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Conventional clustering techniques for neuroimaging applications usually focus on capturing differences between given subjects, while neglecting arising differences between features and the potential bias caused by degraded data quality. In practice, collected neuroimaging data are often inevitably contaminated by noise, which may lead to errors in clustering and clinical interpretation. Additionally, most methods ignore the importance of feature grouping towards optimal clustering. In this paper, we exploit the underlying heterogeneous clusters of features to serve as weak supervision for improved clustering of subjects, which is achieved by simultaneously clustering subjects and features via nonnegative matrix tri-factorization. In order to suppress noise, we further introduce adaptive regularization based on coefficient distribution modeling. Particularly, unlike conventional sparsity regularization techniques that assume zero mean of the coefficients, we form the distributions using the data of interest so that they could better fit the non-negative coefficients. In this manner, the proposed approach is expected to be more effective and robust against noise. We compared the proposed method with standard techniques and recently published methods demonstrating superior clustering performance on synthetic data with known ground truth labels. Furthermore, when applying our proposed technique to magnetic resonance imaging (MRI) data from a cohort of patients with Parkinson’s disease, we identified two stable and highly reproducible patient clusters characterized by frontal and posterior cortical/medial temporal atrophy patterns, respectively, which also showed corresponding differences in cognitive characteristics.

Local and non-local attention-based methods have been well studied in various image restoration tasks while leading to promising performance. However, most of the existing methods solely focus on one type of attention mechanism (local or non-local). Furthermore, by exploiting the self-similarity of natural images, existing pixel-wise non-local attention operations tend to give rise to deviations in the process of characterizing long-range dependence due to image degeneration. To overcome these problems, in this paper we propose a novel collaborative attention network (COLA-Net) for image restoration, as the first attempt to combine local and non-local attention mechanisms to restore image content in the areas with complex textures and with highly repetitive details respectively. In addition, an effective and robust patch-wise non-local attention model is developed to capture long-range feature correspondences through 3D patches. Extensive experiments on synthetic image denoising, real image denoising and compression artifact reduction tasks demonstrate that our proposed COLA-Net is able to achieve state-of-the-art performance in both peak signal-to-noise ratio and visual perception, while maintaining an attractive computational complexity. The source code is available on https://github.com/MC-E/COLA-Net., 11 pages, 6 tables, 9 figures, to be published in IEEE Transactions on Multimedia

Feature dimensionality reduction plays an important role in radiomic studies with a large number of features. However, conventional radiomic approaches may suffer from noise, and feature dimensionality reduction techniques are not equipped to utilize latent supervision information of patient data under study, such as differences in patients, to learn discriminative low dimensional representations. To achieve robustness to noise and feature dimensionality reduction with improved discriminative power, we develop a robust collaborative clustering method to simultaneously cluster patients and radiomic features into distinct groups respectively under adaptive sparse regularization. Our method is built upon matrix tri-factorization enhanced by adaptive sparsity regularization for simultaneous feature dimensionality reduction and denoising. Particularly, latent grouping information of patients with distinct radiomic features is learned and utilized as supervision information to guide the feature dimensionality reduction, and noise in radiomic features is adaptively isolated in a Bayesian framework under a general assumption of Laplacian distributions of transform-domain coefficients. Experiments on synthetic data have demonstrated the effectiveness of the proposed approach in data clustering, and evaluation results on an fDg-PET/CT dataset of rectal cancer patients have demonstrated that the proposed method outperforms alternative methods in terms of both patient stratification and prediction of patient clinical outcomes.

