[Pathogenic mechanisms of impaired neuronal autophagy flux after ischemic stroke].

Autophagy is a metabolic process in which damaged organelles, obsolete proteins, excess cytoplasmic components, and even pathogens are presented to lysosomes for degradation via autophagosomes. It includes 4 processes: the initiation of autophagy, the formation of autophagosomes, the fusion of autophagosomes with lysosomes, and the degradation and removal of autophagic substrates within autophagic lysosomes. When these processes are continuous, it is called autophagy flux. Blockage of one or certain steps in the autophagy/lysosome signaling pathway can lead to impaired autophagy flux. Numerous studies have shown that impaired autophagy flux is an important cause of neuronal damage in the ischemic penumbra after stroke. This paper summarized research progress in the pathological mechanisms that cause impaired neuronal autophagy flux after ischemic stroke and discusses methods to improve neuronal autophagy flux, in order to provide a reference for an in-depth investigation of the pathological injury mechanisms after stroke.

Rational Design of High-Performance Cationic Organic Network Adsorbents.

Development of high-performance ionic organic network (ION) adsorbents is of great importance for water remediation. However, the research on IONs is still nascent, especially, the design philosophy regarding contaminant adsorption has rarely been explored. In this contribution, we optimized the adsorption efficiency of IONs by increasing the density of charged sites and improving their accessibility. We first produced a new cationic organic network (CON), CON-LDU4, with a high density of positive sites via synthesis from tetra(4-pyridyl)ethene. Compared to the analogue CON-LDU2 that synthesized from tetra(4-(4-pyridyl)phenyl)ethene, CON-LDU4 exhibited higher efficiency in adsorption of methyl blue, indicating that the higher ionic density results in the higher adsorption efficiency. To further improve the accessibility of the active sites, another new CON material (CON-LDU5) was synthesized by employing a hard template. CON-LDU5 exhibited a larger specific surface area than CON-LDU4, with clearly enhanced adsorption efficiency. Finally, CON-LDU5 was used to capture CrO42- ions in water with fast adsorption kinetics (k2 = 0.0328 g mg-1 min-1) and high adsorption capacity (369 mg g-1).

The Effect of the Prognostic Nutritional Index on the Toxic Side Effects of Radiochemotherapy and Prognosis After Radical Surgery for Gastric Cancer.

OBJECTIVE: A retrospective analysis was conducted to investigate the effect of the preoperative prognostic nutritional index (PNI) on the severity of toxic side effects of radiochemotherapy and the survival prognosis of patients with gastric cancer to guide the clinical nutritional support for patients with gastric cancer. METHODS: Data of 191 patients with gastric cancer in the Department of Gastrointestinal Surgery of Guizhou Cancer Hospital and the Affiliated Hospital of Guizhou Medical University between January 2008 and December 2018 were analyzed retrospectively. Patients were allocated to the high PNI group (with PNI ≥47.7) and the low PNI group (with PNI <47.7) according to the PNI cutoff value, and the incidence of severe toxic side effects of radiochemotherapy and the overall survival time were compared between the high PNI group and low PNI group. In addition, prognostic factor analysis was performed. RESULTS: The severe hematologic side effects of radiochemotherapy and shorter postoperative survival time were more likely to occur in the low PNI group than in the high PNI group. The multifactor analysis showed that TNM stage (p = 0.000) and PNI (p = 0.001) were the independent risk factors for the overall postoperative survival time in patients with gastric cancer. CONCLUSION: Preoperative PNI might predict the severity of hematologic toxic side effects of adjuvant chemotherapy/radiochemotherapy in patients with gastric cancer after surgery. Patients in the low PNI group were more likely to have severe hematologic toxic side effects, and therefore a low PNI might be one of the important factors affecting the prognosis of gastric cancer.

Computer-aided diagnosis of lung cancer: the effect of training data sets on classification accuracy of lung nodules.

This study aims to develop a computer-aided diagnosis (CADx) scheme for classification between malignant and benign lung nodules, and also assess whether CADx performance changes in detecting nodules associated with early and advanced stage lung cancer. The study involves 243 biopsy-confirmed pulmonary nodules. Among them, 76 are benign, 81 are stage I and 86 are stage III malignant nodules. The cases are separated into three data sets involving: (1) all nodules, (2) benign and stage I malignant nodules, and (3) benign and stage III malignant nodules. A CADx scheme is applied to segment lung nodules depicted on computed tomography images and we initially computed 66 3D image features. Then, three machine learning models namely, a support vector machine, naïve Bayes classifier and linear discriminant analysis, are separately trained and tested by using three data sets and a leave-one-case-out cross-validation method embedded with a Relief-F feature selection algorithm. When separately using three data sets to train and test three classifiers, the average areas under receiver operating characteristic curves (AUC) are 0.94, 0.90 and 0.99, respectively. When using the classifiers trained using data sets with all nodules, average AUC values are 0.88 and 0.99 for detecting early and advanced stage nodules, respectively. AUC values computed from three classifiers trained using the same data set are consistent without statistically significant difference (p > 0.05). This study demonstrates (1) the feasibility of applying a CADx scheme to accurately distinguish between benign and malignant lung nodules, and (2) a positive trend between CADx performance and cancer progression stage. Thus, in order to increase CADx performance in detecting subtle and early cancer, training data sets should include more diverse early stage cancer cases.

