Natural Adrenocorticotropic Hormone (ACTH) Relieves Acute Inflammation in Gout Patients by Changing the Function of Macrophages.

Gout is a common arthritis caused by deposition of monosodium urate crystals. Macrophage is crucial in the process of monosodium urate (MSU)-induced inflammation. Although it has been reported that adrenocorticotropic hormone (ACTH) in nature can be used to cure urarthritis, the mechanism concerning macrophage is still not clear. However, gout patients manifest other complications, such as hypertension, diabetes, chronic kidney disease, and hormone intolerance, which limit efficacy of some of these first-line drugs. Therefore, this study aims to explore how natural ACTH can alleviate urarthritis through functional changes in macrophage. We analyzed the variations in VAS pain scores of five patients, knowing the time of action and detecting the level of cortisol and ACTH in patients 24 hours after the application of ACTH. The effect of natural ACTH on joint inflammation and the level of cortisol in blood in the mouse model was evaluated by studies in vivo. In vitro studies, we evaluated the effect of natural ACTH on macrophages and revealed different functions of ACTH and dexamethasone on macrophages in the transcriptional level. In patients with acute gout, natural ACTH can quickly alleviate pain and does not affect the level of cortisol and ACTH. Natural ACTH is able to ease the swelling and inflammatory cell infiltration caused by arthritis, without changing the level of cortisol. Besides, natural ACTH in vitro can alleviate acute gouty inflammation by regulating phagocytosis and polarization of macrophage, which also exerts different effects on the transcription of some related genes. Natural ACTH is able to alleviate acute gouty inflammation by regulating macrophage, and this effect differs from that of dexamethasone at the transcriptional level.

IL-37 blocks gouty inflammation by shaping macrophages into a non-inflammatory phagocytic phenotype.

OBJECTIVE: Interleukin (IL)-37 is a natural suppressor of inflammation. Macrophages play an important role in acute gout flare by dominating the inflammation and spontaneous relief. We have reported that IL-37 could limit runaway inflammation in gout. Here we focus on whether IL-37 inhibits gouty inflammation by altering macrophage functions, and how it does so. METHODS: Macrophage functions were evaluated in terms of phagocytosis, pyroptosis, polarization and metabolism. Phagocytosis and polarization of macrophages were detected by side scattering and double-labelling induced nitrogen monoxide synthase (iNOS)/arginase-1 (Arg-1) using flow cytometry, respectively. Transcription of pyroptosis-related molecules was detected by qPCR. Metabolomics was performed by liquid chromatograph mass spectrometer. Human IL-37 knock-in mice and a model with point mutation (S9A) at mouse Gsk3b locus were created by CRISPR/Cas-mediated genome engineering. MSU was injected into the paws and peritoneal cavity to model acute gout. Vernier calliper was used to measure the thickness of the paws. The mice paws and human synovium tissues or tophi were collected for pathological staining. Peritoneal fluid of mice was used to enrich macrophages to detect polarization. RESULTS: IL-37 promoted non-inflammatory phagocytic activity of macrophages by enhancing phagocytosis of MSU, reducing transcription of pyroptosis-related proteins and release of inflammatory cytokines, protecting mitochondrial function, and mediating metabolic reprogramming in MSU-treated THP-1 cells. These multifaceted roles of IL-37 were partly depended on the mediation of glycogen synthase kinase-3ß (GSK-3ß). CONCLUSIONS: Our study revealed that IL-37 could shape macrophages into a 'silent' non-inflammatory phagocytic fashion. IL-37 may become a potentially valuable treatment option for patients of chronic gout, especially for those with tophi.

A short-term follow-up CT based radiomics approach to predict response to immunotherapy in advanced non-small-cell lung cancer.

