1-[1-(3-Methyl­phen­yl)-5-phenyl-4-phenyl­sulfonyl-1H-pyrazol-3-yl]ethanone

Both the acetyl and phenyl substituents of the central pyrazole ring in the title compound, C24H20N2O3S, are twisted with respect to the pyrazole ring, with the twist involving the phenyl ring being greater [67.4 (1) and 29.6 (2)°]. The tolyl substituent is disordered over two positions in a 1:1 ratio; the mean planes of the aromatic ring are aligned at 67.7 (3) and 69.4 (3)° with respect to the pyrazole ring

Open Reduction and Internal Fixation with a Small T-plate for Volar Barton Fracture Management

Background: One-sixth of all fractures in the emergency room are distal radius fractures. It is very important to recognize and adequately treat volar Barton fractures to avoid complication of malunion and its adverse effects. Although various fixation techniques have been described, with the plate, the patient can begin early active wrist workouts performing stable reduction. Objective: Open reduction and small T-plate internal fixation of a distal radius volar Barton fracture were used to evaluate the functional outcomes for the fracture treatment. Patients and Methods: At Zagazig University Hospital, 30 patients with a volar Barton fracture were studied in prospective interventional research, the study was carried out through six months. Preoperative X-ray and CT were done and the patient was prepared for surgery. By adopting an FCR technique (flexor carpi radialis approach), the fracture was reduced, the plate was fixed, and the image intensification was utilized for confirming the results. Results: Mean operative time was 54.1±8.47 and of 30 patients operated upon, 16 patients were discharged one day after operation while the mean time lapse before surgery was 1.3±0.53. The mean time of bone union was 6.5±0.89 weeks (range 5-8 weeks). There was a significant improvement in wrist range of motion in all directions postoperatively. 2 patients (6.7%) had superficial infection, 1 patient (3.3%) had tourniquet paralysis, 1 patient had stiffness (3.3%) and another had mal-united fracture (3.3%). Conclusion: Volar distal buttressing with the Ellis T plate is easy and inexpensive, and it delivers good functional benefits. Simplistic and low-complication procedure provides precise anatomical reduction of the fracture and restoration of the wrist's shape and function

Recognizing food places in egocentric photo-streams using multi-scale atrous convolutional networks and self-attention mechanism.

Wearable sensors (e.g., lifelogging cameras) represent very useful tools to monitor people's daily habits and lifestyle. Wearable cameras are able to continuously capture different moments of the day of their wearers, their environment, and interactions with objects, people, and places reflecting their personal lifestyle. The food places where people eat, drink, and buy food, such as restaurants, bars, and supermarkets, can directly affect their daily dietary intake and behavior. Consequently, developing an automated monitoring system based on analyzing a person's food habits from daily recorded egocentric photo-streams of the food places can provide valuable means for people to improve their eating habits. This can be done by generating a detailed report of the time spent in specific food places by classifying the captured food place images to different groups. In this paper, we propose a self-attention mechanism with multi-scale atrous convolutional networks to generate discriminative features from image streams to recognize a predetermined set of food place categories. We apply our model on an egocentric food place dataset called 'EgoFoodPlaces' that comprises of 43 392 images captured by 16 individuals using a lifelogging camera. The proposed model achieved an overall classification accuracy of 80% on the 'EgoFoodPlaces' dataset, respectively, outperforming the baseline methods, such as VGG16, ResNet50, and InceptionV3

Plasma Gamma-Glutamyltransferase Is Strongly Determined by Acylation Stimulating Protein Levels Independent of Insulin Resistance in Patients with Acute Coronary Syndrome

Background. Steatosis is a manifestation of the metabolic syndrome often associated with release of liver enzymes and inflammatory adipocytokines linked to cardiovascular risk. Gamma-glutamyltransferase (GGT) is one sensitive liver marker recently identified as an independent cardiovascular risk factor. Mechanisms involved in enhanced hepatic lipogenesis causing steatosis are not yet identified and are usually linked to insulin resistance (IR). Acylation stimulating protein (ASP), a potent lipogenic factor, was recently shown to increase in patients with steatosis and was implicated in its pathogenesis. Aim. To investigate the association of plasma ASP levels with liver and metabolic risk markers in acute coronary syndrome (ACS) patients. Methods. 28 patients and 30 healthy controls were recruited. Their anthropometrics, lipid profile, liver markers, insulin, and ASP levels were measured. Results. In the patients, ASP, liver, and metabolic risk markers were markedly higher than in the controls. ASP strongly predicted GGT levels ( = 0.75, < 0.0001), followed by triglycerides ( = 0.403, = 0.017), together determining 57.6% variation in GGT levels. Insulin and IR correlated with metabolic risk components but not with liver enzymes. Conclusion. The strong association of ASP with GGT in ACS patients suggests that ASP, independent of IR, may contribute to a vicious cycle of hepatic lipogenic stimulation and GGT release promoting atherogenesis

Breast tumor segmentation in ultrasound images using contextual-information-aware deep adversarial learning framework.

