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Background This study aimed to evaluate the clinical efficacy of coaxial percutaneous Iodine-125 (125I) seed implantation in combination with arterial infusion chemotherapy for the treatment of advanced pancreatic cancer through a randomized controlled trial. Methods 101 patients with advanced pancreatic cancer were randomized into two groups: a control group treated with systemic intravenous chemotherapy, and an experimental group of 125I seed implantation in combination with arterial infusion chemotherapy. Outcomes including tumor control, abdominal pain relief, and survival time were compared in these two groups.Result Pretreatment abdominal pain scores were comparable in both groups, whereas the abdominal pain scores at 1 and 3 months post-treatment were significantly lower in the control group than in the experimental group (1 month 3.74±1.54 vs 4.48±1.46,p=0.015; 3 months: 3.64 ±2.21 vs 5.40±1.56, p

Determining the aggressiveness of gliomas, termed grading, is a critical step toward treatment optimization to increase the survival rate and decrease treatment toxicity for patients. Streamlined grading using molecular information has the potential to facilitate decision making in the clinic and aid in treatment planning. In recent years, molecular markers have increasingly gained importance in the classification of tumors. In this study, we propose a novel hierarchical voting-based methodology for improving the performance results of the feature selection stage and machine learning models for glioma grading with clinical and molecular predictors. To identify the best scheme for the given soft-voting-based ensemble learning model selections, we utilized publicly available TCGA and CGGA datasets and employed four dimensionality reduction methods to carry out a voting-based ensemble feature selection and five supervised models, with a total of sixteen combination sets. We also compared our proposed feature selection method with the LASSO feature selection method in isolation. The computational results indicate that the proposed method achieves 87.606% and 79.668% accuracy rates on TCGA and CGGA datasets, respectively, outperforming the LASSO feature selection method.

Radiofrequency catheter ablation (RFCA) is widely used to treat arrhythmias. However, for atrial fibrillation, the recurrence rate after RFCA is still high. The development of an animal model that mimics the recurrence of electrical conduction after ablation is essential before we can explore the mechanisms involved or develop new therapeutic strategies.Eighteen beagles aged 12 to 24 months were randomly assigned to this study. RFCA ablation of the right atrial free wall was performed. Then, electrical block and conduction recovery in the ablation area were evaluated using voltage mapping and pacing tests assisted by CARTO3 system. Finally, liposome doxorubicin (DOX-L) was intravenously injected after ablation to investigate the effect of DOX-L on this animal model.The conduction block (CB) rates at 5 min after ablation were 16.7%, 83.3%, and 100%, corresponding to 30w, 35w, and 40w power, respectively. However, after 20 min, the rate of CB was 0%, 33.3%, and 75%; thus, the combined success rate of CB and conduction recurrence was 16.7%, 50%, and 25%, respectively. The optimal ablation parameter is 35 W for 20 s, based on the CB rate, REC rate. After 10 days of ablation, the residual conduction recurrence rate was as high as 83.3% in the RFCA alone group, whereas there was no recurrence with RFCA combined with DOX-L treatment.The novel model accurately simulated the electrical conduction recurrence after cardiac radiofrequency ablation. RFCA combined with DOX-L treatment dramatically reduces the recurrence rate of electrical conduction after ablation.

