T1-weighted MRI of targeting atherosclerotic plaque based on CD40 expression on engulfed USPIO's cell surface.

Atherosclerosis is a chronic inflammatory disease characterized by the accumulation of cholesterol within the arterial wall. Its progression can be monitored via magnetic resonance imaging (MRI). Ultrasmall Superparamagnetic Particles of Iron Oxide (USPIO) (<5 nm) have been employed as T1 contrast agents for MRI applications. In this study, we synthesized USPIO with an average surface carboxylation of approximately 5.28 nm and a zeta potential of -47.8 mV. These particles were phagocytosed by mouse aortic endothelial cells (USPIO-MAECs) and endothelial progenitor cells (USPIO-EPCs), suggesting that they can be utilized as potential contrast agent and delivery vehicle for the early detection of atherosclerosis. However, the mechanism by which this contrast agent is delivered to the plaque remains undetermined. Our results demonstrated that with increasing USPIO concentration during 10-100 µg ml-1, consistent change appeared in signal enhancement on T1-weighted MRI. Similarly, T1-weighted MRI of MAECs and EPCs treated with these concentrations exhibited a regular change in signal enhancement. Prussian blue staining of USPIO revealed substantial absorption into MAECs and EPCs after treatment with 50 µg ml-1USPIO for 24 h. The iron content in USPIO-EPCs was much higher (5 pg Fe/cell) than in USPIO-MAECs (0.8 pg Fe/cell). In order to substantiate our hypothesis that CD40 protein on the cell surface facilitates migration towards inflammatory cells, we utilized AuNPs-PEI (gold nanoparticles-polyethylenimine) carrying siRNACD40to knockout CD40 expression in MAECs. It has been documented that gold nanoparticle-oligonucleotide complexes could be employed as intracellular gene regulation agents for the control of protein level in cells. Our results confirmed that macrophages are more likely to bind to MAECs treated with AuNPs-PEI-siRNANC(control) for 72 h than to MAECs treated with AuNPs-PEI-siRNACD40(reduced CD40 expression), thus confirming CD40 targeting at the cellular level. When USPIO-MAECs and MAECs (control) were delivered to mice (high-fat-fed) via tail vein injection respectively, we observed a higher iron accumulation in plaques on blood vessels in high-fat-fed mice treated with USPIO-MAECs. We also demonstrated that USPIO-EPCs, when delivered to high-fat-fed mice via tail vein injection, could indeed label plaques by generating higher T1-weighted MRI signals 72 h post injection compared to controls (PBS, USPIO and EPCs alone). In conclusion, we synthesized a USPIO suitable for T1-weighted MRI. Our results have confirmed separately at the cellular and tissue andin vivolevel, that USPIO-MAECs or USPIO-EPCs are more accessible to atherosclerotic plaques in a mouse model. Furthermore, the high expression of CD40 on the cell surface is a key factor for targeting and USPIO-EPCs may have potential therapeutic effects.

Development and empirical study of international student satisfaction model of online course learning interaction in chinese universities.

COVID-19 has disrupted education internationalisation around the world, making online learning a necessary means of learning. This study proposes an International Student Satisfaction Index Model (ISSM) on the interaction of online international courses in Chinese universities, aiming to investigate the potential factors that affect international students' online learning interaction. Based on the large-scale online course practice in Chinese universities during the pandemic, this study adopted a stratified random sampling method to select 320 international students participating in online courses as a research sample. The model proposed in this study includes four antecedent variables, one target variable, and one outcome variable. This study is quantitative, using SPSS26.0 and AMOS 21.0 to analyse the collected empirical data, and the results verify the nine research hypotheses proposed and the applicability of the online course international students' satisfaction index model (ISSM) proposed. The research results provide strong theoretical and practical support for international students' satisfaction with online course learning interaction, which is conducive to the reform of online courses and improving international students' retention rate in the online course.

Developing a deep learning model to predict epilepsy recurrence in patients with focal cortical dysplasia type III.

