Background
Deep learning is the most promising methodology for automatic computer‐aided diagnosis of prostate cancer (PCa) with multiparametric MRI (mp‐MRI).
Purpose
To develop an automatic approach ...based on deep convolutional neural network (DCNN) to classify PCa and noncancerous tissues (NC) with mp‐MRI.
Study Type
Retrospective.
Subjects
In all, 195 patients with localized PCa were collected from a PROSTATEx database. In total, 159/17/19 patients with 444/48/55 observations (215/23/23 PCas and 229/25/32 NCs) were randomly selected for training/validation/testing, respectively.
Sequence
T2‐weighted, diffusion‐weighted, and apparent diffusion coefficient images.
Assessment
A radiologist manually labeled the regions of interest of PCas and NCs and estimated the Prostate Imaging Reporting and Data System (PI‐RADS) scores for each region. Inspired by VGG‐Net, we designed a patch‐based DCNN model to distinguish between PCa and NCs based on a combination of mp‐MRI data. Additionally, an enhanced prediction method was used to improve the prediction accuracy. The performance of DCNN prediction was tested using a receiver operating characteristic (ROC) curve, and the area under the ROC curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated. Moreover, the predicted result was compared with the PI‐RADS score to evaluate its clinical value using decision curve analysis.
Statistical Test
Two‐sided Wilcoxon signed‐rank test with statistical significance set at 0.05.
Results
The DCNN produced excellent diagnostic performance in distinguishing between PCa and NC for testing datasets with an AUC of 0.944 (95% confidence interval: 0.876–0.994), sensitivity of 87.0%, specificity of 90.6%, PPV of 87.0%, and NPV of 90.6%. The decision curve analysis revealed that the joint model of PI‐RADS and DCNN provided additional net benefits compared with the DCNN model and the PI‐RADS scheme.
Data Conclusion
The proposed DCNN‐based model with enhanced prediction yielded high performance in statistical analysis, suggesting that DCNN could be used in computer‐aided diagnosis (CAD) for PCa classification.
Level of Evidence: 3
Technical Efficacy: Stage 2
J. Magn. Reson. Imaging 2018;48:1570–1577
In high-speed rail operations, contact wire irregularity (CWI) in a catenary is a common disturbance to the pantograph-catenary interaction performance, which directly affects the quality of current ...collection. To describe the pointwise stochastics of CWI, the power spectral density (PSD) function for CWI is proposed, and its effect on the pantograph-catenary interaction is investigated. First, a preprocessing procedure is proposed to eliminate the redundant information in the measured irregularities based on the ensemble empirical mode decomposition (EEMD). Then, the upper envelope of the irregularity, which contains all the information regarding the dropper positions on the contact wire, is extracted. A form of the PSD function is suggested for contact wire irregularities. A methodology is proposed to include the effect of random irregularities in the assessment of the interaction performance of a pantograph-catenary. A developed target configuration under dead load (TCUD) method is used to calculate the initial configuration of the catenary, in which the dropper points on the contact wire are placed on their exact positions. Finally, the effect of the random contact wire irregularities on the contact force is investigated through 500 numerical simulations at each operating speed. The present results indicate that random irregularities have a direct impact on the pantograph-catenary contact, including an increment in the dispersion of the contact force statistics. The stochastic analysis shows that over 70% of the results with irregularities are worse than the ideal result without irregularities.
High‐security nanoplatform with enhanced therapy compliance is extremely promising for tumor. Herein, using a simple and high‐efficient self‐assembly method, a novel active‐targeting nanocluster ...probe, namely, Ag2S/chlorin e6 (Ce6)/DOX@DSPE‐mPEG2000‐folate (ACD‐FA) is synthesized. Experiments indicate that ACD‐FA is capable of specifically labeling tumor and guiding targeting ablation of the tumor via precise positioning from fluorescence and photoacoustic imaging. Importantly, the probe is endowed with a photodynamic “on‐off” effect, that is, Ag2S could effectively quench the fluorescence of chlorin e6 (89.5%) and inhibit release of 1O2 (92.7%), which is conducive to avoid unwanted phototoxicity during transhipment in the body, and only after nanocluster endocytosed by tumor cells could release Ce6 to produce 1O2. Moreover, ACD‐FA also achieves excellent acid‐responsive drug release, and exhibits eminent chemo‐photothermal and photodynamic effects upon laser irradiation. Compared with single or two treatment combining modalities, ACD‐FA could provide the best cancer therapeutic effect with a relatively low dose, because it made the most of combined effect from chemo‐photothermal and controlled photodynamic therapy, and significantly improves the drug compliance. Besides, the active‐targeting nanocluster notably reduces nonspecific toxicity of both doxorubicin and chlorin e6. Together, this study demonstrates the potency of a newly designed nanocluster for nonradioactive concomitant therapy with precise tumor‐targeting capability.
