Predictive value of bilirubin and serum γ-glutamyltranspeptidase levels in type-2 diabetes mellitus patients with acute coronary syndrome.

BACKGROUND: Cardiovascular disease is a major complication of diabetes mellitus (DM). Type-2 DM (T2DM) is associated with an increased risk of cardiovascular events and mortality, while serum biomarkers may facilitate the prediction of these outcomes. Early differential diagnosis of T2DM complicated with acute coronary syndrome (ACS) plays an important role in controlling disease progression and improving safety. AIM: To investigate the correlation of serum bilirubin and γ-glutamyltranspeptidase (γ-GGT) with major adverse cardiovascular events (MACEs) in T2DM patients with ACS. METHODS: The clinical data of inpatients from January 2022 to December 2022 were analyzed retrospectively. According to different conditions, they were divided into the T2DM complicated with ACS group (T2DM + ACS, n = 96), simple T2DM group (T2DM, n = 85), and simple ACS group (ACS, n = 90). The clinical data and laboratory indices were compared among the three groups, and the correlations of serum total bilirubin (TBIL) levels and serum γ-GGT levels with other indices were discussed. T2DM + ACS patients received a 90-day follow-up after discharge and were divided into event (n = 15) and nonevent (n = 81) groups according to the occurrence of MACEs; Univariate and multivariate analyses were further used to screen the independent influencing factors of MACEs in patients. RESULTS: The T2DM + ACS group showed higher γ-GGT, total cholesterol, low-density lipoprotein cholesterol (LDL-C) and glycosylated hemoglobin (HbA1c) and lower TBIL and high-density lipoprotein cholesterol levels than the T2DM and ACS groups (P < 0.05). Based on univariate analysis, the event and nonevent groups were significantly different in age (t = 3.3612, P = 0.0011), TBIL level (t = 3.0742, P = 0.0028), γ-GGT level (t = 2.6887, P = 0.0085), LDL-C level (t = 2.0816, P = 0.0401), HbA1c level (t = 2.7862, P = 0.0065) and left ventricular ejection fraction (LEVF) levels (t=3.2047, P = 0.0018). Multivariate logistic regression analysis further identified that TBIL level and LEVF level were protective factor for MACEs, and age and γ-GGT level were risk factors (P < 0.05). CONCLUSION: Serum TBIL levels are decreased and γ-GGT levels are increased in T2DM + ACS patients, and the two indices are significantly negatively correlated. TBIL and γ-GGT are independent influencing factors for MACEs in such patients.

Inhibition Effect of Pancreatic Exocrine Insufficiency on Immune Checkpoint Inhibitor Treatment in Pancreatic Cancer: A Retrospective Study.

Introduction: Chemotherapy combined with immune checkpoint inhibitors (ChIM) is used to treat advanced pancreatic ductal adenocarcinoma (PDAC). However, the efficacy of ChIM is similar to that of chemotherapy alone. Methods: To assess potential factors affecting the effectiveness of ChIM, we analyzed the clinical data of 359 patients with PDAC who visited the hospital during June 2017 to December 2022. Results: Surgical resection, diabetes, and ChIM were risk factors for pancreatic exocrine insufficiency (PEI). The adjusted odds ratio of ChIM was 2.63 (95% confidence interval (CI) 1.492-4.626) (P = 0.001). The incidence of PEI in the ChIM group (76.9%) was significantly higher than that of the chemotherapy group (60.2%) (P = 0.004). Survival analysis showed that ChIM did not improve the survival rate of patients with PDAC (hazard ratio (HR) 0.92, 0.707-1.197) (P = 0.534) in comparison with that of the chemotherapy group. However, in patients without PEI, those receiving ChIM showed a higher 1-year overall survival (OS) rate of 70.8% (two-sided, P = 0.045) and a median OS of 22.0 months (95% CI 11.5-32.5). Moreover, pancreatic enzyme replacement therapy significantly improved the OS of patients with PDAC (HR = 0.73, 95% CI = 0.561-0.956) (P = 0.022). Conclusion: Immune checkpoint inhibitors (ICIs) increased the incidence of PEI in patients with PDAC. The OS was not different between patients receiving chemotherapy and ChIM due to irregular PERT treatment. The finding show that pancreatic enzyme replacement therapy may improve the response rate of patients with PDAC to ICIs.

