Activation of CD3+ T cells by Helicobacter pylori DNA vaccines in potential immunotherapy of gastric carcinoma

Most of gastric carcinoma (GC) is attributed to infection by Helicobacter pylori (H. pylori) but there is increasing evidence that the positive H. pylori status correlates with better prognosis in GC. The H. pylori-induced cellular immune response may suppress cancer and in this work, recombinant pcDNA3 plasmids encoding various fragments of H. pylori virulence genes of cagA, vacA and babA are constructed and combined into groups to immunize BALB/c mice. The activated splenic CD3⁺ T cells are purified and the anticancer effects are investigated in vitro and in vivo. The H. pylori DNA vaccines induce a shift in the response from Th1 to Th2 that mimicks the immune status in patients of GC with chronic H. pylori infection. The stimulated CD3⁺ T cells inhibit the growth of human GC cells in vitro and adoptive transfusions of the CD3⁺ T cells suppress the growth of GC xenograft in vivo. The effects may be caused by the larger ratios of infiltrated CD8⁺/CD4⁺ T cells, reduced infiltration of regulatory FOXP3⁺ T cells, and enhanced apoptosis induced by upregulation of Caspase-9/Caspase-3 and downregulation of Survivin. Our results reveal the potential immunotherapeutic value of H. pylori vaccine-activated CD3⁺ T cells in those with advanced GC.     

Nucleus classification in histology images using message passing network

Identification of nuclear components in the histology landscape is an important step towards developing computational pathology tools for the profiling of tumor micro-environment. Most existing methods for the identification of such components are limited in scope due to heterogeneous nature of the nuclei. Graph-based methods offer a natural way to formulate the nucleus classification problem to incorporate both appearance and geometric locations of the nuclei. The main challenge is to define models that can handle such an unstructured domain. Current approaches focus on learning better features and then employ well-known classifiers for identifying distinct nuclear phenotypes. In contrast, we propose a message passing network that is a fully learnable framework build on classical network flow formulation. Based on physical interaction of the nuclei, a nearest neighbor graph is constructed such that the nodes represent the nuclei centroids. For each edge and node, appearance and geometric features are computed which are then used for the construction of messages utilized for diffusing contextual information to the neighboring nodes. Such an algorithm can infer global information over an entire network and predict biologically meaningful nuclear communities. We show that learning such communities improves the performance of nucleus classification task in histology images. The proposed algorithm can be used as a component in existing state-of-the-art methods resulting in improved nucleus classification performance across four different publicly available datasets.     

Longitudinal diffusion tensor imaging of patients with degenerative cervical myelopathy following decompression surgery

Previous studies have reported the utility of diffusion tensor imaging (DTI) as an imaging biomarker for the severity of myelopathy and subsequent surgical outcome in patients with degenerative cervical myelopathy (DCM). We hypothesized that DTI may reflect neurological recovery following surgery. The purpose of this study was to evaluate the ability of DTI to assess the post-operative alteration of neural status in patients with DCM as well as to predict post-operative recovery. We enrolled 15 patients with DCM who underwent decompression surgery. The Japanese Orthopaedic Association (JOA) score was evaluated before and 1 year after surgery. The participants were examined using DTI on a 3.0 T magnetic resonance scanner before, and 1 year after surgery. Fractional anisotropy (FA) and mean diffusivity (MD) were assessed for both time points. The correlations between the pre- and post-operative FA and MD values and the pre- and post-operative JOA scores were analyzed. Although the JOA score improved significantly after surgery from 8.9 to 12.3, there was no significant change between the pre- and post-operative FA and MD values. The post-operative outcomes after 1 year moderately correlated with the pre-operative FA values (Spearman’s ρ = 0.55, p = 0.03 and Spearman’s ρ = 0.56, p = 0.03 for change and recovery rate of the JOA score, respectively). However, there was no correlation between the post-operative FA and post-operative JOA scores nor between MD and clinical outcomes. DTI cannot be utilized as a biomarker for post-operative alterations of neural status of the spinal cord; however, pre-operative DTI may be useful as a predictor of surgical outcomes.     