ImportanceEnlarged perivascular spaces (ePVSs) have been associated with cerebral small-vessel disease (cSVD). Although their etiology may differ based on brain location, study of ePVSs has been limited to specific brain regions; therefore, their risk factors and significance remain uncertain.ObjectiveToperform a whole-brain investigation of ePVSs in a large community-based cohort.Design, Setting, and ParticipantsThis cross-sectional study analyzed data from the Atrial Fibrillation substudy of the population-based Multi-Ethnic Study of Atherosclerosis. Demographic, vascular risk, and cardiovascular disease data were collected from September 2016 to May 2018. Brain magnetic resonance imaging was performed from March 2018 to July 2019. The reported analysis was conducted between August and October 2022. A total of 1026 participants with available brain magnetic resonance imaging data and complete information on demographic characteristics and vascular risk factors were included.Main Outcomes and MeasuresEnlarged perivascular spaces were quantified using a fully automated deep learning algorithm. Quantified ePVS volumes were grouped into 6 anatomic locations: basal ganglia, thalamus, brainstem, frontoparietal, insular, and temporal regions, and were normalized for the respective regional volumes. The association of normalized regional ePVS volumes with demographic characteristics, vascular risk factors, neuroimaging indices, and prevalent cardiovascular disease was explored using generalized linear models.ResultsIn the 1026 participants, mean (SD) age was 72 (8) years; 541 (53%) of the participants were women. Basal ganglia ePVS volume was positively associated with age (β = 3.59 × 10−3; 95% CI, 2.80 × 10−3 to 4.39 × 10−3), systolic blood pressure (β = 8.35 × 10−4; 95% CI, 5.19 × 10−4 to 1.15 × 10−3), use of antihypertensives (β = 3.29 × 10−2; 95% CI, 1.92 × 10−2 to 4.67 × 10−2), and negatively associated with Black race (β = −3.34 × 10−2; 95% CI, −5.08 × 10−2 to −1.59 × 10−2). Thalamic ePVS volume was positively associated with age (β = 5.57 × 10−4; 95% CI, 2.19 × 10−4 to 8.95 × 10−4) and use of antihypertensives (β = 1.19 × 10−2; 95% CI, 6.02 × 10−3 to 1.77 × 10−2). Insular region ePVS volume was positively associated with age (β = 1.18 × 10−3; 95% CI, 7.98 × 10−4 to 1.55 × 10−3). Brainstem ePVS volume was smaller in Black than in White participants (β = −5.34 × 10−3; 95% CI, −8.26 × 10−3 to −2.41 × 10−3). Frontoparietal ePVS volume was positively associated with systolic blood pressure (β = 1.14 × 10−4; 95% CI, 3.38 × 10−5 to 1.95 × 10−4) and negatively associated with age (β = −3.38 × 10−4; 95% CI, −5.40 × 10−4 to −1.36 × 10−4). Temporal region ePVS volume was negatively associated with age (β = −1.61 × 10−2; 95% CI, −2.14 × 10−2 to −1.09 × 10−2), as well as Chinese American (β = −2.35 × 10−1; 95% CI, −3.83 × 10−1 to −8.74 × 10−2) and Hispanic ethnicities (β = −1.73 × 10−1; 95% CI, −2.96 × 10−1 to −4.99 × 10−2).Conclusions and RelevanceIn this cross-sectional study of ePVSs in the whole brain, increased ePVS burden in the basal ganglia and thalamus was a surrogate marker for underlying cSVD, highlighting the clinical importance of ePVSs in these locations.