Discrimination of benign and malignant breast lesions on dynamic contrast-enhanced magnetic resonance imaging using deep learning.

PURPOSE: To evaluate the capability of deep transfer learning (DTL) and fine-tuning methods in differentiating malignant from benign lesions in breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). METHODS: The diagnostic efficiencies of the VGG19, ResNet50, and DenseNet201 models were tested under the same dataset. The model with the highest performance was selected and modified utilizing three fine-tuning strategies (S1-3). Fifty additional lesions were selected to form the validation set to verify the generalization abilities of these models. The accuracy (Ac) of the different models in the training and test sets, as well as the precision (Pr), recall rate (Rc), F1 score (), and area under the receiver operating characteristic curve (AUC), were primary performance indicators. Finally, the kappa test was used to compare the degree of agreement between the DTL models and pathological diagnosis in differentiating malignant from benign breast lesions. RESULTS: The Pr, Rc, f1, and AUC of VGG19 (86.0%, 0.81, 0.81, and 0.81, respectively) were higher than those of DenseNet201 (70.0%, 0.61, 0.63, and 0.61, respectively) and ResNet50 (61.0%, 0.59, 0.59, and 0.59). After fine-tuning, the Pr, Rc, f1, and AUC of S1 (87.0%, 0.86, 0.86, and 0.86, respectively) were higher than those of VGG19. Notably, the degree of agreement between S1 and pathological diagnosis in differentiating malignant from benign breast lesions was 0.720 (κ = 0.720), which was higher than that of DenseNet201 (κ = 0.440), VGG19 (κ = 0.640), and ResNet50 (κ = 0.280). CONCLUSION: The VGG19 model is an effective method for identifying benign and malignant breast lesions on DCE-MRI, and its performance can be further improved via fine-tuning. Overall, our findings insinuate that this technique holds potential clinical application value.

NEK8 regulates colorectal cancer progression via phosphorylating MYC.

Radiotherapy and chemotherapy remain the mainstay of treatment for colorectal cancer (CRC), although their efficacy is limited. A detailed understanding of the molecular mechanisms underlying CRC progression could lead to the development of new therapeutic strategies. Although it has been established that MYC signaling is dysregulated in various human cancers, direct targeting MYC remains challenging due to its "undruggable" protein structure. Post-translational modification of proteins can affect their stability, activation, and subcellular localization. Hence, targeting the post-translational modification of MYC represents a promising approach to disrupting MYC signaling. Herein, we revealed that NEK8 positively regulates CRC progression by phosphorylating c-MYC protein at serine 405, which exhibited enhanced stability via polyubiquitination. Our findings shed light on the role of NEK8/MYC signaling in CRC progression, offering a novel and helpful target for colorectal cancer treatment. Video Abstract.

Artificial Intelligence and Big Data Technologies in the Construction of Surgical Risk Prediction Model for Patients with Coronary Artery Bypass Grafting.

The objective of this work was to predict the risk of mortality rate in patients with coronary artery bypass grafting (CABG) based on the risk prediction model of CABG using artificial intelligence (AI) and big data technologies. The clinical data of 2,364 patients undergoing CABG in our hospital from January 2019 to August 2021 were collected in this work. Based on AI and big data technology, business requirement analysis, system requirement analysis, complication prediction module, big data mining technology, and model building are carried out, respectively; the successful CABG risk prediction system includes case feature analysis service, risk warning service, and case retrieval service. The commonly used precision, recall, and F1-score were adopted to evaluate the quality of the gradient-boosted tree (GBT) model. The analysis proved that the GBT model was the best in terms of precision, F1-score, and area under the receiver operating characteristic curve (ROC). According to the CABG risk prediction model, 1,382 patients had a score of <0, 463 patients had a score of 0 ≤ score ≤ 2, 252 patients had a score of 2 < score ≤ 5, and 267 patients had a score of >5, which were stratified into four groups: A, B, C, and D. The actual number of in-hospital deaths was 25, and the in-hospital mortality rate was 1.05%. The mortality rate predicted by the CABG risk prediction model was 2.67 ± 1.82% (95% confidential interval (CI) (2.87-2.98)), which was higher than the actual value. The CABG risk prediction model showed the credible results only in group B with AUC = 0.763 > 0.7. In group B, 3 patients actually died, the actual mortality rate was 0.33%, and the predicted mortality rate was 0.96 ± 0.78 (95% CI (0.82-0.87)), which overestimated the mortality rate of patients in group B. It successfully constructed a CABG risk prediction model based on the AI and big data technologies, which would overestimate the mortality of patients with intermediate risk, and it is suitable for different types of heart diseases through continuous research and development and innovation, and provides clinical guidance value.

