- Selective androgen receptor modulators (SARMs) have specific impacts on the mouse uterus. [Journal Article]
- JEJ Endocrinol 2019 Jul 01
- Selective androgen receptor modulators (SARMs) have been proposed as therapeutics for women suffering from breast cancer, muscle wasting or urinary incontinence. The androgen receptor (AR) is express…
Selective androgen receptor modulators (SARMs) have been proposed as therapeutics for women suffering from breast cancer, muscle wasting or urinary incontinence. The androgen receptor (AR) is expressed in the uterus but the impact of SARMs on the function of this organ is unknown. We used a mouse model to compare the impact of SARMs (GTx-007/Andarine, GTx-024/Enobosarm), Danazol (a synthetic androstane steroid) and dihydrotestosterone (DHT) on tissue architecture, cell proliferation and gene expression. Ovariectomised mice were treated daily for 7 days with compound or vehicle control (VC). Uterine morphometric characteristics were quantified using high-throughput image analysis (StrataQuest; TissueGnostics), protein and gene expression were evaluated by immunohistochemistry and RT-qPCR, respectively. Treatment with GTx-024, Danazol or DHT induced significant increases in body weight, uterine weight and the surface area of the endometrial stromal and epithelial compartments compared to VC. Treatment with GTx-007 had no impact on these parameters. GTx-024, Danazol and DHT all significantly increased the percentage of Ki67-positive cells in the stroma, but only GTx-024 had an impact on epithelial cell proliferation. GTx-007 significantly increased uterine expression of Wnt4 and Wnt7a, whereas GTx-024 and Danazol decreased their expression. In summary, the impact of GTx-024 and Danazol on uterine cells mirrored that of DHT, whereas GTx-007 had minimal impact on the tested parameters. This study has identified endpoints that have revealed differences in the effects of SARMs on uterine tissue and provides a template for preclinical studies comparing the impact of compounds targeting the AR on endometrial function.
- The image-guided operating room-Utility and impact on surgeon's performance in the head and neck surgery. [Journal Article]
- HNHead Neck 2019 Jul 09
- CONCLUSIONS: The image quality of CBCT in the image-guided OR is good for bony detail and complex oncological reconstructions in the head and neck setting but probably has limited benefit for intraoperative soft tissue delineation. Future studies must also focus on clinical outcomes to help demonstrate the value of the image-guided OR.
- Multiplexed ELISA screening assay for nine paralytic shellfish toxins in human plasma. [Journal Article]
- AAnalyst 2019 Jul 04
- Paralytic shellfish poisoning is a lethal syndrome that can develop in humans who consume shellfish contaminated with paralytic shellfish toxins. These toxins have a short half-life in the human body…
Paralytic shellfish poisoning is a lethal syndrome that can develop in humans who consume shellfish contaminated with paralytic shellfish toxins. These toxins have a short half-life in the human body, so a rapid diagnostic assessment of the poisoning is necessary. In this paper, we have developed and validated a rapid ELISA screening assay using anti-saxitoxin antibodies to screen nine toxins: saxitoxin; decarbamoyl saxitoxin; gonyautoxin 2,3; decarbamoyl GTX 2,3; neosaxitoxin; and gonyautoxin 1,4, in human plasma with lower limits of detection of 0.02, 0.08, 0.12, 1.2, 5.0, and 25 ng mL-1, respectively. Intra-day and inter-day precision experiments showed good reproducibility with a percent coefficient of variation less than 15%. The assay was 100% accurate in determining the presence or absence of these toxins in human plasma specimens. Blank specimens were assessed as negative for toxin content indicating that the method has excellent analyte specificity. This rapid screening assay can be used to quickly diagnose exposure to paralytic shellfish toxins, though an additional confirmatory method will be necessary to identify and quantitate the specific toxin in an exposure.