Background: A sizable number of patients with focal cortical dysplasia (FCD) type III-related refractory epilepsy continue to experience seizures postsurgically. Deep learning models can automatically assess complex medical image characteristics and predict prognosis with higher efficiency. This study sought to determine whether T2-weighted fluid attenuated inversion recovery (T2W FLAIR) images could predict prognosis of FCD type III-related refractory epilepsy using a deep learning approach. Methods: Magnetic resonance imaging (MRI) images of 266 patients with FCD type III diagnosed between 2015 and 2019 were included in this retrospective analysis. A deep learning algorithm utilizing a convolutional neural network (CNN) was trained to classify T2W FLAIR images according to Engel's classification. The preprocessed original image and the region of interest (ROI) outlined by clinicians were input into our neural network separately and then together. Precision, sensitivity, specificity, receiver operating characteristic (ROC) curves, and areas under the ROC curves (AUCs) were computed as part of the statistical analyses of the network performance with varied inputs of the network model assessed. Results: The overall performance met the following metrics when the original image only was input: AUC of 96.22%, sensitivity of 84.47%, and specificity of 97.21%. The metrics were as follows when the ROI only was input: area under the ROC curve of 94.76%, sensitivity of 84.92%, and specificity of 96.24%. For the combined inputs, the metrics were as follows: AUC of 97.17%, sensitivity of 90.86%, and specificity of 96.63%. Conclusions: Deep learning used with conventional MRI can effectively predict the recurrence conditions of epilepsy. Artificial intelligence may help the design of clinical management and enable more precise and individualized prediction for postsurgical prognosis of FCD type III-related refractory epilepsy.

Focal Cortical Dysplasia Type â ¢ Related Medically Refractory Epilepsy: MRI Findings and Potential Predictors of Surgery Outcome.

This study aims to explore the relationship between neuropathologic and the post-surgical prognosis of focal cortical dysplasia (FCD) typed-Ⅲ-related medically refractory epilepsy. A total of 266 patients with FCD typed-Ⅲ-related medically refractory epilepsy were retrospectively studied. Presurgical clinical data, type of surgery, and postsurgical seizure outcome were analyzed. The minimum post-surgical follow-up was 1 year. A total of 266 patients of FCD type Ⅲ were included in this study and the median follow-up time was 30 months (range, 12~48 months). Age at onset ranged from 1.0 years to 58.0 years, with a median age of 12.5 years. The number of patients under 12 years old was 133 (50%) in patients with FCD type Ⅲ. A history of febrile seizures was present in 42 (15.8%) cases. In the entire postoperative period, 179 (67.3%) patients were seizure-free. Factors with p < 0.15 in univariate analysis, such as age of onset of epilepsy (p = 0.145), duration of epilepsy (p = 0.004), febrile seizures (p = 0.150), being MRI-negative (p = 0.056), seizure type (p = 0.145) and incomplete resection, were included in multivariate analysis. Multivariate analyses revealed that MRI-negative findings of FCD (OR 0.34, 95% CI 0.45-0.81, p = 0.015) and incomplete resection (OR 0.12, 95% CI 0.05-0.29, p < 0.001) are independent predictors of unfavorable seizure outcomes. MRI-negative finding of FCD lesions and incomplete resection were the most important predictive factors for poor seizure outcome in patients with FCD type Ⅲ.

Development and Validation of a Radiomics Nomogram for Differentiating Mycoplasma Pneumonia and Bacterial Pneumonia.