A multifunctional nanoprobe Ag2S/Ce6/DOX@DSPE‐mPEG2000‐folate can specifically label tumor via precise positioning from fluorescence and photoacoustic imaging, which guide targeting ablation of tumor combined effect of chemo‐photothermal and controlled photodynamic therapy. In the process, the probe minimizes the administration dose as much as possible while achieving effective therapeutic effect, reducing nonspecific toxicity, and improving drug compliance.
Chronic intestinal inflammation is a key risk factor of colorectal cancer (CRC). It is known that microbial dysbiosis induces increased inflammatory factors which promote tumorigenesis and cellulose ...can be beneficial to CRC. In the present study, we investigated the regulatory effects of cellulose on intestinal flora composition and colorectal carcinogenesis in an azoxymethane (AOM)/dextran sulfate sodium (DSS)-induced CRC mouse model. Supplementation of cellulose significantly attenuated inflammation and tumor formation in AOM/DSS-treated CRC mice. The survival rate and the tumor inhibition rate were higher in the medium-dose cellulose group (MCEG) and high-dose cellulose group (HCEG) than in the model group (MG; P < 0.05). Cellulose supplementation stimulated shifts in the intestinal flora in AOM/DSS-treated CRC mice. Additionally, levels of inflammatory mediators involved in colorectal carcinogenesis, such as IL-6, IL-1β, and TNF-α, were lower in the serum of the low-dose cellulose group, MCEG, and HCEG when compared with the MG (P < 0.05). Whereas the abundance of differential bacteria was correlated with the concentration of IL-6, IL-1β, and TNF-α. These results showed cellulose changed the composition of intestinal flora and inhibited colon inflammation and neoplasm formation caused by the AOM/DSS treatment.
In radiomics studies, researchers usually need to develop a supervised machine learning model to map image features onto the clinical conclusion. A classical machine learning pipeline consists of ...several steps, including normalization, feature selection, and classification. It is often tedious to find an optimal pipeline with appropriate combinations. We designed an open-source software package named FeAture Explorer (FAE). It was programmed with Python and used NumPy, pandas, and scikit-learning modules. FAE can be used to extract image features, preprocess the feature matrix, develop different models automatically, and evaluate them with common clinical statistics. FAE features a user-friendly graphical user interface that can be used by radiologists and researchers to build many different pipelines, and to compare their results visually. To prove the effectiveness of FAE, we developed a candidate model to classify the clinical-significant prostate cancer (CS PCa) and non-CS PCa using the PROSTATEx dataset. We used FAE to try out different combinations of feature selectors and classifiers, compare the area under the receiver operating characteristic curve of different models on the validation dataset, and evaluate the model using independent test data. The final model with the analysis of variance as the feature selector and linear discriminate analysis as the classifier was selected and evaluated conveniently by FAE. The area under the receiver operating characteristic curve on the training, validation, and test dataset achieved results of 0.838, 0.814, and 0.824, respectively. FAE allows researchers to build radiomics models and evaluate them using an independent testing dataset. It also provides easy model comparison and result visualization. We believe FAE can be a convenient tool for radiomics studies and other medical studies involving supervised machine learning.
The magnetoelastic effect-the variation of the magnetic properties of a material under mechanical stress-is usually observed in rigid alloys, whose mechanical modulus is significantly different from ...that of human tissues, thus limiting their use in bioelectronics applications. Here, we observed a giant magnetoelastic effect in a soft system based on micromagnets dispersed in a silicone matrix, reaching a magnetomechanical coupling factor indicating up to four times more enhancement than in rigid counterparts. The results are interpreted using a wavy chain model, showing how mechanical stress changes the micromagnets' spacing and dipole alignment, thus altering the magnetic field generated by the composite. Combined with liquid-metal coils patterned on polydimethylsiloxane working as a magnetic induction layer, the soft magnetoelastic composite is used for stretchable and water-resistant magnetoelastic generators adhering conformably to human skin. Such devices can be used as wearable or implantable power generators and biomedical sensors, opening alternative avenues for human-body-centred applications.