- Accurate, rapid and reliable, fully automated MRI brainstem segmentation for application in multiple sclerosis and neurodegenerative diseases. [Journal Article]
- HBHum Brain Mapp 2019 Jun 17
- Neurodegenerative disorders, such as Alzheimer's disease (AD) and progressive forms of multiple sclerosis (MS), can affect the brainstem and are associated with atrophy that can be visualized by MRI.…
Neurodegenerative disorders, such as Alzheimer's disease (AD) and progressive forms of multiple sclerosis (MS), can affect the brainstem and are associated with atrophy that can be visualized by MRI. Anatomically accurate, large-scale assessments of brainstem atrophy are challenging due to lack of automated, accurate segmentation methods. We present a novel method for brainstem volumetry using a fully-automated segmentation approach based on multi-dimensional gated recurrent units (MD-GRU), a deep learning based semantic segmentation approach employing a convolutional adaptation of gated recurrent units. The neural network was trained on 67 3D-high resolution T1-weighted MRI scans from MS patients and healthy controls (HC) and refined using segmentations of 20 independent MS patients' scans. Reproducibility was assessed in MR test-retest experiments in 33 HC. Accuracy and robustness were examined by Dice scores comparing MD-GRU to FreeSurfer and manual brainstem segmentations in independent MS and AD datasets. The mean %-change/SD between test-retest brainstem volumes were 0.45%/0.005 (MD-GRU), 0.95%/0.009 (FreeSurfer), 0.86%/0.007 (manually edited segmentations). Comparing MD-GRU to manually edited segmentations the mean Dice scores/SD were: 0.97/0.005 (brainstem), 0.95/0.013 (mesencephalon), 0.98/0.006 (pons), 0.95/0.015 (medulla oblongata). Compared to the manual gold standard, MD-GRU brainstem segmentations were more accurate than FreeSurfer segmentations (p < .001). In the multi-centric acquired AD data, the mean Dice score/SD for the MD-GRU-manual segmentation comparison was 0.97/0.006. The fully automated brainstem segmentation method MD-GRU provides accurate, highly reproducible, and robust segmentations in HC and patients with MS and AD in 200 s/scan on an Nvidia GeForce GTX 1080 GPU and shows potential for application in large and longitudinal datasets.
- Intraoperative cone-beam CT-guided osteotomy navigation in mandible and maxilla surgery. [Journal Article]
- LLaryngoscope 2019 May 21
- CONCLUSIONS: The overall performance in comparison to alternative approaches warrants further consideration. In terms of accuracy, the results presented here are comparable to recent systematic reviews assessing CAD-CAM cutting guides that cite accuracies of ~2 to 2.5 mm.
- Projection-domain scatter correction for cone beam computed tomography using a residual convolutional neural network. [Journal Article]
- MPMed Phys 2019; 46(7):3142-3155
- CONCLUSIONS: The proposed deep learning-based method provides an effective tool for CBCT scatter correction and holds significant value for quantitative imaging and image-guided radiation therapy.
- Analysis of hematologic parameters of donors, patients, and granulocyte concentrates to predict successful granulocyte transfusion. [Journal Article]
- BRBlood Res 2019; 54(1):52-56
- CONCLUSIONS: The TWBCC and ANC after GTx were significant factors to predict patients' outcome. Therefore, follow-up of those two parameters may be helpful to select or consider other therapeutic modalities including additional GTx.