OBJECTIVES: To develop and validate a radiological nomogram combining radiological and clinical characteristics for differentiating mycoplasma pneumonia and bacterial pneumonia with similar CT findings. METHODS: A total of 100 cases of pneumonia patients receiving chest CT scan were retrospectively analyzed, including 60 patients with mycoplasma pneumonia and 40 patients with bacterial pneumonia. The patients were divided into the train set (n = 70) and the test set (n = 30). The features were extracted from chest CT images of each patient by AK analysis software, then univarite analysis, spearman correlation analysis, and least absolute shrinkage and selection operator (LASSO) were utilized for dimension reduction in training set. A radiomics model was built by multivariable logistic regression based on the selected features, and a radiomics-clinical multivariable logistic regression model was built by combining imaging radiomics and clinical risk factors (age and temperature). ROC, AUC, sensitivity, specificity, and accuracy were calculated to validate the two models. The nomogram of the radiomics-clinical was built and evaluated by calibration curve. The clinical benefit of the two models was measured by using decision curve. RESULTS: A total of 396 texture features were extracted from each chest CT image, and 10 valuable features were screened out. In the radiomics model, the AUC, sensitivity, specificity, and accuracy for the train set is 0.877, 0.762, 0.821, 78.6%, and for the test set it is 0.810, 0.667, 0.750 and 70.0%, respectively. In the radiomics-clinical model, the AUC, sensitivity, specificity, and accuracy for the train set is 0.905, 0.976, 0.714, 87.1%, and for the test set is is 0.847, 0.889, 0.667 and 80.0%, respectively. Decision curve analysis shows that both the two models increase the clinical benefits of the patients, and the radiomics-clinical model gains higher clinical benefits, compared to the radiomics model. CONCLUSION: The radiomics-clinical nomogram had good performance in identifying mycoplasma pneumonia and bacterial pneumonias, which would be helpful in clinical decision-making.

Isoniazid preventive therapy in HIV-infected pregnant and postpartum women in high prevalence of tuberculosis countries: A protocol for systematic review.

BACKGROUND: Tuberculosis (TB) is the leading cause of health complications and death among human with immunodeficiency virus (HIV) infection. When TB develops during pregnancy or the early postpartum period, it is associated with negative maternal, pregnancy, and fetus and infant outcome, including premature birth, low birth weight, and congenital or neonatal TB infection or disease. The objective of this systematic review is to investigate the effective and safe of isoniazid for preventing TB for HIV-infected pregnant women in counties with high prevalence of TB. METHODS: Pubmed, Embase, and Cochrane library will be searched to include randomized control trials which compared isoniazid preventive therapy with placebo for preventing TB in HIV-infected pregnant and postpartum women. RevMan version 5.3 will be used to perform all calculations related to the meta-analysis. Dichotomous data will be calculated in terms of a fixed or random effect model and expressed by the relative risk (RR) with 95% confidence interval (CI). The Cochrane collaboration's tool in the following aspects was used to assess the risk of bias (ROB) in included studies. The inconsistency index (I2) and Chi-squared will be applied for heterogeneity detection between clinical trials. A value of P < 0.05 will be considered statistically significant. RESULTS: The main outcomes of pooled evidence synthesis will be presented including the incidence of TB and adverse events. CONCLUSION: This study will provide the evidence of whether isoniazid is an effective and safe intervention for preventing TB for HIV-infected pregnant women. REGISTRATION NUMBER: INPLASY202070011.

Prophylactic antibiotics for miscarriage surgery: A protocol for systematic review and meta-analysis.

BACKGROUND: Infection is a serious potential consequence of surgery to complete a spontaneous abortion. Antibiotic prophylaxis before some operations has been shown to reduce the risk of postoperative infections. However, for miscarriage surgery, evidence is lacking to show effectiveness. METHODS: In this systematic review, the electronic databases of Cochrane Central Register of Controlled Trials, EMBASE, and PUBMED will be searched from inception to May 1, 2020. Randomized controlled trials that assessed the effectiveness and safety of antibiotic prophylaxis for preventing infection for patients undergoing miscarriage surgery will be included. All process of the study selection, data extraction, and methodology evaluation will be carried out by two authors independently. RevMan 5.3 software will be utilized for statistical analysis. RESULTS: This study will provide a detailed summary of latest evidence related to the effectiveness and safety of antibiotic prophylaxis for preventing infection for patients undergoing miscarriage surgery. CONCLUSION: The findings of this study may provide possible guidance for the use of antibiotic prophylaxis for preventing infection for patients undergoing miscarriage surgery. DISSEMINATION AND ETHICS: Ethical approval is not required in this study, because it will not collect the original data from individual patient. The results are expected to publish through a peer-reviewed journal. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD CRD42020155643.