ImportanceLittle is known about the associations of strict blood pressure (BP) control with microstructural changes in small vessel disease markers.ObjectiveTo investigate the regional associations of intensive vs standard BP control with small vessel disease biomarkers, such as white matter lesions (WMLs), fractional anisotropy (FA), mean diffusivity (MD), and cerebral blood flow (CBF).Design, Setting, and ParticipantsThe Systolic Blood Pressure Intervention Trial (SPRINT) is a multicenter randomized clinical trial that compared intensive systolic BP (SBP) control (SBP target InterventionsAt baseline, 355 participants received intensive SBP treatment and 315 participants received standard SBP treatment.Main Outcomes and MeasuresThe main outcomes were regional changes in WMLs, FA, MD (in white matter regions of interest), and CBF (in gray matter regions of interest).ResultsAt baseline, 355 participants (mean [SD] age, 67.7 [8.0] years; 200 men [56.3%]) received intensive BP treatment and 315 participants (mean [SD] age, 67.0 [8.4] years; 199 men [63.2%]) received standard BP treatment. Intensive treatment was associated with smaller mean increases in WML volume compared with standard treatment (644.5 mm3 vs 1258.1 mm3). The smaller mean increases were observed specifically in the deep white matter regions of the left anterior corona radiata (intensive treatment, 30.3 mm3 [95% CI, 16.0-44.5 mm3]; standard treatment, 80.5 mm3 [95% CI, 53.8-107.2 mm3]), left tapetum (intensive treatment, 11.8 mm3 [95% CI, 4.4-19.2 mm3]; standard treatment, 27.2 mm3 [95% CI, 19.4-35.0 mm3]), left superior fronto-occipital fasciculus (intensive treatment, 3.2 mm3 [95% CI, 0.7-5.8 mm3]; standard treatment, 9.4 mm3 [95% CI, 5.5-13.4 mm3]), left posterior corona radiata (intensive treatment, 26.0 mm3 [95% CI, 12.9-39.1 mm3]; standard treatment, 52.3 mm3 [95% CI, 34.8-69.8 mm3]), left splenium of the corpus callosum (intensive treatment, 45.4 mm3 [95% CI, 25.1-65.7 mm3]; standard treatment, 83.0 mm3 [95% CI, 58.7-107.2 mm3]), left posterior thalamic radiation (intensive treatment, 53.0 mm3 [95% CI, 29.8-76.2 mm3]; standard treatment, 106.9 mm3 [95% CI, 73.4-140.3 mm3]), and right posterior thalamic radiation (intensive treatment, 49.5 mm3 [95% CI, 24.3-74.7 mm3]; standard treatment, 102.6 mm3 [95% CI, 71.0-134.2 mm3]).Conclusions and RelevanceThis study suggests that intensive BP treatment, compared with standard treatment, was associated with a slower increase of WMLs, improved diffusion tensor imaging, and FA and CBF changes in several brain regions that represent vulnerable areas that may benefit from more strict BP control.Trial RegistrationClinicalTrials.gov Identifier: NCT01206062

G-Cast is a wireless visual communication scheme that conveys visual information via image gradient. It is inspired by the characteristics of human vision systems and can provide improved perceptual quality. G-Cast is power efficient but bandwidth demanding, because gradient data have double the size of the original image. This paper presents a scheme named CG-Cast for scalable image transmission in bandwidth-limited wireless scenarios. It employs a compressive-gradient-based image representation to describe perceptually sensitive image details and reduce the bandwidth requirement at the same time, combining gradient-based visual representation with compressive sensing techniques. The compressive gradient data are transmitted in a pseudo-analog way so that it achieves elegant quality transition in a wide channel signal-to-noise ratio (CSNR) range. CG-Cast also sends a small set of low-frequency data in digital a way to provide the global and local luminance of the image. We developed an effective optimization algorithm for the decoder to reconstruct the original image from the received noisy compressive gradient and the low-frequency part of the image. Experimental results demonstrate that the proposed scheme improves the quality of received images remarkably under different CSNR and channel bandwidth conditions.

This paper proposes a robust multi-frame video super-resolution (SR) scheme to obtain high SR performance under large upscaling factors. Although the reference low-resolution frames can provide complementary information for the high-resolution frame, an effective regularizer is required to rectify the unreliable information from the reference frames. As the high-frequency information is mostly contained in the image gradient field, we propose to learn the gradient-mapping function between the high-resolution (HR) and the low-resolution (LR) image to regularize the fusion of multiple frames. In contrast to the existing spatial-domain networks, we train a deep gradient-mapping network to learn the horizontal and vertical gradients. We found that adding the low-frequency information (mainly from the LR image) to the gradient-learning network can boost the performance of the network. A forward and backward motion field prior is used to regularize the estimation of the motion flow between frames. For robust SR reconstruction, a weighting scheme is proposed to exclude the outlier data. Visual and quantitative evaluations on benchmark datasets demonstrate that our method is superior to many state-of-the-art methods and can recover better details with less artifacts.