Purpose Gliomas are the most common primary tumor of the brain and are classified into grades I-IV of the World Health Organization (WHO), based on their invasively histological appearance. Gliomas grading plays an important role to determine the treatment plan and prognosis prediction. In this study we propose two novel methods for automatic, non-invasively distinguishing low-grade (Grades II and III) glioma (LGG) and high-grade (grade IV) glioma (HGG) on conventional MRI images by using deep convolutional neural networks (CNNs). Methods All MRI images have been preprocessed first by rigid image registration and intensity inhomogeneity correction. Both proposed methods consist of two steps: (a) three-dimensional (3D) brain tumor segmentation based on a modification of the popular U-Net model; (b) tumor classification on segmented brain tumor. In the first method, the slice with largest area of tumor is determined and the state-of-the-art mask R-CNN model is employed for tumor grading. To improve the performance of the grading model, a two-dimensional (2D) data augmentation has been implemented to increase both the amount and the diversity of the training images. In the second method, denoted as 3DConvNet, a 3D volumetric CNNs is applied directly on bounding image regions of segmented tumor for classification, which can fully leverage the 3D spatial contextual information of volumetric image data. Results The proposed schemes were evaluated on The Cancer Imaging Archive (TCIA) low grade glioma (LGG) data, and the Multimodal Brain Tumor Image Segmentation (BraTS) Benchmark 2018 training datasets with fivefold cross validation. All data are divided into training, validation, and test sets. Based on biopsy-proven ground truth, the performance metrics of sensitivity, specificity, and accuracy are measured on the test sets. The results are 0.935 (sensitivity), 0.972 (specificity), and 0.963 (accuracy) for the 2D Mask R-CNN based method, and 0.947 (sensitivity), 0.968 (specificity), and 0.971 (accuracy) for the 3DConvNet method, respectively. In regard to efficiency, for 3D brain tumor segmentation, the program takes around ten and a half hours for training with 300 epochs on BraTS 2018 dataset and takes only around 50 s for testing of a typical image with a size of 160 × 216 × 176. For 2D Mask R-CNN based tumor grading, the program takes around 4 h for training with around 60 000 iterations, and around 1 s for testing of a 2D slice image with size of 128 × 128. For 3DConvNet based tumor grading, the program takes around 2 h for training with 10 000 iterations, and 0.25 s for testing of a 3D cropped image with size of 64 × 64 × 64, using a DELL PRECISION Tower T7910, with two NVIDIA Titan Xp GPUs. Conclusions Two effective glioma grading methods on conventional MRI images using deep convolutional neural networks have been developed. Our methods are fully automated without manual specification of region-of-interests and selection of slices for model training, which are common in traditional machine learning based brain tumor grading methods. This methodology may play a crucial role in selecting effective treatment options and survival predictions without the need for surgical biopsy.

Recent technological developments have led to an increase in the size and types of data in the medical field derived from multiple platforms such as proteomic, genomic, imaging, and clinical data. Many machine learning models have been developed to support precision/personalized medicine initiatives such as computer-aided detection, diagnosis, prognosis, and treatment planning by using large-scale medical data. Bias and class imbalance represent two of the most pressing challenges for machine learning-based problems, particularly in medical (e.g., oncologic) data sets, due to the limitations in patient numbers, cost, privacy, and security of data sharing, and the complexity of generated data. Depending on the data set and the research question, the methods applied to address class imbalance problems can provide more effective, successful, and meaningful results. This review discusses the essential strategies for addressing and mitigating the class imbalance problems for different medical data types in the oncologic domain.

Glioblastomas (GBM) are rapidly growing, aggressive, nearly uniformly fatal, and the most common primary type of brain cancer. They exhibit significant heterogeneity and resistance to treatment, limiting the ability to analyze dynamic biological behavior that drives response and resistance, which are central to advancing outcomes in glioblastoma. Analysis of the proteome aimed at signal change over time provides a potential opportunity for non-invasive classification and examination of the response to treatment by identifying protein biomarkers associated with interventions. However, data acquired using large proteomic panels must be more intuitively interpretable, requiring computational analysis to identify trends. Machine learning is increasingly employed, however, it requires feature selection which has a critical and considerable effect on machine learning problems when applied to large-scale data to reduce the number of parameters, improve generalization, and find essential predictors. In this study, using 7k proteomic data generated from the analysis of serum obtained from 82 patients with GBM pre- and post-completion of concurrent chemoirradiation (CRT), we aimed to select the most discriminative proteomic features that define proteomic alteration that is the result of administering CRT. Thus, we present a novel rank-based feature weighting method (RadWise) to identify relevant proteomic parameters using two popular feature selection methods, least absolute shrinkage and selection operator (LASSO) and the minimum redundancy maximum relevance (mRMR). The computational results show that the proposed method yields outstanding results with very few selected proteomic features, with higher accuracy rate performance than methods that do not employ a feature selection process. While the computational method identified several proteomic signals identical to the clinical intuitive (heuristic approach), several heuristically identified proteomic signals were not selected while other novel proteomic biomarkers not selected with the heuristic approach that carry biological prognostic relevance in GBM only emerged with the novel method. The computational results show that the proposed method yields promising results, reducing 7k proteomic data to 7 selected proteomic features with a performance value of 93.921%, comparing favorably with techniques that do not employ feature selection.