Prognostic Significance of Myocardial Blood Flow Quantification for Major Adverse Cardiac Events: A Systematic Review and Meta-analysis.

Chronic coronary syndromes involve reduced myocardial blood flow (MBF). MBF is a reliable predictor of outcomes, independent of the presence of significant stenosis. Whether MBF can predict major adverse cardiac events (MACE) during long-term follow-up is unknown. PubMed, Embase, Cochrane, CNKI, and WANFANG were searched for papers published up to January 2021. The exposure was the incremental unit of stress MBF (mL/g/min) or low MBF versus high MBF. The imaging examinations included positron emission tomography/computed tomography and coronary magnetic resonance. The study outcome was the occurrence of MACE during follow-up, summarized as time-to-event hazard ratios (HRs) and 95% confidence intervals (CIs). Six studies (300 MACEs in 2326 patients) were included. Four studies presented stress MBF data by unit increments. The pooled HR showed that an increase in stress MBF by 1 mL/g/min is a protective factor for MACE (HR = 0.32; 95% CI, 0.18-0.57; I2 = 62.9%, Pheterogeneity = 0.044). Two studies reported stress MBF as low/high. The results showed that a high-stress MBF was protective against MACEs (HR = 0.43; 95% CI, 0.24-0.78; I2 = 39.5%, Pheterogeneity = 0.199). Quantification of stress MBF using positron emission tomography/computed tomography and coronary magnetic resonance might have incremental predictive value for future MACEs in a population at intermediate to high cardiovascular risk. The results will require validation in large prospective randomized controlled trials.

A clinical-radiomics based nomogram to predict progressive intraparenchymal hemorrhage in mild to moderate traumatic injury patients.

PURPOSE: To develop a non-contrast computed tomography(NCCT)based radiomics model for predicting intraparenchymal hemorrhage progression in patients with mild to moderate traumatic brain injury(TBI). METHODS: We retrospectively analyzed 166 mild to moderate TBI patients with intraparenchymal hemorrhage from January 2018 to December 2021. The enrolled patients were divided into training cohort and test cohort with a ratio of 6:4. Uni- and multivariable logistic regression analyses were implemented to screen clinical-radiological factors and to establish a clinical-radiological model. Model performance was evaluated by the area under the receiver operating characteristic curve (AUC), the calibration curve, the decision curve analysis, sensitivity, and specificity. RESULTS: Eleven radiomics features, presence with SDH, and D-dimer > 5 mg/l were selected to construct the combined clinical-radiomic model for the prediction of TICH in mild to moderate TBI patients. The AUC of the combined model was 0.81(95% confidence interval (CI), 0.72 to 0.90) in the training cohort and 0.88 (95% CI 0.79 to 0.96) in the test cohort, which were superior to the clinical model alone (AUCtraining = 0.72, AUCtest = 0.74). The calibration curve demonstrated that the radiomics nomogram had a good agreement between prediction and observation. Decision curve analysis confirmed clinically useful. CONCLUSIONS: The combined clinical-radiomic model that incorporates the radiomics score and clinical risk factors can serve as a reliable and powerful tool for Predicting intraparenchymal hemorrhage progression for patients with mild to moderate TBI.

Prediction of myocardial ischemia in coronary heart disease patients using a CCTA-Based radiomic nomogram.