Multi-site clustering and nested feature extraction for identifying autism spectrum disorder with resting-state fMRI

Brain functional connectivity (FC) derived from resting-state functional magnetic resonance imaging (rs-fMRI) has been widely employed to study neuropsychiatric disorders such as autism spectrum disorder (ASD). Existing studies usually suffer from (1) significant data heterogeneity caused by different scanners or studied populations in multiple sites, (2) curse of dimensionality caused by millions of voxels in each fMRI scan and a very limited number (tens or hundreds) of training samples, and (3) poor interpretability, which hinders the identification of reproducible disease biomarkers. To this end, we propose a Multi-site Clustering and Nested Feature Extraction (MC-NFE) method for fMRI-based ASD detection. Specifically, we first divide multi-site training data into ASD and healthy control (HC) groups. To model inter-site heterogeneity within each category, we use a similarity-driven multiview linear reconstruction model to learn latent representations and perform subject clustering within each group. We then design a nested singular value decomposition (SVD) method to mitigate inter-site heterogeneity and extract FC features by learning both local cluster-shared features across sites within each category and global category-shared features across ASD and HC groups, followed by a linear support vector machine (SVM) for ASD detection. Experimental results on 609 subjects with rs-fMRI from the ABIDE database with 21 imaging sites suggest that the proposed MC-NFE outperforms several state-of-the-art methods in ASD detection. The most discriminative FCs identified by the MC-NFE are mainly located in default mode network, salience network, and cerebellum region, which could be used as potential biomarkers for fMRI-based ASD analysis.     

Disease prediction with edge-variational graph convolutional networks

The need for computational models that can incorporate imaging data with non-imaging data while investigating inter-subject associations arises in the task of population-based disease analysis. Although off-the-shelf deep convolutional neural networks have empowered representation learning from imaging data, incorporating data of different modalities complementarily in a unified model to improve the disease diagnostic quality is still challenging. In this work, we propose a generalizable graph-convolutional framework for population-based disease prediction on multi-modal medical data. Unlike previous methods constructing a static affinity population graph in a hand-crafting manner, the proposed framework can automatically learn to build a population graph with variational edges, which we show can be optimized jointly with spectral graph convolutional networks. In addition, to estimate the predictive uncertainty related to the constructed graph, we propose Monte–Carlo edge dropout uncertainty estimation. Experimental results on four multi-modal datasets demonstrate that the proposed method can substantially improve the predictive accuracy for Autism Spectrum Disorder, Alzheimer’s disease, and ocular diseases. A sufficient ablation study with in-depth discussion is conducted to evaluate the effectiveness of each component and the choice of algorithmic details of the proposed method. The results indicate the potential and extendability of the proposed framework in leveraging multi-modal data for population-based disease prediction.     

Percutaneous CT-Guided Cryoablation of the Celiac Plexus: A Retrospective Cohort Comparison with Ethanol

PurposeTo retrospectively analyze and compare the incidence of diarrhea in patients who underwent cryoablation of the celiac plexus for intractable abdominal pain versus ethanol therapy over a 5-year period.Materials and MethodsFrom June 2014 to August 2019, 83 patients were identified who underwent neurolysis of the celiac plexus for management of intractable abdominal pain by using either cryoablation (n = 39 [59% female; age range, 36–79 years old [average, 60 ± 11 years old]) or alcohol (n = 44 [48% female; age range, 29–76 years old [average, 60 ± 12 years old]). Pain scores and reports of procedure-related complications or side effects, with special attention to diarrhea and/or other gastrointestinal symptoms, were collected from follow-up visits at 1 week, 1 month, and 3 months post-intervention and were compared between groups.ResultsThe mean time of follow-up was 17.7 days. Four patients who underwent cryoablation developed gastrointestinal symptoms consisting of 2 cases of nausea and vomiting and 2 cases of diarrhea (5.1%). Twelve patients who underwent ethanol ablation developed gastrointestinal symptoms, including 1 case of nausea, 3 cases of vomiting, and 9 cases of diarrhea (20.5%). There was a significantly higher incidence of both diarrhea (chi-squared likelihood ratio, P = .03) and overall gastrointestinal symptoms (chi-squared likelihood ratio, P = .04) in the ethanol group than in the cryoablation group.ConclusionsCryoablation of the celiac plexus may provide a new treatment option for intractable abdominal pain, and it appears to have a lower incidence of diarrhea and fewer gastrointestinal side effects than ablation using ethanol.