Fusion of quantitative imaging features and serum biomarkers to improve performance of computer-aided diagnosis scheme for lung cancer: A preliminary study.

OBJECTIVES: To develop and test a new multifeature-based computer-aided diagnosis (CADx) scheme of lung cancer by fusing quantitative imaging (QI) features and serum biomarkers to improve CADx performance in classifying between malignant and benign pulmonary nodules. METHODS: First, a dataset involving 173 patients was retrospectively assembled, which includes computed tomography (CT) images and five serum biomarkers extracted from blood samples. Second, a CADx scheme using a four-step-based semiautomatic segmentation method was applied to segment the targeted lung nodules, and compute 78 QI features from each segmented nodule from CT images. Third, two support vector machine (SVM) classifiers were built using QI features and serum biomarkers, respectively. SVM classifiers were trained and tested using the overall dataset with a Relief feature selection method, a synthetic minority oversampling technique and a leave-one-case-out validation method. Finally, to further improve CADx performance, an information-fusion method was used to combine the prediction scores generated by two SVM classifiers. RESULTS: Areas under receiver operating characteristic curves (AUC) generated by QI feature and serum biomarker-based SVMs were 0.81 ± 0.03 and 0.69 ± 0.05, respectively. Using an optimal weighted fusion method to combine prediction scores generated by two SVMs, AUC value significantly increased to 0.85 ± 0.03 (P < 0.05). CONCLUSIONS: This study demonstrates (a) higher CADx performance by using QI features than using the serum biomarkers and (b) feasibility of further improving CADx performance by fusion of QI features and serum biomarkers, which indicates that QI features and serum biomarkers contain the complementary classification information.

Automatic detection of pulmonary nodules in CT images by incorporating 3D tensor filtering with local image feature analysis.

Computer-aided detection (CAD) technology has been developed and demonstrated its potential to assist radiologists in detecting pulmonary nodules especially at an early stage. In this paper, we present a novel scheme for automatic detection of pulmonary nodules in CT images based on a 3D tensor filtering algorithm and local image feature analysis. We first apply a series of preprocessing steps to segment the lung volume and generate the isotropic volumetric CT data. Next, a unique 3D tensor filtering approach and local image feature analysis are used to detect nodule candidates. A 3D level set segmentation method is used to correct and refine the boundaries of nodule candidates subsequently. Then, we extract the features of the detected candidates and select the optimal features by using a CFS (Correlation Feature Selection) subset evaluator attribute selection method. Finally, a random forest classifier is trained to classify the detected candidates. The performance of this CAD scheme is validated using two datasets namely, the LUNA16 (Lung Nodule Analysis 2016) database and the ANODE09 (Automatic Nodule Detection 2009) database. By applying a 10-fold cross-validation method, the CAD scheme yielded a sensitivity of 79.3% at an average of 4 false positive detections per scan (FP/Scan) for the former dataset, and a sensitivity of 84.62% and 2.8 FP/Scan for the latter dataset, respectively. Our detection results show that the use of 3D tensor filtering algorithm combined with local image feature analysis constitutes an effective approach to detect pulmonary nodules.

Computer-aided detection of pulmonary nodules using dynamic self-adaptive template matching and a FLDA classifier.

Improving the performance of computer-aided detection (CAD) system for pulmonary nodules is still an important issue for its future clinical applications. This study aims to develop a new CAD scheme for pulmonary nodule detection based on dynamic self-adaptive template matching and Fisher linear discriminant analysis (FLDA) classifier. We first segment and repair lung volume by using OTSU algorithm and three-dimensional (3D) region growing. Next, the suspicious regions of interest (ROIs) are extracted and filtered by applying 3D dot filtering and thresholding method. Then, pulmonary nodule candidates are roughly detected with 3D dynamic self-adaptive template matching. Finally, we optimally select 11 image features and apply FLDA classifier to reduce false positive detections. The performance of the new method is validated by comparing with other methods through experiments using two groups of public datasets from Lung Image Database Consortium (LIDC) and ANODE09. By a 10-fold cross-validation experiment, the new CAD scheme finally has achieved a sensitivity of 90.24% and a false-positive (FP) of 4.54 FP/scan on average for the former dataset, and a sensitivity of 84.1% with 5.59 FP/scan for the latter. By comparing with other previously reported CAD schemes tested on the same datasets, the study proves that this new scheme can yield higher and more robust results in detecting pulmonary nodules.