Background Circular RNAs (circRNAs) are emerging as powerful regulators of cardiac development and disease. Nevertheless, detailed studies describing circRNA-mediated regulation of cardiac fibroblasts (CFs) biology and their role in cardiac fibrosis remain limited. Methods PCR and Sanger sequencing were performed to identify the expression of circHIPK3 in CFs. Edu corporation assays, Transwell migration assays, and immunofluorescence staining assays were conducted to detect the function of circHIPK3 in CFs in vitro. Bioinformatics analysis, dual luciferase activity assays, RNA immunoprecipitation, and fluorescent in situ hybridization experiments were conducted to investigate the mechanism of circHIPK3-mediated cardiac fibrosis. Echocardiographic analysis, Sirius Red staining and immunofluorescence staining were performed to investigate the function of circHIPK3 in angiotensin II (Ang II) induced cardiac fibrosis in vivo. Results circHIPK3 expression markedly increased in CFs and heart tissues after the treatment of Ang II. circHIPK3 silencing attenuates CFs proliferation, migration and the upregulation of a-SMA expression levels induced by Ang II in vitro. circHIPK3 acted as a miR-29b-3p sponge and overexpression of circHIPK3 effectively reverses miR-29b-3p-induced inhibition of CFs proliferation and migration and alters the expression levels of miR-29b-3p targeting genes (a-SMA, COL1A1, COL3A1) in vitro. Combination of circHIPK3 silencing and miR-29b-3p overexpression had a stronger effect on cardiac fibrosis suppression in vivo than did circHIPK3 silencing or miR-29b-3p overexpression alone. Conclusions Our data suggest that circHIPK3 serves as a miR-29b-3p sponge to regulate CF proliferation, migration and development of cardiac fibrosis, revealing a potential new target for the prevention of Ang II-induced cardiac fibrosis.

Radiofrequency ablation (RFA) is widely used to treat patients with atrial fibrillation (AF), but its recurrence rate is still high mainly due to pulmonary vein reconnection and residual conduction gaps. We aim to establish a rabbit model to simulate the residual conduction gaps after ablation. Sixty-nine adult New Zealand white rabbits were randomly assigned to six groups. RFA on the anterior wall of left atrial appendage (LAA) were performed with the ablation power from 6 to 21 W. The electrophysiological pacing and mapping technology was used to evaluate the bidirectional conduction of LAA. Histological study and fluorescence techniques were used to evaluate the effect of RFA and the accumulation of drug-loaded liposome on the loop ablation lesions of LAA. Typical loop ablation lesions of LAA could be observed in vivo and vitro of rabbit models. Histological evaluation revealed coagulative necrosis on the loop ablation lesions. Electrical conduction between inside and outside loop lesions recovered after 1 or 2 weeks after initial unidirectional conduction block. The recurrence rates were significantly different among six groups with varying ablation powers (p

The photocatalytic activity of graphitic carbon nitride (CN) is mainly restricted by its high recombination rate of charge carriers and narrow visible light absorption. In the present work, nitrogen-deficient CN (NDCCN) nanosheets with high crystallinity were synthesized using molten salt (NaLiCO3) as an etching agent and high-temperature solvent. The electronic structure and energy band levels of the obtained NDCCN are optimized to extend its optical absorption and enhance separation efficiency of photo-generated charge carriers. With these changes, NDCCN displays high photocatalytic activity for hydrogen evolution under visible light illumination (111 μmol h−1), which is 4.6 times of that over pristine CN. This finding opens up a new window to simultaneously decrease nitrogen composition and increase crystallinity of carbon nitride for higher solar-light-driven hydrogen production efficiency.

PURPOSE For computed tomography (CT) systems in which noise is nonstationary, a local noise power spectrum (NPS) is often needed to characterize its noise property. We have previously developed a data-efficient radial NPS method to estimate the two-dimensional (2D) local NPS for filtered back projection (FBP)-reconstructed fan-beam CT utilizing the polar separability of CT NPS. In this work, we extend this method to estimate three-dimensional (3D) local NPS for feldkamp-davis-kress (FDK)-reconstructed cone-beam CT (CBCT) volumes. METHODS Starting from the 2D polar separability, we analyze the CBCT geometry and FDK image reconstruction process to derive the 3D expression of the polar separability for CBCT local NPS. With the polar separability, the 3D local NPS of CBCT can be decomposed into a 2D radial NPS shape function and a one-dimensional (1D) angular amplitude function with certain geometrical transforms. The 2D radial NPS shape function is a global function characterizing the noise correlation structure, while the 1D angular amplitude function is a local function reflecting the varying local noise amplitudes. The 3D radial local NPS method is constructed from the polar separability. We evaluate the accuracy of the 3D radial local NPS method using simulated and real CBCT data by comparing the radial local NPS estimates to a reference local NPS in terms of normalized mean squared error (NMSE) and a task-based performance metric (lesion detectability). RESULTS In both simulated and physical CBCT examples, a very small NMSE (