To develop a short-term follow-up CT-based radiomics approach to predict response to immunotherapy in advanced non-small-cell lung cancer (NSCLC) and investigate the prognostic value of radiomics features in predicting progression-free survival (PFS) and overall survival (OS). We first retrospectively collected 224 advanced NSCLC patients from two centers, and divided them into a primary cohort and two validation cohorts respectively. Then, we processed CT scans with a series of image preprocessing techniques namely, tumor segmentation, image resampling, feature extraction and normalization. To select the optimal features, we applied the feature ranking with recursive feature elimination method. After resampling the training dataset with a synthetic minority oversampling technique, we applied the support vector machine classifier to build a machine-learning-based classification model to predict response to immunotherapy. Finally, we used Kaplan-Meier (KM) survival analysis method to evaluate prognostic value of rad-score generated by CT-radiomics model. In two validation cohorts, the delta-radiomics model significantly improved the area under receiver operating characteristic curve from 0.64 and 0.52 to 0.82 and 0.87, respectively (P < .05). In sub-group analysis, pre- and delta-radiomics model yielded higher performance for adenocarcinoma (ADC) patients than squamous cell carcinoma (SCC) patients. Through the KM survival analysis, the rad-score of delta-radiomics model had a significant prognostic for PFS and OS in validation cohorts (P < .05). Our results demonstrated that (1) delta-radiomics model could improve the prediction performance, (2) radiomics model performed better on ADC patients than SCC patients, (3) delta-radiomics model had prognostic values in predicting PFS and OS of NSCLC patients.

Volumetric segmentation of ground glass nodule based on 3D attentional cascaded residual U-Net and conditional random field.

BACKGROUND: Ground glass nodule (GGN) segmentation is one of the important and challenging tasks in diagnosing early-stage lung adenocarcinomas. Manually delineating of 3D GGN in a computed tomography (CT) image is a subjective, laborious, and tedious task, which presents poor repeatability. PURPOSE: To reduce the annotation burden and improve the segmentation performance, this study proposes a 3D deep learning-based volumetric segmentation model to segment the GGN in CT images. METHODS: A total of 379 GGNs were retrospectively collected from the public database, Shanghai Pulmonary Hospital (SHPH), and Fudan University Shanghai Cancer Center (FUSCC). First, a series of image preprocessing techniques involving image resampling, intensity normalization, 3D nodule patch cropping, and data augmentation, were adopted to generate the input images for the deep learning model by using CT scans. Then, a 3D attentional cascaded residual network (ACRU-Net) was proposed to develop the deep learning-based segmentation model by using the residual network and the atrous spatial pyramid pooling module. To improve the model performance, a voxel-based conditional random field (CRF) method was used to optimize the segmentation results. Finally, a balanced cross-entropy and Dice combined loss function was applied to train and build the segmentation model. RESULTS: Testing on SHPH and FUSCC datasets, the proposed method generates the Dice coefficients of 0.721 ± 0.167 and 0.733 ± 0.100, respectively, which are higher than those of 3D residual U-Net and ACRU-Net without CRF optimization. CONCLUSIONS: The results demonstrated that combining 3D ACRU-Net and CRF effectively improved the GGN segmentation performance. The proposed segmentation model may provide a potential tool to help the radiologist in the segmentation and diagnosis of 3D GGN.

Deep Learning-Based Stage-Wise Risk Stratification for Early Lung Adenocarcinoma in CT Images: A Multi-Center Study.

This study aims to develop a deep neural network (DNN)-based two-stage risk stratification model for early lung adenocarcinomas in CT images, and investigate the performance compared with practicing radiologists. A total of 2393 GGNs were retrospectively collected from 2105 patients in four centers. All the pathologic results of GGNs were obtained from surgically resected specimens. A two-stage deep neural network was developed based on the 3D residual network and atrous convolution module to diagnose benign and malignant GGNs (Task1) and classify between invasive adenocarcinoma (IA) and non-IA for these malignant GGNs (Task2). A multi-reader multi-case observer study with six board-certified radiologists' (average experience 11 years, range 2-28 years) participation was conducted to evaluate the model capability. DNN yielded area under the receiver operating characteristic curve (AUC) values of 0.76 ± 0.03 (95% confidence interval (CI): (0.69, 0.82)) and 0.96 ± 0.02 (95% CI: (0.92, 0.98)) for Task1 and Task2, which were equivalent to or higher than radiologists in the senior group with average AUC values of 0.76 and 0.95, respectively (p > 0.05). With the CT image slice thickness increasing from 1.15 mm ± 0.36 to 1.73 mm ± 0.64, DNN performance decreased 0.08 and 0.22 for the two tasks. The results demonstrated (1) a positive trend between the diagnostic performance and radiologist's experience, (2) the DNN yielded equivalent or even higher performance in comparison with senior radiologists, and (3) low image resolution decreased model performance in predicting the risks of GGNs. Once tested prospectively in clinical practice, the DNN could have the potential to assist doctors in precision diagnosis and treatment of early lung adenocarcinoma.

