Background/Aims: This study aimed to analyze the impact of microRNA-499 (miR-499) on the inflammatory damage of endothelial cells during coronary artery disease (CAD) via the targeting of PDCD4 through the NF-kB/ TNF-α signaling pathway. Methods: A total of 216 CAD patients (CAD group) and 90 healthy people (normal group) were enrolled in our study. Endothelial cells were collected and assigned into normal, OX-LDL, negative control (NC), miR-499 inhibitor, miR-499 mimic, PDCD4 siRNA, and miR-499 inhibitor + PDCD4 siRNA groups. The qRT-PCR and western blotting were performed to detect the mRNA and protein expression levels of PDCD4 and miR-499. The MTT assay was performed to determine cell viability, ELISA was performed to determine the expression levels of inflammatory factors, and flow cytometry assay to evaluate cell apoptosis. Results: Increased miR-499 expression and decreased PDCD4 expression in the plasma were observed in the CAD group compared with the normal group, demonstrating a negative correlation between miR-499 and PDCD4. Compared to the normal and miR-499 inhibitor groups, the survival rate of cells and PDCD4 expression were decreased; and the expressions of miR-499, IL-6, IL-8, IL-1β, TNF-α, NF-kB, VCAM-1, ICAM-1 and MCP-1 and the apoptosis rate were all elevated in the OX-LDL, NC, miR-499 mimic, PDCD4 siRNA and miR-499 inhibitor + PDCD4 siRNA groups. Compared to the OX-LDL, NC and miR-499 inhibitor + PDCD4 siRNA groups, PDCD4 expression and the survival rate of cells were increased; and the IL-6, IL-8, IL-1β, TNF-α, NF-κB, VCAM-1, ICAM-1 and MCP-1 expression levels and the apoptosis rate were all reduced in the miR-499 inhibitor group. In the PDCD4 siRNA group, PDCD4 expression and the survival rate of cells were lower, and the expression levels of IL-6, IL-8, IL-1β, TNF-α, NF-κB, VCAM-1, ICAM-1 and MCP-1 and the apoptosis rate were all higher compared with the miR-499 mimic group. In the miR-499 inhibitor + PDCD4 siRNA group, PDCD4 expression and the survival rate of cells were higher, and the expression levels of IL-6, IL-8, IL-1β, TNF-α, NF-κB, VCAM-1, ICAM-1, and MCP-1 and the apoptosis rate were all lower than those in the PDCD4 siRNA group. Conclusion: Down-regulated miR-499 expression increased PDCD4 expression and protected endothelial cells from inflammatory damage during CAD by inhibiting the NF-κB/TNF-α signaling pathway.

Objective: To verify the association between CD44 and CD133 expression levels and the prognosis of patients with lower-grade gliomas (LGGs) and constructing radiomic models to predict those two genes’ expression levels before surgery. Materials & methods: Genomic data of patients with LGG and the corresponding T2-weighted fluid-attenuated inversion recovery images were downloaded from the Cancer Genome Atlas and the Cancer Imaging Archive, which were utilized for prognosis analysis, radiomic feature extraction and model construction, respectively. Results & conclusion: CD44 and CD133 expression levels in LGG can significantly affect the prognosis of patients with LGG. Based on the T2-weighted fluid-attenuated inversion recovery images, the radiomic features can effectively predict the expression levels of CD44 and CD133 before surgery.

PurposeTo develop and validate a clinical-radiomic nomogram for the preoperative prediction of the aldosterone-producing adenoma (APA) risk in patients with unilateral adrenal adenoma.Patients and MethodsNinety consecutive primary aldosteronism (PA) patients with unilateral adrenal adenoma who underwent adrenal venous sampling (AVS) were randomly separated into training (n = 62) and validation cohorts (n = 28) (7:3 ratio) by a computer algorithm. Data were collected from October 2017 to June 2020. The prediction model was developed in the training cohort. Radiomic features were extracted from unenhanced computed tomography (CT) images of unilateral adrenal adenoma. The least absolute shrinkage and selection operator (LASSO) regression model was used to reduce data dimensions, select features, and establish a radiomic signature. Multivariable logistic regression analysis was used for the predictive model development, the radiomic signature and clinical risk factors integration, and the model was displayed as a clinical-radiomic nomogram. The nomogram performance was evaluated by its calibration, discrimination, and clinical practicability. Internal validation was performed.ResultsSix potential predictors were selected from 358 texture features by using the LASSO regression model. These features were included in the Radscore. The predictors included in the individualized prediction nomogram were the Radscore, age, sex, serum potassium level, and aldosterone-to-renin ratio (ARR). The model showed good discrimination, with an area under the receiver operating characteristic curve (AUC) of 0.900 [95% confidence interval (CI), 0.807 to 0.993], and good calibration. The nomogram still showed good discrimination [AUC, 0.912 (95% CI, 0.761 to 1.000)] and good calibration in the validation cohort. Decision curve analysis presented that the nomogram was useful in clinical practice.ConclusionsA clinical-radiomic nomogram was constructed by integrating a radiomic signature and clinical factors. The nomogram facilitated accurate prediction of the probability of APA in patients with unilateral adrenal nodules and could be helpful for clinical decision making.