OBJECTIVES Our aim was to explore a novel quantitative method [based upon an MRI-based image segmentation that allows actual calculation of grey matter, white matter and cerebrospinal fluid (CSF) volumes] for overcoming the difficulties associated with conventional techniques for measuring actual metabolic activity of the grey matter. METHODS We included four patients with normal brain MRI and fluorine-18 fluorodeoxyglucose (F-FDG)-PET scans (two women and two men; mean age 46±14 years) in this analysis. The time interval between the two scans was 0-180 days. We calculated the volumes of grey matter, white matter and CSF by using a novel segmentation technique applied to the MRI images. We measured the mean standardized uptake value (SUV) representing the whole metabolic activity of the brain from the F-FDG-PET images. We also calculated the white matter SUV from the upper transaxial slices (centrum semiovale) of the F-FDG-PET images. The whole brain volume was calculated by summing up the volumes of the white matter, grey matter and CSF. The global cerebral metabolic activity was calculated by multiplying the mean SUV with total brain volume. The whole brain white matter metabolic activity was calculated by multiplying the mean SUV for the white matter by the white matter volume. The global cerebral metabolic activity only reflects those of the grey matter and the white matter, whereas that of the CSF is zero. We subtracted the global white matter metabolic activity from that of the whole brain, resulting in the global grey matter metabolism alone. We then divided the grey matter global metabolic activity by grey matter volume to accurately calculate the SUV for the grey matter alone. RESULTS The brain volumes ranged between 1546 and 1924 ml. The mean SUV for total brain was 4.8-7. Total metabolic burden of the brain ranged from 5565 to 9617. The mean SUV for white matter was 2.8-4.1. On the basis of these measurements we generated the grey matter SUV, which ranged from 8.1 to 11.3. CONCLUSION The accurate metabolic activity of the grey matter can be calculated using the novel segmentation technique that we applied to MRI. By combining these quantitative data with those generated from F-FDG-PET images we were able to calculate the accurate metabolic activity of the grey matter. These types of measurements will be of great value in accurate analysis of the data from patients with neuropsychiatric disorders.

Nonalcoholic fatty liver disease (NAFLD), characterized by hepatic steatosis (HS), insulin resistance (IR), and inflammation, poses a high risk of cardiometabolic disorders. Ubiquitin specific protease 4 (USP4), a deubiquitinating enzyme, is pivotally involved in regulating multiple inflammatory pathways; however, the role of USP4 in NAFLD is unknown. Here, we report that USP4 expression was dramatically down-regulated in livers from NAFLD patients and different NAFLD mouse models induced by high-fat diet (HFD) or genetic deficiency (ob/ob) as well as in palmitate-treated hepatocytes. Hepatocyte-specific USP4 depletion exacerbated HS, IR, and inflammatory response in HFD-induced NAFLD mice. Conversely, hepatic USP4 overexpression notably alleviated the pathological alterations in two different NAFLD models. Mechanistically, hepatocyte USP4 directly bound to and deubiquitinated transforming growth factor-β activated kinase 1 (TAK1), leading to a suppression of the activation of downstream nuclear factor kappa B (NF-κB) and c-Jun N-terminal kinase (JNK) cascades, which, in turn, reversed the disruption of insulin receptor substrate/protein kinase B/glycogen synthase kinase 3 beta (IRS-AKT-GSK3β) signaling. In addition, USP4-TAK1 interaction and subsequent TAK1 deubiquitination were required for amelioration of metabolic dysfunctions. Conclusion: Collectively, the present study provides evidence that USP4 functions as a pivotal suppressor in NAFLD and related metabolic disorders. (Hepatology 2018; 00:000-000).

Purpose Gliomas are rapidly progressive, neurologically devastating, largely fatal brain tumors. Magnetic Resonance Imaging (MRI) is a widely used technique employed in the diagnosis and management of gliomas in clinical practice. MRI is also the standard imaging modality used to delineate the brain tumor target as part of treatment planning for the administration of radiation therapy. Despite more than 20 years of research and development, computational brain tumor segmentation in MRI images remains a challenging task. We are presenting a novel method of automatic image segmentation based on holistically-nested neural networks that could be employed for brain tumor segmentation of MRI images. Methods Two preprocessing techniques were applied to MRI images. The N4ITK method was employed for correction of bias field distortion. A novel landmark-based intensity normalization method was developed so that tissue types have a similar intensity scale in images of different subjects for the same MRI protocol. The holistically-nested neural networks (HNN), which extend from the convolutional neural networks (CNN) with a deep supervision through an additional weighted-fusion output layer, was trained to learn the multi-scale and multi-level hierarchical appearance representation of the brain tumor in MRI images and was subsequently applied to produce a prediction map of the brain tumor on test images. Finally, the brain tumor was obtained through an optimum thresholding on the prediction map. Results The proposed method was evaluated on both the Multimodal Brain Tumor Image Segmentation (BRATS) Benchmark 2013 training data sets, and clinical data from our institute. A dice similarity coefficient (DSC) and sensitivity of 0.78 and 0.81 were achieved on 20 BRATS 2013 training data sets with High Grade Gliomas (HGG), based on a two-fold cross-validation. The HNN model built on the BRATS 2013 training data was applied to 10 clinical data sets with HGG from a locally developed database. DSC and sensitivity of 0.83 and 0.85 were achieved. A quantitative comparison indicated that the proposed method outperforms the popular fully convolutional network (FCN) method. In terms of efficiency, the proposed method took around 10 hours for training with 50,000 iterations, and approximately 30 seconds for testing of a typical MRI image in the BRATS 2013 data set with a size of 160×216×176, using a DELL PRECISION workstation T7400, with an NVIDIA Tesla K20c GPU. Conclusions An effective brain tumor segmentation method for MRI images based on a HNN has been developed. The high level of accuracy and efficiency make this method practical in brain tumor segmentation. It may play a crucial role in both brain tumor diagnostic analysis and in the treatment planning of radiation therapy. This article is protected by copyright. All rights reserved.

