure, called FirstScan, is created. In Phase II, the FirstScan structure is utilized to mine for large fuzzy k-itemsets. Ultimately, in Phase III, the given k-itemsets are employed to generate fuzzy rules for associations between risk factors and diseases. To evaluate the performance of the proposed method the Z-Alizadeh Sani coronary artery disease (CAD) dataset, containing 303 records and 54 features, was used. The results show a positive correlation between typical chest pain and old age with the incidence of CAD. The comparisons made in this study showed that, firstly, the proposed algorithm has a higher partitioning accuracy than other methods, and secondly, it has a reasonably short execution time.

Advances of ECG Sensors from Hardware, Software and Format Interoperability Perspectives

It is well-known that cardiovascular disease is one of the major causes of death worldwide nowadays. Electrocardiogram (ECG) sensor is one of the tools commonly used by cardiologists to diagnose and detect signs of heart disease with their patients. Since fast, prompt and accurate interpretation and decision is important in saving the life of patients from sudden heart attack or cardiac arrest, many innovations have been made to ECG sensors. However, the use of traditional ECG sensors is still prevalent in the clinical settings of many medical institutions. This article provides a comprehensive survey on ECG sensors from hardware, software and data format interoperability perspectives. The hardware perspective outlines a general hardware architecture of an ECG sensor along with the description of its hardware components. The software perspective describes various techniques (denoising, machine learning, deep learning, and privacy preservation) and other computer paradigms used in the software development and deployment for ECG sensors. Finally, the format interoperability perspective offers a detailed taxonomy of current ECG formats and the relationship among these formats. The intention is to help researchers towards the development of modern ECG sensors that are suitable and approved for adoption in real clinical settings.

Coronary artery disease detection using artificial intelligence techniques: A survey of trends, geographical differences and diagnostic features 1991-2020

While coronary angiography is the gold standard diagnostic tool for coronary artery disease (CAD), but it is associated with procedural risk, it is an invasive technique requiring arterial puncture, and it subjects the patient to radiation and iodinated contrast exposure. Artificial intelligence (AI) can provide a pretest probability of disease that can be used to triage patients for angiography. This review comprehensively investigates published papers in the domain of CAD detection using different AI techniques from 1991 to 2020, in order to discern broad trends and geographical differences. Moreover, key decision factors affecting CAD diagnosis are identified for different parts of the world by aggregating the results from different studies. In this study, all datasets that have been used for the studies for CAD detection, their properties, and achieved performances using various AI techniques, are presented, compared, and analyzed. In particular, the effectiveness of machine learning (ML) and deep learning (DL) techniques to diagnose and predict CAD are reviewed. From PubMed, Scopus, Ovid MEDLINE, and Google Scholar search, 500 papers were selected to be investigated. Among these selected papers, 256 papers met our criteria and hence were included in this study. Our findings demonstrate that AI-based techniques have been increasingly applied for the detection of CAD since 2008. AI-based techniques that utilized electrocardiography (ECG), demographic characteristics, symptoms, physical examination findings, and heart rate signals, reported high accuracy for the detection of CAD. In these papers, the authors ranked the features based on their assessed clinical importance with ML techniques. The results demonstrate that the attribution of the relative importance of ML features for CAD diagnosis is different among countries. More recently, DL methods have yielded high CAD detection performance using ECG signals, which drives its burgeoning adoption.

lncRNA-MALAT1 expression in patients with coronary atherosclerosis and its predictive value for in-stent restenosis

This study was designed to investigate the long non-coding RNA (lncRNA)-metastasis associated lung adenocarcinoma transcript 1 (MALAT1) expression in patients with coronary atherosclerosis and its predictive value for in-stent restenosis. Ninety-five patients with coronary heart disease who came to our hospital for treatment and underwent stent implantation were selected as a research group (RG), and 95 volunteers undergoing physical examination who did not suffer from coronary heart disease during the same period were selected as a control group (CG). MALAT1 of subjects in both groups before and after treatment were detected by RT-qPCR, and N-terminal pro-brain natriuretic peptide (NT-proBNP), high sensitivity C-reactive protein (hs-CRP), lactate dehydrogenase (LDH), and creatine kinase isoenzyme (CK-MB) of them in the RG before treatment were detected. The level was evaluated and detected, and its correlation with MALAT1 was analyzed. Then, the predictive value of MALAT1 for in-stent restenosis in patients with coronary heart disease was analyzed. MALAT1 expression in patients with coronary heart disease was higher than that of normal subjects (P<0.05); after treatment, the expression levels of MALAT1, NT-proBNP, hs-CRP, LDH, and CK-MB in the serum of patients were significantly lower than those before treatment (P<0.05); MALAT1 expression was positively correlated with the expression levels of NT-proBNP, hs-CRP, LDH, and CK-MB (P<0.05). Receiver operating characteristic of MALAT1 for predicting in-stent restenosis in patients with coronary heart disease was over 0.8; the number of lesions, MALAT1, diabetes, NT-proBNP and hs-CRP were independent risk factors for in-stent restenosis. MALAT1 is highly expressed in the serum of patients with coronary heart disease, and it has high value in its diagnosis and the prediction of in-stent restenosis. It is also an independent risk factor for in-stent restenosis in patients with coronary heart disease.