ObjectiveThis study aimed to develop a radiomics model to predict early recurrence (<1 year) in grade II glioma after the first resection.MethodsThe pathological, clinical, and magnetic resonance imaging (MRI) data of patients diagnosed with grade II glioma who underwent surgery and had a recurrence between 2017 and 2020 in our hospital were retrospectively analyzed. After a rigorous selection, 64 patients were eligible and enrolled in the study. Twenty-two cases had a pathologically confirmed recurrent glioma. The cases were randomly assigned using a ratio of 7:3 to either the training set or validation set. T1-weighted image (T1WI), T2-weighted image (T2WI), and contrast-enhanced T1-weighted image (T1CE) were acquired. The minimum-redundancy-maximum-relevancy (mRMR) method alone or in combination with univariate logistic analysis were used to identify the most optimal predictive feature from the three image sequences. Multivariate logistic regression analysis was then used to develop a predictive model using the screened features. The performance of each model in both training and validation datasets was assessed using a receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA).ResultsA total of 396 radiomics features were initially extracted from each image sequence. After running the mRMR and univariate logistic analysis, nine predictive features were identified and used to build the multiparametric radiomics model. The model had a higher AUC when compared with the univariate models in both training and validation data sets with an AUC of 0.966 (95% confidence interval: 0.949–0.99) and 0.930 (95% confidence interval: 0.905–0.973), respectively. The calibration curves indicated a good agreement between the predictable and the actual probability of developing recurrence. The DCA demonstrated that the predictive value of the model improved when combining the three MRI sequences.ConclusionOur multiparametric radiomics model could be used as an efficient and accurate tool for predicting the recurrence of grade II glioma.

PurposeTo evaluate whether multiparametric magnetic resonance imaging (MRI)-based logistic regression models can facilitate the early prediction of chemoradiotherapy response in patients with residual brain gliomas after surgery.Patients and MethodsA total of 84 patients with residual gliomas after surgery from January 2015 to September 2020 who were treated with chemoradiotherapy were retrospectively enrolled and classified as treatment-sensitive or treatment-insensitive. These patients were divided into a training group (from institution 1, 57 patients) and a validation group (from institutions 2 and 3, 27 patients). All preoperative and postoperative MR images were obtained, including T1-weighted (T1-w), T2-weighted (T2-w), and contrast-enhanced T1-weighted (CET1-w) images. A total of 851 radiomics features were extracted from every imaging series. Feature selection was performed with univariate analysis or in combination with multivariate analysis. Then, four multivariable logistic regression models derived from T1-w, T2-w, CET1-w and Joint series (T1+T2+CET1-w) were constructed to predict the response of postoperative residual gliomas to chemoradiotherapy (sensitive or insensitive). These models were validated in the validation group. Calibration curves, receiver operating characteristic (ROC) curves, and decision curve analysis (DCA) were applied to compare the predictive performances of these models.ResultsFour models were created and showed the following areas under the ROC curves (AUCs) in the training and validation groups: Model-Joint series (AUC, 0.923 and 0.852), Model-T1 (AUC, 0.835 and 0.809), Model-T2 (AUC, 0.784 and 0.605), and Model-CET1 (AUC, 0.805 and 0.537). These results indicated that the Model-Joint series had the best performance in the validation group, followed by Model-T1, Model-T2 and finally Model-CET1. The calibration curves indicated good agreement between the Model-Joint series predictions and actual probabilities. Additionally, the DCA curves demonstrated that the Model-Joint series was clinically useful.ConclusionMultiparametric MRI-based radiomics models can potentially predict tumor response after chemoradiotherapy in patients with postoperative residual gliomas, which may aid clinical decision making, especially to help patients initially predicted to be treatment-insensitive avoid the toxicity of chemoradiotherapy.