Objective: The present study aimed to predict myocardial ischemia in coronary heart disease (CHD) patients based on the radiologic features of coronary computed tomography angiography (CCTA) combined with clinical factors. Methods: The imaging and clinical data of 110 patients who underwent CCTA scan before DSA or FFR examination in Changzhou Second People's Hospital, Nanjing Medical University (90 patients), and The First Affiliated Hospital of Soochow University (20 patients) from March 2018 to January 2022 were retrospectively analyzed. According to the digital subtraction angiography (DSA) and fractional flow reserve (FFR) results, all patients were assigned to myocardial ischemia (n = 58) and normal myocardial blood supply (n = 52) groups. All patients were further categorized into training (n = 64) and internal validation (n = 26) sets at a ratio of 7:3, and the patients from second site were used as external validation. Clinical indicators of patients were collected, the left ventricular myocardium were segmented from CCTA images using CQK software, and the radiomics features were extracted using pyradiomics software. Independent prediction models and combined prediction models were established. The predictive performance of the model was assessed by calibration curve analysis, receiver operating characteristic (ROC) curve and decision curve analysis. Results: The combined model consisted of one important clinical factor and eight selected radiomic features. The area under the ROC curve (AUC) of radiomic model was 0.826 in training set, and 0.744 in the internal validation set. For the combined model, the AUCs were 0.873, 0.810, 0.800 in the training, internal validation, and external validation sets, respectively. The calibration curves demonstrated that the probability of myocardial ischemia predicted by the combined model was in good agreement with the observed values in both training and validation sets. The decision curve was within the threshold range of 0.1-1, and the clinical value of nomogram was higher than that of clinical model. Conclusion: The radiomic characteristics of CCTA combined with clinical factors have a good clinical value in predicting myocardial ischemia in CHD patients.

Upper-Limb Amputation Disrupts the Interhemispheric Structural Rather than Functional Connectivity.

Background: Recent neuroimaging studies on upper-limb amputation have revealed the reorganization of bilateral sensorimotor cortex after sensory deprivation, underpinning the assumption of changes in the interhemispheric connections. In the present study, using functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI), we aim to explore the alterations in the interhemispheric functional and structural connectivity after upper-limb amputation. Methods: Twenty-two upper-limb amputees and 15 age- and sex-matched healthy controls were recruited for MRI scanning. The amputees were further divided into subgroups by amputation side and residual limb pain (RLP). DTI metrics of corpus callosum (CC) subregions and resting-state functional connectivity (FC) between the bilateral sensorimotor cortices were measured for each participant. Linear mixed models were carried out to investigate the relationship of interhemispheric connectivity with the amputation, amputation side, and RLP. Results: Compared with healthy controls, upper-limb amputees showed lower axial diffusivity (AD) in CC subregions II and III. Subgroup analyses showed that the dominant hand amputation induced significant microstructural changes in CC subregion III. In addition, only amputees with RLP showed decreased fractional anisotropy and AD in CC, which was also correlated with the intensity of RLP. No significant changes in interhemispheric FC were found after upper-limb amputation. Conclusion: The present study demonstrated that the interhemispheric structural connectivity rather than FC degenerated after upper-limb amputation, and the degeneration of interhemispheric structural connectivity was shown to be relevant to the amputation side and the intensity of RLP. Impact statement Neuroimaging studies have revealed the functional reorganization of bilateral sensorimotor cortex after amputation, with expanded activation from the intact hemisphere to the deprived hemisphere. Our findings indicated a degeneration of interhemispheric white matter connections in upper-limb amputees, unveiling the underlying structural basis for bilateral functional reorganization after amputation.

The correlation of pericoronary adipose tissue with coronary artery disease and left ventricular function.

