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.

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.

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.

Intravoxel Incoherent Motion and Dynamic Contrast-Enhanced Magnetic Resonance Imaging to Early Detect Tissue Injury and Microcirculation Alteration in Hepatic Injury Induced by Intestinal Ischemia-Reperfusion in a Rat Model.

BACKGROUND: Intravoxel incoherent motion (IVIM) can provide quantitative information about water diffusion and perfusion that can be used to evaluate hepatic injury, but it has not been studied in hepatic injury induced by intestinal ischemia-reperfusion (IIR). Dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) can provide perfusion data, but it is unclear whether it can provide useful information for assessing hepatic injury induced by IIR. PURPOSE: To examine whether IVIM and DCE-MRI can detect early IIR-induced hepatic changes, and to evaluate the relationship between IVIM and DCE-derived parameters and biochemical indicators and histological scores. STUDY TYPE: Prospective pre-clinical study. POPULATION: Forty-two male Sprague-Dawley rats. FIELD STRENGTH/SEQUENCE: IVIM-diffusion-weighted imaging (DWI) using diffusion-weighted echo-planar imaging sequence and DCE-MRI using fast spoiled gradient recalled-based sequence at 3.0 T. ASSESSMENT: All rats were randomly divided into the control group (Sham), the simple ischemia group, the ischemia-reperfusion (IR) group (IR1h, IR2h, IR3h, and IR4h) in a model of secondary hepatic injury caused by IIR, and IIR was induced by clamping the superior mesenteric artery for 60 minutes and then removing the vascular clamp. Advanced Workstation (AW) 4.6 was used to calculate the imaging parameters (apparent diffusion coefficient [ADC], true diffusion coefficient [D], perfusion-related diffusion [D* ] and volume fraction [f]) of IVIM. OmniKinetics (OK) software was used to calculate the DCE imaging parameters (Ktrans , Kep , and Ve ). Alanine aminotransferase (ALT) and aspartate aminotransferase (AST) were analyzed with an automatic biochemical analyzer. Superoxide dismutase (SOD) activity was assessed using the nitro-blue tetrazolium method. Malondialdehyde (MDA) was determined by thiobarbituric acid colorimetry. Histopathology was performed with hematoxylin and eosin staining. STATISTICAL TESTS: One-way analysis of variance (ANOVA) and Bonferroni post-hoc tests were used to analyze the imaging parameters and biochemical indicators among the different groups. Pearson correlation analysis was applied to determine the correlation between imaging parameters and biochemical indicators or histological score. RESULTS: ALT and MDA reached peak levels at IR4h, while SOD reached the minimum level at IR4h (all P < 0.05). ADC, D, D* , and f gradually decreased as reperfusion continued, and Ktrans and Ve gradually increased (all P < 0.05). The degrees of change for f and Ve were greater than those of other imaging parameters at IR1h (all P < 0.05). All groups showed good correlation between imaging parameters and SOD and MDA (r[ADC] = 0.615, -0.666, r[D] = 0.493, -0.612, r[D* ] = 0.607, -0.647, r[f] = 0.637, -0.682, r[Ktrans ] = -0.522, 0.500, r[Ve ] = -0.590, 0.665, respectively; all P < 0.05). However, the IR groups showed poor or no correlation between the imaging parameters and SOD and MDA (P [Ktrans and MDA] = 0.050, P [D and SOD] = 0.125, P [the remaining imaging parameters] < 0.05). All groups showed a positive correlation between histological score and Ktrans and Ve (r = 0.775, 0.874, all P < 0.05), and a negative correlation between histological score and ADC, D, f, and D* (r = -0.739, -0.821, -0.868, -0.841, respectively; all P < 0.05). For the IR groups, there was a positive correlation between histological score and Ktrans and Ve (r = 0.747, 0.802, all P < 0.05), and a negative correlation between histological score and ADC, D, f, and D* (r = -0.567, -0.712, -0.715, -0.779, respectively; all P < 0.05). DATA CONCLUSION: The combined application of IVIM and DCE-MRI has the potential to be used as an imaging tool for monitoring IIR-induced hepatic histopathology. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY STAGE: 2.

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.

Development and External Validation of Radiomics Approach for Nuclear Grading in Clear Cell Renal Cell Carcinoma.

BACKGROUND AND PURPOSE: Nuclear grades of clear cell renal cell carcinoma (ccRCC) are usually confirmed by invasive methods. Radiomics is a quantitative tool that uses non-invasive medical imaging for tumor diagnosis and prognosis. In this study, a radiomics approach was proposed to analyze the association between preoperative computed tomography (CT) images and nuclear grades of ccRCC. METHODS: Our dataset included 320 ccRCC patients from two centers and was divided into a training set (n = 124), an internal test set (n = 123), and an external test set (n = 73). A radiomic feature set was extracted from unenhanced, corticomedullary phase, and nephrographic phase CT images. The maximizing independent classification information criteria function and recursive feature elimination with cross-validation were used to select effective features. Random forests were used to build a final model for predicting nuclear grades, and area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of radiomic features and models. RESULTS: The radiomic features from the three CT phases could effectively distinguished the four nuclear grades. A combined model, merging radiomic features and clinical characteristics, obtained good predictive performances in the internal test set (AUC 0.77, 0.75, 0.79, and 0.85 for the four grades, respectively), and performance was further confirmed in the external test set, with AUCs of 0.75, 0.68, and 0.73 (no fourth-level data). CONCLUSION: The combination of CT radiomic features and clinical characteristics could discriminate the nuclear grades in ccRCC, which may help in assisting treatment decision making.