Association of myeloperoxidase, homocysteine and high-sensitivity C-reactive protein with the severity of coronary artery disease and their diagnostic and prognostic value

In the present study, the association between the severity of coronary artery disease (CAD) and myeloperoxidase (MPO), homocysteine (Hcy) and high-sensitivity C-reactive protein (hs-CRP) was assessed and their diagnostic and prognostic value was determined. A total of 112 patients with CAD [patient group (PG)] and 112 healthy participants who visited the hospital for physical examinations [control group (CG)] were enrolled in the present study. The plasma levels of MPO, Hcy and hs-CRP were compared between the two groups. According to the arteriography results, the patients were further divided into the single-vessel disease group (SVG), double-vessel disease group (DVG) and multi-vessel disease group (MVG). The Gensini scores of the three groups were evaluated according to the Gensini score standard. The correlations between the expression of MPO, Hcy or hs-CRP and the Gensini score of the PG were analyzed. The patients' major adverse cardiovascular event (MACEs) were recorded over 6 months and compared, and the predictive values of MPO, Hcy and hs-CRP regarding MACEs were determined by receiver operating characteristics analysis. The results indicated that the levels of MPO, Hcy and hs-CRP in the PG were higher than those in the CG (P<0.05). The Gensini score and the expression of MPO, Hcy and hs-CRP in the MVG were higher than those in the SVG and the DVG, and the Gensini score and the expression of MPO, Hcy and hs-CRP in the DVG were higher than those in the SVG (P<0.05). There was a positive correlation between the Gensini score and the expression of MPO (r=0.814, P<0.05), Hcy (r=0.774, P<0.05) and hs-CRP (r=0.765, P<0.05) in the PG. The total incidence of MACEs in patients with multiple lesions was significantly higher than that in patients with double and single lesions (P<0.05). The total incidence of MACEs in the MVG group was higher than that in the SVG and the DVG, and the total incidence of MACEs in the DVG was higher than that in the SVG (P<0.05). The area under the curve (AUC) and sensitivity for MPO levels to predict MACEs were higher than those of Hcy and hs-CRP (P<0.05); however, there was no significant difference in the AUC and sensitivity of Hcy and hs-CRP for predicting MACEs (P<0.05). The specificity of hs-CRP for predicting MACEs was higher than that of MPO and Hcy (P<0.05). The number of lesions, hypertension, diabetes, MPO, Hys and hs-CRP were determined to be independent risk factors for MACEs. In conclusion, for patients with CAD, elevated plasma levels of MPO, Hcy and hs-CRP were directly correlated with the severity of CAD and the risk of MACEs. Furthermore, MPO, Hcy and hs-CRP may effectively predict MACEs and are of important clinical significance in terms of judging the condition and improving the prognosis for patients with CAD.

The classification of public research organizations: Taxonomical explorations

This article addresses, conceptually and empirically, the classification of public research organizations (PROs) understood as non-university and non-enterprise research-focused organizations that are public by nature or in which the government has an influence. The construction of archetypes of research performing organizations has been a standard method of analysis, as reflected in the Frascati Manual that guides national statistical offices to delineate the perimeter of the institutional sector of PROs. However, this practice has often overlooked the emergence of new types because traditional approaches to classification tend to characterize previously defined mutually exclusive categories, rather than allow evidence to reveal categories ex-post. This gives rise to a number of concerns related to the scientific validity of the classification of entities in the organizational field of research. The present article discusses conceptual and methodological issues associated with different classificatory strategies. It also presents the empirical results of a taxonomical exploration that allows the identification of categories not determined ex-ante. Our empirical strategy consists in applying clustering techniques on a number of organizational dimensions, chosen based on theoretical grounds and proxied by variables determined by data availability. We implement it on a pilot dataset of 197 research-focused organizations from eight different European countries.