We sought to evaluate the disregarded spatial torsion of the ipsilateral superior and inferior pulmonary veins (PVs).Forty-eight consecutive atrial fibrillation patients, with four discrete PVs, were enrolled. The ostium plane of each PV, labeled by three landmarks on the CT image, was identified by three experienced observers respectively. Angle and distance between ostium planes were used to reach a consensus and to select the best ostium plane of each PV. A common ostium plane of the ipsilateral PV was computed using the two geometric centers of each side PVs and the axial center of the two geometric centers. The torsion angle was defined as the absolute difference of the two dihedral angles between the common ostium plane and the best ostium plane of the superior and inferior PVs.The torsion angle15° was found in 16 left PVs (16/48) and in nine right PVs (9/48, P = 0.104). Moreover, in two cases (2.1 %), the torsion angle exceeded 30° (one left PV and one right PV). The torsion angle of the left PVs was significantly greater than that of the right PVs (13.65 ± 5.90° vs. 10.61 ± 5.96°, P = 0.014). Furthermore, in 29 paroxysmal AF patients, the spatial torsion of the left side PVs was 13.70 ± 5.18°, which was significant greater than that of the right side PVs (10.69 ± 5.52°, P = 0.037).There was a significant torsion between the ipsilateral PVs, which should be taken into account when physicians plan their ablation to avoid a single-plane circumferential ablation.

Ying Zhuge,1,* Zhi-Feng Zheng,1,* Mu-Qing Xie,2 Lin Li,2 Fang Wang,1 Feng Gao2,3 1Department of Cardiology, Shanghai First People’s Hospital of Nanjing Medical University, 2Department of Pharmaceutics, School of Pharmacy, 3Shanghai Key Laboratory of Functional Materials Chemistry, East China University of Science and Technology, Shanghai, People’s Republic of China*These authors contributed equally tothis workAbstract: The objective of this study was to develop an amiodarone hydrochloride (ADHC)-loaded liposome (ADHC-L) formulation and investigate its potential for cardiomyocyte targeting after cardiac radiofrequency ablation (CA) in vivo. The ADHC-L was prepared by thin-film method combined with ultrasonication and extrusion. The preparation process was optimized by Box–Behnken design with encapsulation efficiency as the main evaluation index. The optimum formulation was quantitatively obtained with a diameter of 99.9±0.4 nm, a zeta potential of 35.1±10.9 mV, and an encapsulation efficiency of 99.5%±13.3%. Transmission electron microscopy showed that the liposomes were spherical particles with integrated bilayers and well dispersed with high colloidal stability. Pharmacokinetic studies were investigated in rats after intravenous administration, which revealed that compared with free ADHC treatment, ADHC-L treatment showed a 5.1-fold increase in the area under the plasma drug concentration–time curve over a period of 24 hours (AUC0–24h) and an 8.5-fold increase in mean residence time, suggesting that ADHC-L could facilitate drug release in a more stable and sustained manner while increasing the circulation time of ADHC, especially in the blood. Biodistribution studies of ADHC-L demonstrated that ADHC concentration in the heart was 4.1 times higher after ADHC-L treatment in CA rat model compared with ADHC-L sham-operated treatment at 20minutes postinjection. Fluorescence imaging studies further proved that the heart-targeting ability of ADHC-L was mainly due to the CA in rats. These results strongly support that ADHC-L could be exploited as a potential heart-targeting drug delivery system with enhanced bioavailability and reduced side effects for arrhythmia treatment after CA.Keywords: liposomes, cardiomyocyte targeting, amiodarone hydrochloride, cardiac radiofrequency ablation