Automatic tumor segmentation in breast ultrasound (BUS) images is still a challenging task because of many sources of uncertainty, such as speckle noise, very low signal-to-noise ratio, shadows that make the anatomical boundaries of tumors ambiguous, as well as the highly variable tumor sizes and shapes. This article proposes an efficient automated method for tumor segmentation in BUS images based on a contextual information-aware conditional generative adversarial learning framework. Specifically, we exploit several enhancements on a deep adversarial learning framework to capture both texture features and contextual dependencies in the BUS images that facilitate beating the challenges mentioned above. First, we adopt atrous convolution (AC) to capture spatial and scale context (i.e., position and size of tumors) to handle very different tumor sizes and shapes. Second, we propose the use of channel attention along with channel weighting (CAW) mechanisms to promote the tumor-relevant features (without extra supervision) and mitigate the effects of artifacts. Third, we propose to integrate the structural similarity index metric (SSIM) and L1-norm in the loss function of the adversarial learning framework to capture the local context information derived from the area surrounding the tumors. We used two BUS image datasets to assess the efficiency of the proposed model. The experimental results show that the proposed model achieves competitive results compared with state-of-the-art segmentation models in terms of Dice and IoU metrics. The source code of the proposed model is publicly available at https://github.com/vivek231/Breast-US-project

Breast tumor segmentation and shape classification in mammograms using generative adversarial and convolutional neural network.

Mammogram inspection in search of breast tumors is a tough assignment that radiologists must carry out frequently. Therefore, image analysis methods are needed for the detection and delineation of breast tumors, which portray crucial morphological information that will support reliable diagnosis. In this paper, we proposed a conditional Generative Adversarial Network (cGAN) devised to segment a breast tumor within a region of interest (ROI) in a mammogram. The generative network learns to recognize the tumor area and to create the binary mask that outlines it. In turn, the adversarial network learns to distinguish between real (ground truth) and synthetic segmentations, thus enforcing the generative network to create binary masks as realistic as possible. The cGAN works well even when the number of training samples are limited. As a consequence, the proposed method outperforms several state-of-the-art approaches. Our working hypothesis is corroborated by diverse segmentation experiments performed on INbreast and a private in-house dataset. The proposed segmentation model, working on an image crop containing the tumor as well as a significant surrounding area of healthy tissue (loose frame ROI), provides a high Dice coefficient and Intersection over Union (IoU) of 94% and 87%, respectively. In addition, a shape descriptor based on a Convolutional Neural Network (CNN) is proposed to classify the generated masks into four tumor shapes: irregular, lobular, oval and round. The proposed shape descriptor was trained on DDSM, since it provides shape ground truth (while the other two datasets does not), yielding an overall accuracy of 80%, which outperforms the current state-of-the-art

SLSNet: Skin lesion segmentation using a lightweight generativeadversarial network

The determination of precise skin lesion boundaries in dermoscopic images using automated methods faces many challenges, most importantly, the presence of hair, inconspicuous lesion edges and low contrast in dermoscopic images, and variability in the color, texture and shapes of skin lesions. Existing deep learning-based skin lesion segmentation algorithms are expensive in terms of computational time and memory. Consequently, running such segmentation algorithms requires a powerful GPU and high bandwidth memory, which are not available in dermoscopy devices. Thus, this article aims to achieve precise skin lesion segmentation with minimum resources: a lightweight, efficient generative adversarial network (GAN) model called SLSNet, which combines 1-D kernel factorized networks, position and channel attention, and multiscale aggregation mechanisms with a GAN model. The 1-D kernel factorized network reduces the computational cost of 2D filtering. The position and channel attention modules enhance the discriminative ability between the lesion and non-lesion feature representations in spatial and channel dimensions, respectively. A multiscale block is also used to aggregate the coarse-to-fine features of input skin images and reduce the effect of the artifacts. SLSNet is evaluated on two publicly available datasets: ISBI 2017 and the ISIC 2018. Although SLSNet has only 2.35 million parameters, the experimental results demonstrate that it achieves segmentation results on a par with the state-of-the-art skin lesion segmentation methods with an accuracy of 97.61%, and Dice and Jaccard similarity coefficients of 90.63% and 81.98%, respectively. SLSNet can run at more than 110 frames per second (FPS) in a single GTX1080Ti GPU, which is faster than well-known deep learning-based image segmentation models, such as FCN. Therefore, SLSNet can be used for practical dermoscopic applications

Comparative Study of Ambulatory Blood Pressure Monitoring and Clinic Blood Pressure Measurement in the Risk Assessment and Management of Hypertension

Objectives: Blood pressure (BP) measurements taken in a physician’s clinic do not represent readings throughout the day. Ambulatory blood pressure monitoring (ABPM) overcomes this problem by providing multiple readings with minimal interference with the patient’s daily activities. The purpose of our study was to evaluate the value of ABPM in risk assessment and management of hypertension compared to office measurements. Methods: A total of 104 consecutive hypertensive patients were retrospectively studied from January 2007 to December 2009. The following data were gathered: 1) clinic BP measurements; 2) routine blood test results; 3) electrocardiography, echocardiography, and 4) 24-hour ABPM. Results: The mean age of patients was 41.1 ± 8.6 years and 51.9% of them male. Indications for ABPM were: suspected “white coat” hypertension (10.6%), de novo hypertension (18.2%), resistant hypertension (27.9%) and others (43.3%). Mean daytime and nighttime BP were 134/82 and 124/73 mmHg respectively. A non-dipping pattern was reported in 64.4%. Echocardiographic evidence of left ventricular hypertrophy (LVH) and diastolic dysfunction (LVDD) was encountered in 22.1% and 29.8% respectively. ABPM parameters were significantly correlated with LVDD (P = 0.043). Patients with proved “white coat” hypertension did not receive antihypertensive therapy. Conclusion: Twenty-four hour ABPM is an important yet underused tool for proper risk stratification of treated hypertensive patients. The non-dipping profile is associated with a higher incidence of diastolic dysfunction. Our collective results revealed the superiority of ABPM over office BP measurement. Keywords: Hypertension; Blood pressure monitoring, ambulatory; Hypertrophy; Left Ventricular; Ventricular Dysfunction, Left