OBJECTIVE: We sought to investigate the correlation of pericoronary adipose tissue with coronary artery disease and left ventricular (LV) function. METHODS: Participants with clinically suspected coronary artery disease were enrolled. All participants underwent coronary computed tomography angiography (CCTA) and echocardiography followed by invasive coronary angiography (ICA) within 6 months. Pericoronary adipose tissue (PCAT) was extracted to analyze the correlation with the Gensini score and LV function parameters, including IVS, LVPW, LVEDD, LVESD, LVEDV, LVESV, FS, LVEF, LVM, and LVMI. The correlation between PCAT and the Gensini score was assessed using Spearman's correlation analysis, and that between the PCAT volume or FAI and LV function parameters was determined using partial correlation analysis. RESULTS: One hundred and fifty-nine participants (mean age, 64.55 ± 10.64 years; men, 65.4% [104/159]) were included in the final analysis. Risk factors for coronary artery disease, such as hypertension, diabetes, dyslipidemia, and a history of smoking or drinking, had no significant association with PCAT (P > 0.05), and there was also no correlation between PCAT and the Gensini score. However, the LAD-FAI was positively correlated with the IVS (r = 0.203, P = 0.013), LVPW (r = 0.218, P = 0.008), LVEDD (r = 0.317, P < 0.001), LVESD (r = 0.298, P < 0.001), LVEDV (r = 0.317, P < 0.001), LVESV (r = 0.301, P < 0.001), LVM (r = 0.371, P < 0.001), and LVMI (r = 0.304, P < 0.001). Also, the LCX-FAI was positively correlated with the LVEDD (r = 0.199, P = 0.015), LVESD (r = 0.190, P = 0.021), LVEDV (r = 0.203, P = 0.013), LVESV (r = 0.197, P = 0.016), LVM (r = 0.220, P = 0.007), and LVMI (r = 0.172, P = 0.036), and the RCA-FAI was positively correlated with the LVEDD (r = 0.258, P = 0.002), LVESD (r = 0.238, P = 0.004), LVEDV (r = 0.266, P = 0.001), LVESV (r = 0.249, P = 0.002), LVM (r = 0.237, P = 0.004), and LVMI (r = 0.218, P = 0.008), respectively. Finally, the total volume was positively correlated with FS (r = 0.167, P = 0.042). CONCLUSION: The FAI was positively correlated with the LV function but was not associated with the severity of coronary artery disease.

Neural Entrainment to Rhythms of Imagined Syllables.

Imagined speech based brain-computer interface (BCI) is of great interest due to its efficiency and user-friendliness for patients with speech impairment. The aim of this work was to study whether different rhythms of imagined syllables could elicit corresponding frequency components on EEG amplitude spectra. Seventeen participants were recruited to take part in the experiments, and performed a control task and four imagery tasks with the presence of periodic pure tones while their EEG signals were recorded. The four imagery tasks included imagining the syllable' /a/' every time, every two times, and every three times the periodic pure tones occurred, and imagined twice every three times the periodic pure tones occurred. The experimental results analyzed by Fourier transform indicated that neural entrainment to rhythmic speech imagery can be notably reflected on the EEG amplitude spectra. Clinical Relevance- This work manifested that different rhythms of imagined syllables could be identified from EEG amplitude spectra, which may be beneficial to the development of imagined speech based BCIs.

Delivery of miR-654-5p via SonoVue Microbubble Ultrasound Inhibits Proliferation, Migration, and Invasion of Vascular Smooth Muscle Cells and Arterial Thrombosis and Stenosis through Targeting TCF21.

Background: Abnormal proliferation of vascular smooth muscle cells (VSMCs) is an important cause of vascular stenosis. The study explored the mechanism of inhibition of vascular stenosis through the molecular mechanism of smooth muscle cell phenotype transformation. Methods: Coronary heart disease-related genes were screened by bioinformatics, and the target genes of miR-654-5p were predicted by dual-luciferase method and immunofluorescence method. miR-654-5p mimic stimulation and transfection of TCF21 and MTAP into cells. SonoVue microbubble sonication was used to deliver miR-654-5p into cells. Cell proliferation, migration, and invasion were detected by CCK-8, wound scratch, and Transwell. HE and IHC staining were performed to study the effect of miR-654-5p delivery via SonoVue microbubble ultrasound on vessel stenosis in a model of arterial injury. Gene expression was determined by qRT-PCR and WB. Results: TCF21 and MTAP were predicted as the target genes of miR-654-5p. Cytokines induced smooth muscle cell proliferation, migration, and invasion and promoted miR-654-5p downregulation; noticeably, downregulated miR-654-5p was positively associated with the cell proliferation and migration. Overexpression of TCF21 promoted proliferation, invasion, and migration, and mimic reversed such effects. miR-654-5p overexpression delivered by SonoVue microbubble ultrasound inhibited proliferation, migration, and invasion of cells. Moreover, in arterial injury model, we found that SonoVue microbubble ultrasound transmitted miR-654-5p into the arterial wall to inhibit arterial thrombosis and stenosis, while TCF21 was inhibited. Conclusion: Ultrasound delivery of miR-654-5p via SonoVue microbubbles was able to inhibit arterial thrombosis and stenosis by targeting TCF21.