MRPL15 is a novel prognostic biomarker and therapeutic target for epithelial ovarian cancer.

PURPOSE: To analyze the role of six human epididymis protein 4 (HE4)-related mitochondrial ribosomal proteins (MRPs) in ovarian cancer and selected MRPL15, which is most closely related to the tumorigenesis and prognosis of ovarian cancer, for further analyses. METHODS: Using STRING database and MCODE plugin in Cytoscape, six MRPs were identified among genes that are upregulated in response to HE4 overexpression in epithelial ovarian cancer cells. The Cancer Genome Atlas (TCGA) ovarian cancer, GTEX, Oncomine, and TISIDB were used to analyze the expression of the six MRPs. The prognostic impact and genetic variation of these six MRPs in ovarian cancer were evaluated using Kaplan-Meier Plotter and cBioPortal, respectively. MRPL15 was selected for immunohistochemistry and GEO verification. TCGA ovarian cancer data, gene set enrichment analysis, and Enrichr were used to explore the mechanism of MRPL15 in ovarian cancer. Finally, the relationship between MRPL15 expression and immune subtype, tumor-infiltrating lymphocytes, and immune regulatory factors was analyzed using TCGA ovarian cancer data and TISIDB. RESULTS: Six MRPs (MRPL10, MRPL15, MRPL36, MRPL39, MRPS16, and MRPS31) related to HE4 in ovarian cancer were selected. MRPL15 was highly expressed and amplified in ovarian cancer and was related to the poor prognosis of patients. Mechanism analysis indicated that MRPL15 plays a role in ovarian cancer through pathways such as the cell cycle, DNA repair, and mTOR 1 signaling. High expression of MRPL15 in ovarian cancer may be associated with its amplification and hypomethylation. Additionally, MRPL15 showed the lowest expression in C3 ovarian cancer and was correlated with proliferation of CD8+ T cells and dendritic cells as well as TGFßR1 and IDO1 expression. CONCLUSION: MRPL15 may be a prognostic indicator and therapeutic target for ovarian cancer. Because of its close correlation with HE4, this study provides insights into the mechanism of HE4 in ovarian cancer.

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.

A Risk Signature with Nine Stemness Index-Associated Genes for Predicting Survival of Patients with Uterine Corpus Endometrial Carcinoma.

PURPOSE: To identify mRNA expression-based stemness index- (mRNAsi-) related genes and build an mRNAsi-related risk signature for endometrial cancer. METHODS: We collected mRNAsi data of endometrial cancer samples from The Cancer Genome Atlas (TCGA) and analyzed their relationship with the main clinicopathological characteristics and prognosis of endometrial cancer patients. We screened the top 50% of the genes in TCGA for weighted gene correlation network analysis (WGCNA) to explore mRNAsi-related gene sets. Among these mRNAsi-related genes, we further screened for those related to the prognosis of endometrial cancer patients via univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression analysis. Using stepwise multivariate Cox regression analysis, a stemness index-related risk signature was constructed. Finally, we identified potential prognostic biomarkers for endometrial cancer by combining the GEO database and immunohistochemical staining. RESULTS: The mRNAsi of endometrial cancer samples was significantly higher than that of normal samples and was related to the International Federation of Gynecology and Obstetrics (FIGO) stage, pathological grade, postoperative tumor status, and overall survival of endometrial cancer patients. We identified 21 mRNAsi-related gene modules, and 1,324 genes were obtained from the most relevant module. TCGA samples were divided into training and validation cohorts, and the training cohort was used to construct a nine-mRNAsi-related gene signature (B3GAT2, CD3EAP, DMC1, FRMPD3, LINC01224, LINC02068, LY6H, NR6A1, and TLE2). High-risk and low-risk patients had significant prognostic differences, and the risk signature could accurately predict their 1-, 3-, and 5-year survival. The nomogram composed of risk score and multiple clinicopathological features could accurately predict 1-, 3-, and 5-year survival. Finally, CD3EAP was found to be a novel prognostic biomarker for endometrial cancer. CONCLUSION: Endometrial cancer cell stemness is related to patient prognosis. The nine-gene risk signature is an independent prognostic factor and can accurately predict endometrial cancer patient prognosis.

Prevalent Eurasian avian-like H1N1 swine influenza virus with 2009 pandemic viral genes facilitating human infection.