Lobar cerebral microbleeds (CMBs) and localized non-hemorrhage iron deposits in the basal ganglia have been associated with brain aging, vascular disease and neurodegenerative disorders. Particularly, CMBs are small lesions and require multiple neuroimaging modalities for accurate detection. Quantitative susceptibility mapping (QSM) derived from in vivo magnetic resonance imaging (MRI) is necessary to differentiate between iron content and mineralization. We set out to develop a deep learning-based segmentation method suitable for segmenting both CMBs and iron deposits. We included a convenience sample of 24 participants from the MESA cohort and used T2-weighted images, susceptibility weighted imaging (SWI), and QSM to segment the two types of lesions. We developed a protocol for simultaneous manual annotation of CMBs and non-hemorrhage iron deposits in the basal ganglia. This manual annotation was then used to train a deep convolution neural network (CNN). Specifically, we adapted the U-Net model with a higher number of resolution layers to be able to detect small lesions such as CMBs from standard resolution MRI. We tested different combinations of the three modalities to determine the most informative data sources for the detection tasks. In the detection of CMBs using single class and multiclass models, we achieved an average sensitivity and precision of between 0.84–0.88 and 0.40–0.59, respectively. The same framework detected non-hemorrhage iron deposits with an average sensitivity and precision of about 0.75–0.81 and 0.62–0.75, respectively. Our results showed that deep learning could automate the detection of small vessel disease lesions and including multimodal MR data (particularly QSM) can improve the detection of CMB and non-hemorrhage iron deposits with sensitivity and precision that is compatible with use in large-scale research studies.

In image restoration tasks, image priors generally utilize correlation within image contents to predict the latent image signal. In this paper, we propose to jointly exploit both intra- and inter-patch correlation of the input image, so as to further reduce the uncertainty of the unknown signal, and thus improve the prediction of the latent image. The proposed scheme evolves from the low-rank regularization for non-local highly-correlated image contents. Since the underlying cost function to pursue minimal rank is hard to solve, we use non-convex smooth surrogates for the rank penalty. Two such surrogates are utilized in order to incorporate both intra- and inter-patch correlation. To tackle the optimization problem, we use iterative alternating direction technique to divide the problem into two subproblems, each of which is solved via an empirical Bayesian procedure built upon variational approximation. Experimental results on image denoising show that the proposed approach outperforms several state-of-the-art methods in terms of peak signal-to-noise ratio, structural similarity, and perceptual quality.

Total variation (TV) regularization is widely used in image restoration to exploit the local smoothness of image content. Essentially, the TV model assumes a zero-mean Laplacian distribution for the gradient at all pixels. However, real-world images are nonstationary in general, and the zero-mean assumption of pixel gradient might be invalid, especially for regions with strong edges or rich textures. This paper introduces a nonlocal (NL) extension of TV regularization, which models the sparsity of the image gradient with pixelwise content-adaptive distributions, reflecting the nonstationary nature of image statistics. Taking advantage of the NL similarity of natural images, the proposed approach estimates the image gradient statistics at a particular pixel from a group of nonlocally searched patches, which are similar to the patch located at the current pixel. The gradient data in these NL similar patches are regarded as the samples of the gradient distribution to be learned. In this way, more accurate estimation of gradient is achieved. Experimental results demonstrate that the proposed method outperforms the conventional TV and several other anchors remarkably and produces better objective and subjective image qualities.