Analysis for warning factors of type 2 diabetes mellitus complications with Markov blanket based on a Bayesian network model

BACKGROUND AND OBJECTIVE

Type 2 diabetes mellitus (T2DM) complications seriously affect the quality of life and could not be cured completely. Actions should be taken for prevention and self-management. Analysis of warning factors is beneficial for patients, on which some previous studies focused. They generally used the professional medical test factors or complete factors to predict and prevent, but it was inconvenient and impractical for patients to self-manage. With this in mind, this study built a Bayesian network (BN) model, from the perspective of diabetic patients' self-management and prevention, to predict six complications of T2DM using the selected warning factors which patients could have access from medical examination. Furthermore, the model was analyzed to explore the relationships between physiological variables and T2DM complications, as well as the complications themselves. The model aims to help patients with T2DM self-manage and prevent themselves from complications.

METHODS

The dataset was collected from a well-known data center called the National Health Clinical Center between 1st January 2009 and 31st December 2009. After preprocess and impute the data, a BN model merging expert knowledge was built with Bootstrap and Tabu search algorithm. Markov Blanket (MB) was used to select the warning factors and predict T2DM complications. Moreover, a Bayesian network without prior information (BN-wopi) model learned using 10-fold cross-validation both in structure and in parameters was added to compare with other classifiers learned using 10-fold cross-validation fairly. The warning factors were selected according the structure learned in each fold and were used to predict. Finally, the performance of two BN models using warning features were compared with Naïve Bayes model, Random Forest model, and C5.0 Decision Tree model, which used all features to predict. Besides, the validation parameters of the proposed model were also compared with those in existing studies using some other variables in clinical data or biomedical data to predict T2DM complications.

RESULTS

Experimental results indicated that the BN models using warning factors performed statistically better than their counterparts using all other variables in predicting T2DM complications. In addition, the proposed BN model were effective and significant in predicting diabetic nephropathy (DN) (AUC: 0.831), diabetic foot (DF) (AUC: 0.905), diabetic macrovascular complications (DMV) (AUC: 0.753) and diabetic ketoacidosis (DK) (AUC: 0.877) with the selected warning factors compared with other experiments.

CONCLUSIONS

The warning factors of DN, DF, DMV, and DK selected by MB in this research might be able to help predict certain T2DM complications effectively, and the proposed BN model might be used as a general tool for prevention, monitoring, and self-management.

New hybrid method for heart disease diagnosis utilizing optimization algorithm in feature selection

The number and size of medical databases are rapidly increasing, and the advanced models of data mining techniques could help physicians to make efficient and applicable decisions. The challenges of heart disease data include the feature selection, the number of the samples; imbalance of the samples, lack of magnitude for some features, etc. This study mainly focuses on the feature selection improvement and decreasing the numbers of the features. In this study, imperialist competitive algorithm with meta-heuristic approach is suggested in order to select prominent features of the heart disease. This algorithm can provide a more optimal response for feature selection toward genetic in compare with other optimization algorithms. Also, the K-nearest neighbor algorithm is used for the classification. Evaluation result shows that by using the proposed algorithm, the accuracy of feature selection technique has been improved.

Inflammation-Related MicroRNAs Are Associated with Plaque Stability Calculated by IVUS in Coronary Heart Disease Patients

Objectives

This study aimed to investigate the association between inflammation-related microRNAs (miR-21, 146a, 155) and the plaque stability in coronary artery disease patients.

Methods

The expression of miR-21, 146a, and 155 was measured by real-time PCR in 310 consecutive patients. The level of hs-CRP, IL-6, and IL-8 was measured by ELISA. The plaque stability of coronary stenotic lesions was evaluated with intravascular ultrasound (IVUS).