Pigs are considered as important hosts or "mixing vessels" for the generation of pandemic influenza viruses. Systematic surveillance of influenza viruses in pigs is essential for early warning and preparedness for the next potential pandemic. Here, we report on an influenza virus surveillance of pigs from 2011 to 2018 in China, and identify a recently emerged genotype 4 (G4) reassortant Eurasian avian-like (EA) H1N1 virus, which bears 2009 pandemic (pdm/09) and triple-reassortant (TR)-derived internal genes and has been predominant in swine populations since 2016. Similar to pdm/09 virus, G4 viruses bind to human-type receptors, produce much higher progeny virus in human airway epithelial cells, and show efficient infectivity and aerosol transmission in ferrets. Moreover, low antigenic cross-reactivity of human influenza vaccine strains with G4 reassortant EA H1N1 virus indicates that preexisting population immunity does not provide protection against G4 viruses. Further serological surveillance among occupational exposure population showed that 10.4% (35/338) of swine workers were positive for G4 EA H1N1 virus, especially for participants 18 y to 35 y old, who had 20.5% (9/44) seropositive rates, indicating that the predominant G4 EA H1N1 virus has acquired increased human infectivity. Such infectivity greatly enhances the opportunity for virus adaptation in humans and raises concerns for the possible generation of pandemic viruses.

A deep residual learning network for predicting lung adenocarcinoma manifesting as ground-glass nodule on CT images.

OBJECTIVE: To develop a deep learning-based artificial intelligence (AI) scheme for predicting the likelihood of the ground-glass nodule (GGN) detected on CT images being invasive adenocarcinoma (IA) and also compare the accuracy of this AI scheme with that of two radiologists. METHODS: First, we retrospectively collected 828 histopathologically confirmed GGNs of 644 patients from two centers. Among them, 209 GGNs are confirmed IA and 619 are non-IA, including 409 adenocarcinomas in situ and 210 minimally invasive adenocarcinomas. Second, we applied a series of pre-preprocessing techniques, such as image resampling, rescaling and cropping, and data augmentation, to process original CT images and generate new training and testing images. Third, we built an AI scheme based on a deep convolutional neural network by using a residual learning architecture and batch normalization technique. Finally, we conducted an observer study and compared the prediction performance of the AI scheme with that of two radiologists using an independent dataset with 102 GGNs. RESULTS: The new AI scheme yielded an area under the receiver operating characteristic curve (AUC) of 0.92 ± 0.03 in classifying between IA and non-IA GGNs, which is equivalent to the senior radiologist's performance (AUC 0.92 ± 0.03) and higher than the score of the junior radiologist (AUC 0.90 ± 0.03). The Kappa value of two sets of subjective prediction scores generated by two radiologists is 0.6. CONCLUSIONS: The study result demonstrates using an AI scheme to improve the performance in predicting IA, which can help improve the development of a more effective personalized cancer treatment paradigm. KEY POINTS: • The feasibility of using a deep learning method to predict the likelihood of the ground-glass nodule being invasive adenocarcinoma. • Residual learning-based CNN model improves the performance in classifying between IA and non-IA nodules. • Artificial intelligence (AI) scheme yields higher performance than radiologists in predicting invasive adenocarcinoma.

Computer-aided diagnosis of ground-glass opacity pulmonary nodules using radiomic features analysis.

This study aims to develop a CT-based radiomic features analysis approach for diagnosis of ground-glass opacity (GGO) pulmonary nodules, and also assess whether computer-aided diagnosis (CADx) performance changes in classifying between benign and malignant nodules associated with histopathological subtypes namely, adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC), respectively. The study involves 182 histopathology-confirmed GGO nodules collected from two cancer centers. Among them, 59 are benign, 50 are AIS, 32 are MIA, and 41 are IAC nodules. Four training/testing data sets-(1) all nodules, (2) benign and AIS nodules, (3) benign and MIA nodules, (4) benign and IAC nodules-are assembled based on their histopathological subtypes. We first segment pulmonary nodules depicted in CT images by using a 3D region growing and geodesic active contour level set algorithm. Then, we computed and extracted 1117 quantitative imaging features based on the 3D segmented nodules. After conducting radiomic features normalization process, we apply a leave-one-out cross-validation (LOOCV) method to build models by embedding with a Relief feature selection, synthetic minority oversampling technique (SMOTE) and three machine-learning classifiers namely, support vector machine classifier, logistic regression classifier and Gaussian Naïve Bayes classifier. When separately using four data sets to train and test three classifiers, the average areas under receiver operating characteristic curves (AUC) are 0.75, 0.55, 0.77 and 0.93, respectively. When testing on an independent data set, our scheme yields higher accuracy than two radiologists (61.3% versus radiologist 1: 53.1% and radiologist 2: 56.3%). This study demonstrates that: (1) the feasibility of using CT-based radiomic features analysis approach to distinguish between benign and malignant GGO nodules, (2) higher performance of CADx scheme in diagnosing GGO nodules comparing with radiologist, and (3) a consistently positive trend between classification performance and invasive grade of GGO nodules. Thus, to improve the CADx performance in diagnosing of GGO nodules, one should assemble an optimal training data set dominated with more nodules associated with non-invasive lung adenocarcinoma (i.e. AIS and MIA).

