Green, pH-Sensitive, Highly Stretchable, and Hydrogen Bond-Dominated Ionogel for Wound Healing Activity.

Traditional hydrogel dressings generally have poor mechanical properties and stability when subjected to external stress due to the undesirable chain entanglement structure of their single valence bond compositions. Therefore, it is particularly important to develop a type of gel dressing with good mechanical strength, stability, and environment-friendly monitoring. In this work, a transparent, pH-sensitive, highly stretchable, and biocompatible anthocyanidin ionogel dressing was prepared, realizing green and accurate detection. Attributed to the antibacterial activity of the ionic liquid, the biocompatibility of the pectin, and the ability to scavenge free radicals of the anthocyanidin, the ionogel dressing exhibited excellent re-epithelialization in the 14 day wound healing process. Besides, changes in pH values monitoring of the ionogel over 3 days coincided with normal wound exudate. The obtained ionogel also showed good water retention, swelling properties, mechanical stretchability, and 5 week stability, illustrating great potential in wound dressings.

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.