Angiogenesis is essential for the development, growth, and metastasis of solid tumors. Vaccination with viable human umbilical vein endothelial cells (HUVECs) has been used for antitumor angiogenesis. However, the limited immune response induced by HUVECs hinders their clinical application. In the present study, we found that HUVECs induced by a tumor microenvironment using the supernatant of murine CT26 colorectal cancer cells exerted a better antiangiogenic effect than HUVECs themselves. The inhibitory effect on tumor growth in the induced HUVEC group was significantly better than that of the HUVEC group, and the induced HUVEC group showed a strong inhibition in CD31-positive microvessel density in the tumor tissues. Moreover, the level of anti-induced HUVEC membrane protein antibody in mouse serum was profoundly higher in the induced HUVEC group than in the HUVEC group. Based on this, the antitumor effect of a vaccine with a combination of induced HUVECs and dendritic cell-loading CT26 antigen (DC-CT26) was evaluated. Notably, the microvessel density of tumor specimens was significantly lower in the combined vaccine group than in the control groups. Furthermore, the spleen index, the killing effect of cytotoxic T lymphocytes (CTLs), and the concentration of interferon-&gamma, in the serum were enhanced in the combined vaccine group. Based on these results, the combined vaccine targeting both tumor angiogenesis and tumor cells may be an attractive and effective cancer immunotherapy strategy.

Most machine learning approaches in radiomics studies ignore the underlying difference of radiomic features computed from heterogeneous groups of patients, and intrinsic correlations of the features are not fully exploited yet. In order to better predict clinical outcomes of cancer patients, we adopt an unsupervised machine learning method to simultaneously stratify cancer patients into distinct risk groups based on their radiomic features and learn low-dimensional representations of the radiomic features for robust prediction of their clinical outcomes. Based on nonnegative matrix tri-factorization techniques, the proposed method applies collaborative clustering to radiomic features of cancer patients to obtain clusters of both the patients and their radiomic features so that patients with distinct imaging patterns are stratified into different risk groups and highly correlated radiomic features are grouped in the same radiomic feature clusters. Experiments on a FDG-PET/CT dataset of rectal cancer patients have demonstrated that the proposed method facilitates better stratification of patients with distinct survival patterns and learning of more effective low-dimensional feature representations that ultimately leads to accurate prediction of patient survival, outperforming conventional methods under comparison.

This paper proposes a new image denoising algorithm based on adaptive signal modeling and regularization. It improves the quality of images by regularizing each image patch using bandwise distribution modeling in transform domain. Instead of using a global model for all the patches in an image, it employs content-dependent adaptive models to address the non-stationarity of image signals and also the diversity among different transform bands. The distribution model is adaptively estimated for each patch individually. It varies from one patch location to another and also varies for different bands. In particular, we consider the estimated distribution to have non-zero expectation. To estimate the expectation and variance parameters for every band of a particular patch, we exploit the nonlocal correlation in image to collect a set of highly similar patches as the data samples to form the distribution. Irrelevant patches are excluded so that such adaptively learned model is more accurate than a global one. The image is ultimately restored via bandwise adaptive soft-thresholding, based on a Laplacian approximation of the distribution of similar-patch group transform coefficients. Experimental results demonstrate that the proposed scheme outperforms several state-of-the-art denoising methods in both the objective and the perceptual qualities.

A large volume of data indicates that controlling tumor-associated angiogenesis is a promising therapy against cancer. However, angiogenesis is a complex process, little is known about the differential gene expression in the process of normal endothelial cell differentiation toward tumor vascular endothelial cells induced by tumor microenvironment. The aim of this study is to investigate the effect of tumor microenvironment simulated by the supernatant of esophageal squamous cancer cells (KYSE70) on normal endothelial cells (HUVECs) at the whole genome level. The gene expression profile was studied through gene ontology and signal pathway analysis. Compared with the normal HUVECs, a total of 3769 differentially expressed genes in induced HUVECs were detected, including 1609 upregulated genes and 2160 downregulated genes. Moreover, the microarray data analysis showed that 11 significant biological processes and 10 significant signaling pathways changed most, which are associated with angiogenesis and cell differentiation. According to the different expression levels in the microarrays and their functions, four differentially expressed genes involved in tumor angiogenesis and cell differentiation (IL6, VEGFA, S1PR1, TYMP) were selected and analyzed by qRT-PCR. The qRT-PCR results were consistent with the microarray data. Furthermore, we simulated the tumor microenvironment by human esophageal carcinoma tissue homogenate to investigate its effect on HUVECs, the qRT-PCR results indicated that the above genes were highly expressed in HUVECs after induction by esophageal carcinoma tissue homogenate. In conclusion, tumor microenvironment impact on normal endothelial cells differentiated toward tumor vascular endothelial cells, and the selected genes, which are associated with tumor angiogenesis, would be anti-angiogenesis targets against esophageal carcinoma.