- Robust segmentation of arterial walls in intravascular ultrasound images using Dual Path U-Net. [Journal Article]
- UUltrasonics 2019; 96:24-33
- A Fully Convolutional Network (FCN) based deep architecture called Dual Path U-Net (DPU-Net) is proposed for automatic segmentation of the lumen and media-adventitia in IntraVascular UltraSound (IVUS…
A Fully Convolutional Network (FCN) based deep architecture called Dual Path U-Net (DPU-Net) is proposed for automatic segmentation of the lumen and media-adventitia in IntraVascular UltraSound (IVUS) frames, which is crucial for diagnosis of many cardiovascular diseases and also for facilitating 3D reconstructions of human arteries. One of the most prevalent problems in medical image analysis is the lack of training data. To overcome this limitation, we propose a twofold solution. First, we introduce a deep architecture that is able to learn using a small number of training images and still achieves a high degree of generalization ability. Second, we strengthen the proposed DPU-Net by having a real-time augmentor control the image augmentation process. Our real-time augmentor contains specially-designed operations that simulate three types of IVUS artifacts and integrate them into the training images. We exhaustively assessed our twofold contribution over Balocco's standard publicly available IVUS 20 MHz and 40 MHz B-mode dataset, which contain 109 training image, 326 test images and 19 training images, 59 test images, respectively. Models are trained from scratch with the training images provided and evaluated with two commonly used metrics in the IVUS segmentation literature, namely Jaccard Measure (JM) and Hausdorff Distance (HD). Experimental results show that DPU-Net achieves 0.87 JM, 0.82 mm HD and 0.86 JM, 1.07 mm HD over 40 MHz dataset for segmenting the lumen and the media, respectively. Also, DPU-Net achieves 0.90 JM, 0.25 mm HD and 0.92 JM, 0.30 mm HD over 20 MHz images for segmenting the lumen and the media, respectively. In addition, DPU-Net outperforms existing methods by 8-15% in terms of HD distance. DPU-Net also shows a strong generalization property for predicting images in the test sets that contain a significant amount of major artifacts such as bifurcations, shadows, and side branches that are not common in the training set. Furthermore, DPU-Net runs within 0.03 s to segment each frame with a single modern GPU (Nvidia GTX 1080). The proposed work leverages modern deep learning-based method for segmentation of lumen and the media vessel walls in both 20 MHz and 40 MHz IVUS B-mode images and achieves state-of-the-art results without any manual intervention. The code is available online at https://github.com/Kulbear/IVUS-Ultrasonic.
- Vehicle Detection in Urban Traffic Surveillance Images Based on Convolutional Neural Networks with Feature Concatenation. [Journal Article]
- SSensors (Basel) 2019 Jan 30; 19(3)
- Vehicle detection with category inference on video sequence data is an important but challenging task for urban traffic surveillance. The difficulty of this task lies in the fact that it requires acc…
Vehicle detection with category inference on video sequence data is an important but challenging task for urban traffic surveillance. The difficulty of this task lies in the fact that it requires accurate localization of relatively small vehicles in complex scenes and expects real-time detection. In this paper, we present a vehicle detection framework that improves the performance of the conventional Single Shot MultiBox Detector (SSD), which effectively detects different types of vehicles in real-time. Our approach, which proposes the use of different feature extractors for localization and classification tasks in a single network, and to enhance these two feature extractors through deconvolution (D) and pooling (P) between layers in the feature pyramid, is denoted as DP-SSD. In addition, we extend the scope of the default box by adjusting its scale so that smaller default boxes can be exploited to guide DP-SSD training. Experimental results on the UA-DETRAC and KITTI datasets demonstrate that DP-SSD can achieve efficient vehicle detection for real-world traffic surveillance data in real-time. For the UA-DETRAC test set trained with UA-DETRAC trainval set, DP-SSD with the input size of 300 × 300 achieves 75.43% mAP (mean average precision) at the speed of 50.47 FPS (frames per second), and the framework with a 512 × 512 sized input reaches 77.94% mAP at 25.12 FPS using an NVIDIA GeForce GTX 1080Ti GPU. The DP-SSD shows comparable accuracy, which is better than those of the compared state-of-the-art models, except for YOLOv3.
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- Analysis of simulated mandibular reconstruction using a segmental mirroring technique. [Journal Article]
- JCJ Craniomaxillofac Surg 2019; 47(3):468-472
- CONCLUSIONS: The segmental mirroring technique can be used reliably to generate highly accurate three-dimensional models that may assist with mandibular reconstruction in circumstances where bony deformity limits intraoperative adaptation of a reconstruction plate. This technique is less accurate where pathology involves the mandibular condyle and, to a lesser degree, the coronoid process.