Using multiple datasets and a partial correlation method, the authors analyze the different impacts of eastern Pacific (EP) and central Pacific (CP) El Niño on East Asian climate, focusing on the ...features from El Niño developing summer to El Niño decaying summer. Unlike the positive–negative–positive (+/−/+) anomalous precipitation pattern over East Asia and the equatorial Pacific during EP El Niño, an anomalous −/+/− rainfall pattern appears during CP El Niño. The anomalous dry conditions over southeastern China and the northwestern Pacific during CP El Niño seem to result from the anomalous low-level anticyclone over southern China and the South China Sea, which is located more westward than the Philippine Sea anticyclone during EP El Niño. The continuous anomalous sinking motion over southeastern China, as part of the anomalous Walker circulation associated with CP El Niño, also contributes to these dry conditions.
During the developing summer, the impact of CP El Niño on East Asian climate is more significant than the influence of EP El Niño. During the decaying summer, however, EP El Niño exerts a stronger influence on East Asia, probably due to the long-lasting anomalous warming over the tropical Indian Ocean accompanying EP El Niño.
Temperatures over portions of East Asia and the northwestern Pacific tend to be above normal during EP El Niño but below normal from the developing autumn to the next spring during CP El Niño. A possible reason is the weakened (enhanced) East Asian winter monsoon related to EP (CP) El Niño.
Immune checkpoint inhibitors had a great effect in triple-negative breast cancer (TNBC); however, they benefited only a subset of patients, underscoring the need to co-target alternative pathways and ...select optimal patients. Herein, we investigated patient subpopulations more likely to benefit from immunotherapy and inform more effective combination regimens for TNBC patients.
We conducted exploratory analyses in the FUSCC cohort to characterize a novel patient selection method and actionable targets for TNBC immunotherapy. We investigated this in vivo and launched a phase 2 trial to assess the clinical value of such criteria and combination regimen. Furthermore, we collected clinicopathological and next-generation sequencing data to illustrate biomarkers for patient outcomes.
CD8-positivity could identify an immunomodulatory subpopulation of TNBCs with higher possibilities to benefit from immunotherapy, and angiogenesis was an actionable target to facilitate checkpoint blockade. We conducted the phase II FUTURE-C-Plus trial to assess the feasibility of combining famitinib (an angiogenesis inhibitor), camrelizumab (a PD-1 monoclonal antibody) and chemotherapy in advanced immunomodulatory TNBC patients. Within 48 enrolled patients, the objective response rate was 81.3% (95% CI, 70.2-92.3), and the median progression-free survival was 13.6 months (95% CI, 8.4-18.8). No treatment-related deaths were reported. Patients with CD8- and/or PD-L1- positive tumors benefit more from this regimen. PKD1 somatic mutation indicates worse progression-free and overall survival.
This study confirms the efficacy and safety of the triplet regimen in immunomodulatory TNBC and reveals the potential of combining CD8, PD-L1 and somatic mutations to guide clinical decision-making and treatments.
ClinicalTrials.gov: NCT04129996 . Registered 11 October 2019.
Adrenomedullin (ADM) 2/intermedin (IMD) is a short peptide that belongs to the CGRP superfamily. Although it shares receptors with CGRP, ADM and amylin, ADM2 has significant and unique functions in ...the cardiovascular system. In the past decade, the cardiovascular effect of ADM2 has been carefully analysed. In this review, progress in understanding the effects of ADM2 on the cardiovascular system and its protective role in cardiometabolic diseases are summarized.
Linked Articles
This article is part of a themed section on Spotlight on Small Molecules in Cardiovascular Diseases. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v175.8/issuetoc
If I provide you a face image of mine (without telling you the actual age when I took the picture) and a large amount of face images that I crawled (containing labeled faces of different ages but not ...necessarily paired), can you show me what I would look like when I am 80 or what I was like when I was 5? The answer is probably a No. Most existing face aging works attempt to learn the transformation between age groups and thus would require the paired samples as well as the labeled query image. In this paper, we look at the problem from a generative modeling perspective such that no paired samples is required. In addition, given an unlabeled image, the generative model can directly produce the image with desired age attribute. We propose a conditional adversarial autoencoder (CAAE) that learns a face manifold, traversing on which smooth age progression and regression can be realized simultaneously. In CAAE, the face is first mapped to a latent vector through a convolutional encoder, and then the vector is projected to the face manifold conditional on age through a deconvolutional generator. The latent vector preserves personalized face features (i.e., personality) and the age condition controls progression vs. regression. Two adversarial networks are imposed on the encoder and generator, respectively, forcing to generate more photo-realistic faces. Experimental results demonstrate the appealing performance and flexibility of the proposed framework by comparing with the state-of-the-art and ground truth.
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