Radiomic approaches have achieved promising performance in prediction of clinical outcomes of cancer patients. Particularly, feature dimensionality reduction plays an important role in radiomic studies. However, conventional feature dimensionality reduction techniques are not equipped to suppress data noise or utilize latent supervision information of patient data under study (e.g. difference in patients) for learning discriminative low dimensional representations. To achieve feature dimensionality reduction with improved discriminative power and robustness to noisy radiomic features, we develop an adaptive sparsity regularization based collaborative clustering method to simultaneously cluster patients and radiomic features into distinct groups respectively. Our method is built on adaptive sparsity regularized matrix tri-factorization for simultaneous feature denoising and dimension reduction so that the noise is adaptively isolated from the features, and grouping information of patients with distinctive features provides latent supervision information to guide feature dimension reduction. The sparsity regularization is grounded on distribution modeling of transform-domain coefficients in a Bayesian framework. Experiments on synthetic data have demonstrated the effectiveness of the proposed approach in data clustering, and empirical results on an FDG-PET/CT dataset of rectal cancer patients have demonstrated that the proposed method outperforms alternative methods in terms of both patient stratification and prediction of patient clinical outcomes.

The antitumor effect of the human umbilical vein endothelial cell (HUVEC) vaccine has been well documented; however, its anti‑angiogenic effects on human esophageal squamous cell carcinoma (ESCC) have yet to be studied. In the present study, a 'humanized' mouse model was established by transplanting NOD/SCID mice with human peripheral blood mononuclear cells (PBMC). After 4 weeks, the level of cluster of differentiation (CD)‑45+ human T‑lymphocytes in mouse peripheral blood was >0.1%, which indicated that mouse reconstruction and the human immune system transformation had been successful. The humanized mice were used to evaluate the anti‑angiogenic effect of the HUVEC vaccine on human ESCC. After immunization with the HUVEC vaccine for 5 consecutive weeks, the humanized mice were subcutaneously transplanted with EC9706 cells. The results indicated that the HUVEC vaccine reduced the size of human esophageal carcinoma xenografts by suppressing angiogenesis. In addition, the HUVEC‑immunized mice exhibited reduced expression of angiogenesis‑associated antigens (vascular endothelial growth factor receptor 2 and VE‑Cadherin) in the tumor specimens, and increased levels of angiogenesis‑associated antibodies in the serum. Notably, the HUVEC vaccine also increased the infiltration of human T‑lymphocytes into the spleen of humanized mice. In conclusion, the present study demonstrated the anti‑angiogenic effect of the HUVEC vaccine on ESCC in a humanized mouse model, and set an experimental foundation for the application of the HUVEC vaccine in ESCC patients.