Aberrant brain gray matter and functional networks topology in end stage renal disease patients undergoing maintenance hemodialysis with cognitive impairment.

Purpose: To characterize the topological properties of gray matter (GM) and functional networks in end-stage renal disease (ESRD) patients undergoing maintenance hemodialysis to provide insights into the underlying mechanisms of cognitive impairment. Materials and methods: In total, 45 patients and 37 healthy controls were prospectively enrolled in this study. All subjects completed resting-state functional magnetic resonance imaging (rs-fMRI) and diffusion kurtosis imaging (DKI) examinations and a Montreal cognitive assessment scale (MoCA) test. Differences in the properties of GM and functional networks were analyzed, and the relationship between brain properties and MoCA scores was assessed. Cognitive function was predicted based on functional networks by applying the least squares support vector regression machine (LSSVRM) and the whale optimization algorithm (WOA). Results: We observed disrupted topological organizations of both functional and GM networks in ESRD patients, as indicated by significantly decreased global measures. Specifically, ESRD patients had impaired nodal efficiency and degree centrality, predominantly within the default mode network, limbic system, frontal lobe, temporal lobe, and occipital lobe. Interestingly, the involved regions were distributed laterally. Furthermore, the MoCA scores significantly correlated with decreased standardized clustering coefficient (γ), standardized characteristic path length (λ), and nodal efficiency of the right insula and the right superior temporal gyrus. Finally, optimized LSSVRM could predict the cognitive scores of ESRD patients with great accuracy. Conclusion: Disruption of brain networks may account for the progression of cognitive dysfunction in ESRD patients. Implementation of prediction models based on neuroimaging metrics may provide more objective information to promote early diagnosis and intervention.

Image quality and diagnostic accuracy of different dosages of iodixanol in computed tomography angiography and perfusion of overweight patients with coronary artery stenosis: A feasibility study.

OBJECTIVES: Iodixanol contrast media with different doses using computed tomography angiography (CTA) and perfusion (CTP) to diagnose coronary artery disease (CAD) in overweight patients lacks assessment. Our study compared iodixanol 320 mg I/ml and 270 mg I/ml on image quality and accuracy of CTA combined CTP (CTA-CTP) to diagnose CAD. METHODS: Overweight patients with suspected of CAD were randomized into iodixanol 270 group (received iodixanol 270 mg I/ml) and iodixanol 320 group (received iodixanol 320 mg I/ml). Based on these characteristics data, receiver operating characteristic (ROC) curve and corresponding area under the curve (AUC) were plotted to assess the sensitivity and specificity of the two administrations. RESULTS: The subjective definition score, signal to noise ratio, and CT value of aorta in iodixanol 320 group were higher than iodixanol 270 group. In iodixanol 270 group: the image exhibited a normal state of both vessels and myocardial perfusion; and the AUC, specificity, and sensitivity were 0.376, 66.67, and 80.46, respectively. In iodixanol 320 group: the image exhibited a diameter stenosis in right coronary artery and myocardial infarction of inferior wall and proximal inferior wall septum, as well as myocardial perfusion defects; and the AUC, specificity, and sensitivity in iodixanol 320 group were 0.824, 75.00, and 89.87, respectively. CONCLUSION: Accuracy and image quality of iodixanol 320 mg I/ml in the diagnosis of CAD with CTA-CTP was higher than using iodixanol 270 mg I/ml.

High Inflammatory Factor Levels Increase Cardiovascular Complications in Diabetic Patients Undergoing Coronary Artery Bypass Grafting.