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.

Maskless Hydrophilic Patterning of the Superhydrophobic Aluminum Surface by an Atmospheric Pressure Microplasma Jet for Water Adhesion Controlling.

Superhydrophobic surfaces with hydrophilic patterns have great application potential in various fields, such as microfluidic systems and water harvesting. However, many reported preparation methods involve complicated devices and/or masks, making fabrication of these patterned surfaces time-consuming and inefficient. Here, we propose a highly efficient, simple, and maskless microplasma jet (MPJ) treatment method to prepare hydrophilic patterns such as dots, lines, and curves on superhydrophobic aluminum substrates. Contact angles, sliding angles, adhesive forces, and droplet impact behavior of the created patterns are investigated and analyzed. The prepared "dot" patterns exhibit great water adhesion, whereas the "line" patterns show anisotropic adhesion. Additionally, the MPJ treatment does not obviously change the surface structures, which makes it possible to achieve repeatable patterning on one substrate. The adhesion behavior of these patterns could be adjusted using MPJs with different diameters. MPJs with larger diameters are efficient for the creation of patterns with high water adhesion, which can be potentially used for open-channel lab-on-chip systems (e.g., continuous water transportation), whereas MPJs with smaller diameters are preferable in preparing patterns with low water adhesion for diverse applications in biomedical fields (e.g., lossless liquid droplet mixing and cell screening).

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.

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.

3Cpro of foot-and-mouth disease virus antagonizes the interferon signaling pathway by blocking STAT1/STAT2 nuclear translocation.

UNLABELLED: Foot-and-mouth disease virus (FMDV) causes a highly contagious, debilitating disease in cloven-hoofed animals with devastating economic consequences. To survive in the host, FMDV has evolved to antagonize the host type I interferon (IFN) response. Previous studies have reported that the leader proteinase (L(pro)) and 3C(pro) of FMDV are involved in the inhibition of type I IFN production. However, whether the proteins of FMDV can inhibit type I IFN signaling is less well understood. In this study, we first found that 3C(pro) of FMDV functioned to interfere with the JAK-STAT signaling pathway. Expression of 3C(pro) significantly reduced the transcript levels of IFN-stimulated genes (ISGs) and IFN-stimulated response element (ISRE) promoter activity. The protein level, tyrosine phosphorylation of STAT1 and STAT2, and their heterodimerization were not affected. However, the nuclear translocation of STAT1/STAT2 was blocked by the 3C(pro) protein. Further mechanistic studies demonstrated that 3C(pro) induced proteasome- and caspase-independent protein degradation of karyopherin α1 (KPNA1), the nuclear localization signal receptor for tyrosine-phosphorylated STAT1, but not karyopherin α2, α3, or α4. Finally, we showed that the protease activity of 3C(pro) contributed to the degradation of KPNA1 and thus blocked STAT1/STAT2 nuclear translocation. Taken together, results of our experiments describe for the first time a novel mechanism by which FMDV evolves to inhibit IFN signaling and counteract host innate antiviral responses. IMPORTANCE: We show that 3C(pro) of FMDV antagonizes the JAK-STAT signaling pathway by blocking STAT1/STAT2 nuclear translocation. Furthermore, 3C(pro) induces KPNA1 degradation, which is independent of proteasome and caspase pathways. The protease activity of 3C(pro) contributes to the degradation of KPNA1 and governs the ability of 3C(pro) to inhibit the JAK-STAT signaling pathway. This study uncovers a novel mechanism evolved by FMDV to antagonize host innate immune responses.