Results

(1) The levels of hs-CRP, IL-6, and IL-8 were significantly increased in the UAP and AMI groups compared with the CPS group (P < 0.01). (2) The expression of miR-21 and miR-146a in peripheral blood mononuclear cells (PBMCs) and plasma was significantly higher in CAD patients compared with non-CAD patients, whereas the miR-155 expression in PBMCs and plasma was significantly lower in patients with CAD. (3) The miR-21 expression in PBMCs was higher in UAP and AMI groups compared with CPS group. The miR-146a expression in PBMCs was higher in SAP, UAP, and AMI groups than in CPS group. Although the level of miR-155 in PBMCs was lower in SAP, UAP, and AMI groups than in CPS group. The expression patterns of miR-21, miR-146a, and miR-155 in plasma were consistent with those of PBMCs. (4) The expressions of miR-21 and miR-146a in PBMCs and plasma were significantly higher in the vulnerable plaque group than those in stable plaque group. While miR-155 in PBMCs and plasma was significantly lower in vulnerable plaque group compared with stable plaque group. (5) The levels of miR-21 and miR-146a in PBMCs and plasma were significantly higher in soft plaque group than in fibrous plaque group and calcified plaque group. However, miR-155 in PBMCs and plasma was significantly lower in soft plaque group.

Conclusions

The expression of miR-21 and miR-146a are associated with the plaque stability in coronary stenotic lesions, whereas miR-155 expression is inversely associated with the plaque stability.

C-Reactive Protein Gene Variants in Depressive Symptoms & Antidepressants Efficacy

OBJECTIVE

Although the pathogenesis of depression remains unclear, C-reactive protein (CRP) levels are commonly elevated in depressed patients. Thus, CRP single-nucleotide polymorphisms (SNPs) that influence CRP levels may be associated with depression. In the present study, we explored whether CRP SNPs are related to depressive symptoms and antidepressants efficacy in Han Chinese patients.

METHODS

We analyzed data from 440 patients with first-episode depression. We obtained genome CRP SNPs, scores of the 17-item Hamilton Rating Scale for Depression 17 (HAMD17) and its four-factor at baseline and after 6 weeks. Quantitative trait analysis was performed using UNPHASED software and curative effects were analyzed using SPSS software.

RESULTS

Male patients with SNP rs1800947G exhibited lower insomnia scores and rs2794521CC exhibited lower scores of anxiety/ physical symptoms, total HAMD17 score. Female patients with rs2794521TT exhibited higher scores of insomnia and lower antidepressants efficacy.

CONCLUSION

CRP SNPs rs1800947 and rs2794521 may be associated with depressive symptoms in patients with depression in a sexspecific fashion. Furthermore, rs2794521 may be a predictor of the efficacy of antidepressants in female patients.

A network pharmacology integrated pharmacokinetics strategy for uncovering pharmacological mechanism of compounds absorbed into the blood of Dan-Lou tablet on coronary heart disease

ETHNOPHARMACOLOGICAL RELEVANCE

Dan-Lou tablet (DLT) is developed from the traditional Chinese medicine (TCM) formula Gualou Xiebai Baijiu Tang which has been used for at least 2000 years in China. DLT has been widely used in clinical practice to treat cardiovascular diseases.

AIM OF THE STUDY

This study aimed to uncover the pharmacological mechanism of the compounds absorbed into the blood of Dan-Lou tablet (DLT) on coronary heart disease (CHD) using a network pharmacology integrated pharmacokinetics strategy.

MATERIALS AND METHODS

A rapid and sensitive method was developed for the simultaneous determination of the six compounds (puerarin, formononetin, calycosin, paeoniflorin, cryptotanshinone and tanshinone IIA) in rat plasma by liquid chromatography tandem mass spectrometry (LC-MS/MS). Then, the pharmacology network was established based on the relationship between five compounds absorbed into the blood targets (puerarin, formononetin, calycosin, cryptotanshinone and tanshinone IIA) and CHD targets.

RESULTS

The intra-and inter-day precision were less than 11% and the accuracy ranged from 88.2% to 112%, which demonstrated that the LC-MS/MS method could be used to evaluate the pharmacokinetic feature of the six compounds in rats after oral administration of DLT. The pathway enrichment analysis revealed that the significant bioprocess networks of DLT on CHD were positive regulation of estradiol secretion, negative regulation of transcription from RNA polymerase II promoter, lipopolysaccharide-mediated signaling pathway and cytokine activity.

CONCLUSION

The proposed network pharmacology integrated pharmacokinetics strategy provides a combination method to explore the therapeutic mechanism of the compounds absorbed into the blood of multi-component drugs on a systematic level.