Objective: To investigate the association between inflammation and clinical outcomes of coronary artery bypass grafting (CABG) in diabetic patients. Methods: A total of 300 diabetic patients with coronary heart disease who underwent CABG were selected. Patients were divided into a group with cardiovascular events (32 in the MACCE group) and a group without cardiovascular events (268 in the non-MACCE group) according to whether cardiovascular events occurred within 30 days. The differences in clinical parameters; serum levels of TNF-α, IL-6, IL-18, IL-1ß, and CRP; factors associated with the occurrence of MACCE; and risk factors affecting the midterm all-cause mortality of patients were compared between the two groups. Results: The serum levels of TNF-α, IL-6, IL-18, and CRP in the MACCE group were significantly higher than those in the non-MACCE group (p < 0.05). Gender, smoking, hyperlipidemia, duration of diabetes, and levels of TNF-α, IL-6, IL-18, and CRP were closely related to the occurrence of MACCE. The Kaplan-Meier survival analysis evaluation results showed that the levels of IL-6 and CRP significantly affected the midterm all-cause mortality rate (p < 0.05). Multivariate Cox regression analysis showed that the advanced age, hypertension, hyperlipidemia, long duration of diabetes, elevated serum IL-6, and CRP levels could be used as risk factors for midterm all-cause mortality. Conclusions: Inflammation levels in diabetic patients are associated with complications and midterm all-cause mortality in patients undergoing coronary artery bypass grafting.

Exploring value of CT coronary imaging combined with machine-learning methods to predict myocardial ischemia.

PURPOSE: To establish a machine-learning (ML) model based on coronary computed tomography angiography (CTA) images for evaluating myocardial ischemia in patients diagnosed with coronary atherosclerosis. METHODS: This retrospective analysis includes CTA images acquired from 110 patients. Among them, 58 have myocardial ischemia and 52 have normal myocardial blood supply. The patients are divided into training and test datasets with a ratio 7 : 3. Deep learning model-based CQK software is used to automatically segment myocardium on CTA images and extract texture features. Then, seven ML models are constructed to classify between myocardial ischemia and normal myocardial blood supply cases. Predictive performance and stability of the classifiers are determined by receiver operating characteristic curve with cross validation. The optimal ML model is then validated using an independent test dataset. RESULTS: Accuracy and areas under ROC curves (AUC) obtained from the support vector machine with extreme gradient boosting linear method are 0.821 and 0.777, respectively, while accuracy and AUC achieved by the neural network (NN) method are 0.818 and 0.757, respectively. The naive Bayes model yields the highest sensitivity (0.942), and the random forest model yields the highest specificity (0.85). The k-nearest neighbors model yields the lowest accuracy (0.74). Additionally, NN model demonstrates the lowest relative standard deviations (0.16 for accuracy and 0.08 for AUC) indicating the high stability of this model, and its AUC applying to the independent test dataset is 0.72. CONCLUSION: The NN model demonstrates the best performance in predicting myocardial ischemia using radiomics features computed from CTA images, which suggests that this ML model has promising potential in guiding clinical decision-making.

The Effects of Classification Method and Electrode Configuration on EEG-based Silent Speech Classification.

The effective classification for imagined speech and intended speech is of great help to the development of speech-based brain-computer interfaces (BCIs). This work distinguished imagined speech and intended speech by employing the cortical EEG signals recorded from scalp. EEG signals from eleven subjects were recorded when they produced Mandarin-Chinese monosyllables in imagined speech and intended speech, and EEG features were classified by the common spatial pattern, time-domain, frequency-domain and Riemannian manifold based methods. The classification results indicated that the Riemannian manifold based method yielded the highest classification accuracy of 85.9% among the four classification methods. Moreover, the classification accuracy with the left-only brain electrode configuration was close to that with the whole brain electrode configuration. The findings of this work have potential to extend the output commands of silent speech interfaces.

Enhanced computed tomography radiomics-based machine-learning methods for predicting the Fuhrman grades of renal clear cell carcinoma.