// Yi Yang 1, 2, 3 , Guoguo Jin 1, 2 , Hangfan Liu 1, 2 , Kangdong Liu 1, 2 , Jimin Zhao 1, 2 , Xinhuan Chen 1, 2 , Dongyu Wang 1, 2 , Ruihua Bai 4 , Xiang Li 1, 2 , Yanan Jang 1, 2 , Jing Lu 1, 2 , Ying Xing 1, 3 and Ziming Dong 1, 2 1 Department of Pathophysiology, School of Basic Medical Sciences, Zhengzhou University, Zhengzhou, Henan 450001, P.R. China 2 Collaborative Innovation Center of Henan Province for Cancer Chemoprevention, Zhengzhou, Henan 450001, P.R. China 3 Department of Physiology, School of Basic Medical Sciences, Zhengzhou University, Zhengzhou, Henan 450001, P.R. China 4 Department of Pathology, Henan Cancer Hospital, Zhengzhou University, Zhengzhou, Henan 450008, P.R. China Correspondence to: Jing Lu, email: lujing@zzu.edu.cn Ying Xing, email: xingy@zzu.edu.cn Ziming Dong, email: dongzm@zzu.edu.cn Keywords: metformin, angiogenesis, ESCC, microenvironment, TECs Received: January 23, 2017 Accepted: July 18, 2017 Published: August 18, 2017 ABSTRACT Although it has been known that the tumor microenvironment affects angiogenesis, the precise mechanism remains unclear. In this study, we simulated the microenvironment of human esophageal squamous cell carcinoma (ESCC) by tumor conditioned medium (TCM) to assess the influence on normal endothelial cells (NECs). We found that the TCM-induced NECs showed enhanced angiogenic properties, such as migration, invasion and tube formation. Moreover, the TCM-induced NECs expressed tumor endothelial cells (TECs) markers at higher levels, which indicated that TCM probably promoted tumor angiogenesis by coercing NECs to change toward TECs. The microarray gene expression analysis indicated that TCM induced great changes in the genome of NECs and altered many regulatory networks, especially c-MYC and JAK/STAT3 signaling pathway. More importantly, we investigated the anti-angiogenic effect of metformin, and found that metformin abrogated the ESCC microenvironment-induced transition of NECs toward TECs by inhibiting JAK/STAT3/c-MYC signaling pathway. Furthermore, we verified the anti-angiogenic activity of metformin in vivo by a human ESCC patient-derived xenograft (PDX) mouse model for the first time. Taken together, our research provides a novel mechanism for the anti-angiogenic effect of metformin, and sets an experimental basis for the development of new anti-angiogenic drugs by blocking the transition of NECs toward TECs, which possibly open new avenues for targeted treatment of cancer.

Dendritic cells (DC) have been exploited for vaccination against cancer for years. DC loading autologous tumor lysate (ATL-DC) have been assessed in ongoing clinical trials, but frequently do not meet expectation. In this study, we found that mice immunized with ATL-DC induced less protective anti-tumor effect than immunized with DC alone. The percentage of CD8+ T cells and the lysis efficiency of CTLs to auto tumor cells in ATL-DC vaccination group was less than that of DC group. Moreover, vaccination of mice with ATL-DC also promoted tumor angiogenesis by analyzing the CD31 positive microvessel density and hemoglobin content of tumor specimens. Human umbilical vein endothelial cells (HUVEC) have been proved effective in the anti-angiogenesis immunity against cancer. However, in the following research we found that the anti-tumor effect was attenuated while immunized mice with HUVEC combined with ATL-DC (HUVEC + ATL-DC). Furthermore, immunized mice with HUVEC + ATL-DC profoundly increased the tumor angiogenesis by analyzing the microvessel density and hemoglobin content of tumor specimens. These data suggest that vaccination using ATL-DC antagonized HUVEC induced anti-angiogenesis effect. Our research for the first time indicated that ATL-DC have the potential to promote the process of tumor angiogenesis in vivo. As vaccines based on DC loading autologous tumor lysate have been used in clinical, this find warned that the safety of this kind of vaccine should be taken into consideration seriously.

Ultrasound imaging (US) is commonly used in nephrology for diagnostic studies of the kidneys and lower urinary tract. However, it remains challenging to automate the disease diagnosis based on clinical 2D US images since they provide partial anatomic information of the kidney and the 2D images of the same kidney may have heterogeneous appearance. To overcome this challenge, we develop a novel multi-instance deep learning method to build a robust classifier by treating multiple 2D US images of each individual subject as multiple instances of one bag. Particularly, we adopt convolutional neural networks (CNNs) to learn instance-level features from 2D US kidney images and graph convolutional networks (GCNs) to further optimize the instance-level features by exploring potential correlation among instances of the same bag. We also adopt a gated attention-based MIL pooling to learn bag-level features using full-connected neural networks (FCNs). Finally, we integrate both instance-level and bag-level supervision to further improve the bag-level classification accuracy. Ablation studies and comparison results have demonstrated that our method could accurately diagnose kidney diseases using ultrasound imaging, with better performance than alternative state-of-the-art multi-instance deep learning methods.