Determinants of the magnitude of response to vitamin D supplementation in adolescent girls identified using a decision tree algorithm

Vitamin D (VitD) supplementation is an inexpensive and effective approach for improving VitD insufficiency/deficiency. However, the response to supplementation, with respect to the increase in serum 25(OH)D level varies between individuals. In this study, we have assessed the factors associated with the response to VitD supplementation using a decision-tree algorithm. Serum VitD levels, pre- and post-VitD supplementation was used as the determinant of responsiveness. The model was validated by constructing a receiver operating characteristic curve. Serum VitD at baseline levels was at the apex of the tree in our model, followed by serum low-density lipoprotein cholesterol and triglyceride, age, waist-hip ratio, and high-density lipoprotein cholesterol. Our model suggests that these determinants of responsiveness to VitD supplementation had sensitivity, specificity, and accuracy, 59.4, 75.8 and 69.3%, respectively. The decision tree model appears to be a relatively accurate, specific, and sensitive approach for identifying the factors associated with response to VitD supplementation.

Machine learning-based coronary artery disease diagnosis: A comprehensive review

Coronary artery disease (CAD) is the most common cardiovascular disease (CVD) and often leads to a heart attack. It annually causes millions of deaths and billions of dollars in financial losses worldwide. Angiography, which is invasive and risky, is the standard procedure for diagnosing CAD. Alternatively, machine learning (ML) techniques have been widely used in the literature as fast, affordable, and noninvasive approaches for CAD detection. The results that have been published on ML-based CAD diagnosis differ substantially in terms of the analyzed datasets, sample sizes, features, location of data collection, performance metrics, and applied ML techniques. Due to these fundamental differences, achievements in the literature cannot be generalized. This paper conducts a comprehensive and multifaceted review of all relevant studies that were published between 1992 and 2019 for ML-based CAD diagnosis. The impacts of various factors, such as dataset characteristics (geographical location, sample size, features, and the stenosis of each coronary artery) and applied ML techniques (feature selection, performance metrics, and method) are investigated in detail. Finally, the important challenges and shortcomings of ML-based CAD diagnosis are discussed.

1-Deoxynojirimycin in Mulberry (Morus indica L.) Leaves Ameliorates Stable Angina Pectoris in Patients With Coronary Heart Disease by Improving Antioxidant and Anti-inflammatory Capacities

Objective: Stable angina pectoris (SAP) in patients with coronary heart disease (CHD) and blood stasis syndrome (BSS) is a potentially serious threat to public health. NF-κB signaling is associated with angina pectoris. 1-Deoxynojirimycin (DNJ), which is a unique polyhydroxy alkaloid, is the main active component in mulberry (Morus indica L.) leaves and may exhibit protective properties in the prevention of SAP in patients with CHD by affecting the NF-κB pathway.

Methods: DNJ was purified from mulberry leaves by using a pretreated cation exchange chromatography column. A total of 144 SAP patients were randomly and evenly divided into experimental (DNJ treatment) and control (conventional treatment) groups. Echocardiography and ascending aortic elasticity were evaluated. The changes in inflammatory, oxidative, and antioxidant factors, including C-reactive protein (CRP), interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), superoxide dismutase (SOD), and malondialdehyde (MDA), were measured before and after a 4-week treatment. Self-Rating Anxiety Scale (SAS) and Hamilton Depression Scale (HAMD) scores were compared between the two groups. The improvement in SAP score, associated symptoms, and BSS was also investigated. The levels of IkB kinase (IKK), nuclear factor-kappa B (NF-κB), and inhibitor of kappa B α (IkBα) were measured by Western blot.

Results: After the 4-week treatment, DNJ increased left ventricular ejection fraction and reduced left ventricular mass index, aortic distensibility, and atherosclerosis index (p < 0.05). DNJ intervention increased angina-free walking distance (p < 0.05). DNJ significantly reduced the levels of hs-CRP, IL-6, TNF-a, MDA, SAS, HAMD, AP, and BSS scores and increased SOD level (p < 0.05). The total effective rate was significantly increased (p < 0.05). The symptoms of angina attack frequency, nitroglycerin use, chest pain and tightness, shortness of breath, and emotional upset were also improved. DNJ reduced IKK and NF-κB levels and increased IkBα level (p < 0.05).

Conclusion: The DNJ in mulberry leaves improved the SAP of patients with CHD and BSS by increasing their antioxidant and anti-inflammatory capacities.