OBJECTIVE: To develop and test an optimal machine learning model based on the enhanced computed tomography (CT) to preoperatively predict pathological grade of clear cell renal cell carcinoma (ccRCC). METHODS: A retrospective analysis of 53 pathologically confirmed cases of ccRCC was performed and 25 consecutive ccRCC cases were selected as a prospective testing set. All patients underwent routine preoperative abdominal CT plain and enhanced scans. Renal tumor lesions were segmented on arterial phase images and 396 radiomics features were extracted. In the training set, seven discrimination classifiers for high- and low-grade ccRCCs were constructed based on seven different machine learning models, respectively, and their performance and stability for predicting ccRCC grades were evaluated through receiver operating characteristic (ROC) analysis and cross-validation. Prediction accuracy and area under ROC curve were used as evaluation indices. Finally, the diagnostic efficacy of the optimal model was verified in the testing set. RESULTS: The accuracies and AUC values achieved by support vector machine with radial basis function kernel (svmRadial), random forest and naïve Bayesian models were 0.860±0.158 and 0.919±0.118, 0.840±0.160 and 0.915±0.138, 0.839±0.147 and 0.921±0.133, respectively, which showed high predictive performance, whereas K-nearest neighborhood model yielded lower accuracy of 0.720±0.188 and lower AUC value of 0.810±0.150. Additionally, svmRadial had smallest relative standard deviation (RSD, 0.13 for AUC, 0.17 for accuracy), which indicates higher stability. CONCLUSION: svmRadial performs best in predicting pathological grades of ccRCC using radiomics features computed from the preoperative CT images, and thus may have high clinical potential in guiding preoperative decision.

A Nano-Based P1Cminimum Probe to Monitor Thrombosis in Grafts and Improve Cardiac Function After Coronary Artery Bypass Grafting Surgery.

Magnetic resonance imaging is widely used to identify and monitor thrombi in grafted vessels following coronary artery bypass grafting surgery (CABG). We produced a biosensor, P1Cm-SPIO-Cy5.5, composed of P1Cm peptide, superparamagnetic iron oxide nanoparticles (SPIO), and polyethylene glycol (PEG), for use in MRI thrombus imaging. Activated platelets induced by adenosine diphosphate were used for combination tests in vitro. A rat model of common carotid artery thrombosis was used for anti-thrombus experiments in vivo. P1Cm-SPIO-Cy5.5 remains stable in vitro and has good anticoagulation capacity. It can bind specifically to activated platelets and thrombi. In rats that underwent bypass surgery, P1Cm-SPIO-Cy5.5 could detect and label thrombi over a long period, and prevent thrombosis in grafted vessels. P1Cm-SPIO-Cy5.5 improved cardiac function in rats following CABG surgery. P1Cm-SPIO-Cy5.5 is a potential sensor for use in MRI for the early diagnosis and prevention of thrombosis after CABG surgery.

LncRNA NORAD Promotes Vascular Endothelial Cell Injury and Atherosclerosis Through Suppressing VEGF Gene Transcription via Enhancing H3K9 Deacetylation by Recruiting HDAC6.

Coronary artery disease (CAD) is a major atherosclerotic cardiovascular disease and the leading cause of mortality globally. Long non-coding RNAs (lncRNAs) play crucial roles in CAD development. To date, the effect of lncRNA non-coding RNA activated by DNA damage (NORAD) on atherosclerosis in CAD remains unclear. The primary aim of this study was to investigate the effect of lncRNA NORAD on vascular endothelial cell injury and atherosclerosis. Here, ox-LDL-treated human umbilical vein endothelial cells (HUVECs) and high-fat-diet (HFD)-fed ApoE-/- mice were utilized as in vitro and in vivo models. The present study found that lncRNA NORAD expression was increased in ox-LDL-treated HUVECs and thoracic aorta of atherosclerotic mice, and knockdown of lncRNA NORAD alleviated vascular endothelial cell injury and atherosclerosis development in vitro and in vivo. Knockdown of lncRNA NORAD aggravated ox-LDL-reduced or atherosclerosis-decreased vascular endothelial growth factor (VEGF) expression in HUVECs and thoracic aorta of mice to ameliorate vascular endothelial cell injury and atherosclerosis development. Moreover, nucleus lncRNA NORAD suppressed VEGF gene transcription through enhancing H3K9 deacetylation via recruiting HDAC6 to the VEGF gene promoter in ox-LDL-treated HUVECs. In addition, VEGF reduced FUS (FUS RNA binding protein) expression by a negative feedback regulation in HUVECs. In summary, lncRNA NORAD enhanced vascular endothelial cell injury and atherosclerosis through suppressing VEGF gene transcription via enhancing H3K9 deacetylation by recruiting HDAC6. The findings could facilitate discovering novel diagnostic markers and therapeutic targets for CAD.