Comparative study of dual energy CT iodine imaging and standardized concentrations before and after chemoradiotherapy for esophageal cancer.

BACKGROUND: To compare dual energy CT iodine imaging and standardized iodine concentration before and after chemoradiotherapy (CRT) for esophageal cancer and evaluate the efficacy of CRT for EC by examining DECT iodine maps and standard CT values. METHODS: The clinical data of 45 patients confirmed by pathology with newly diagnosed esophageal cancer who underwent concurrent CRT from February 2012 to January 2017 in our department of radiology were collected. All patients underwent dual-source dual-energy CT (DECT) before and after CRT. Normalized iodine concentration (NIC) and normalized CT (NCT) corresponding to the overall cancer lesion and its maximum cross-sectional area were observed and compared. Additionally, 30 healthy individuals were compared as control group. After treatment, the patients were divided into two groups according to RECIST1.1: treatment effective group and ineffective group. RESULTS: There were 33 patients (CR 9, PR 24) in the effective group and 12 patients (SD 12, PD 0) in the ineffective group. There was no significant difference in the NIC-A, NIC-V, NCT-A and NCT-A indexes between the effective group (B group) and the ineffective group (C group) before treatment (P > 0.05). After the treatment, the above-mentioned indexes in the effective group of patients were significantly lower than before treatment, and compared with the ineffective group, the NIC-A, NIC-V, NCT-A and NCT-V values of the effective group were significantly lower than those of ineffective group (P < 0.05). After treatment, the NIC-V and NCT-V in the ineffective group were lower than before treatment, and the difference was statistically significant (P < 0.05). However, their NIC-A and NCT-A were not statistically different from those before treatment (P > 0.05). CONCLUSION: Using DECT iodine map, the changes of NIC and NIC before and after CRT in patients with esophageal cancer can evaluate the effect of CRT, and does not increase the radiation dose, so it is suitable for clinical use.

Identification and evaluation of 2 circulating microRNAs for non-small cell lung cancer diagnosis.

The circulating miRNAs are dysregulated in non-small cell lung cancer (NSCLC) and have great promise for clinical diagnosis. The aim of this study was to screen and evaluate the reliability and diagnostic efficiency of candidate serum miRNAs for NSCLC diagnosis. We found that miR-21 and let-7a were significantly dysregulated in NSCLC patients (P < .001). When applied for NSCLC diagnosis, combined application of miR-21 and let-7a had a sensitivity of 0.790, specificity of 0.750, which is significantly higher than application alone of miR-21(sensitivity 0.647; specificity 0.641), let-7a (sensitivity 0.653; specificity 0.718) or computed tomography-guided core-needle biopsy (CTCB) (sensitivity 0.725; specificity 0.609) (P < .01). In conclusion, our study indicates that combined application of circulating miR-21 and let-7a has advantages over traditional CTCB technique.

Application of 80-kVp tube voltage, low-concentration contrast agent and iterative reconstruction in coronary CT angiography: evaluation of image quality and radiation dose.

BACKGROUND AND AIM: To evaluate image quality and radiation dosage in coronary CT angiography using 80-kVp tube voltage combined with low-concentration contrast media (CM) and iterative reconstruction (IR) for coronary CT angiography (CCTA) and employing dual-source CT without heart-rate control. METHODS: 154 patients were randomly assigned to Group A (Control Group, 120-kVp tube voltage, high-concentration CM and filtered back projection reconstruction) and Group B (Low-Dose Group, 80 kVp, low-concentration CM and iterative construction). Two experienced radiologists double-blindly evaluated the following parameters: CT attenuation, signal-noise ratio (SNR), contrast-noise ratio (CNR), radiation dose, size-specific dose estimates (SSDE) and total iodine intake. Pearson correlation analysis was used to assess the relationship between SSDE and BMI. RESULTS: 98.1% vessel segments in Group A and 97.6% in Group B passed diagnostics, indicating no significant differences; the average aorta scores and CT attenuation values showed no significant differences between groups. Similar SNR and CNR results were obtained for the two groups, although values were slightly lower in Group A compared with Group B. The Effective Dose in Group B was 63% lower than that in Group A (P<.001). SSDE results were significantly different between the two groups (P<.001) but did not correlate with BMI. Finally, the total iodine intake in Group B was 22.9% lower than that in Group A. CONCLUSIONS: Coronary CTA conducted with a low tube voltage of 80 kVp, a low-concentration CM and IR without heart rate control can achieve images of similar quality to those obtained using standard procedures, significantly reducing the associated radiation dose and iodine intake.

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.

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.

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.

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.

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.

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.

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.