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

Wet-Cupping's Impact on Pancreatitis Induced by Hypertriglyceridemia: A case study and brief literature review

Familial hypertriglyceridemia is a genetic disorder marked by excessive production of very low-density lipoproteins, resulting in elevated serum triglyceride levels. This can lead to various medical conditions, including acute pancreatitis. In cases of recurrence, it may progress to chronic pancreatitis. Cupping therapy, a traditional treatment practiced in numerous cultures worldwide, is utilized to address various medical conditions. This case report presents a 34-year-old male diagnosed with familial hypertriglyceridemia, subsequently developing chronic pancreatitis. During his last presentation with acute-on-chronic pancreatitis, his lipid profile revealed a notable reduction in serum triglycerides. Interestingly, this reduction coincided with the introduction of cupping therapy into his treatment regimen. Remarkably, following the initiation of cupping therapy, his hospital admissions for acute pancreatitis notably decreased. This case report highlights the potential impact of cupping therapy on familial hypertriglyceridemia, potentially mitigating the risk of acute pancreatitis. Keywords: Hyperlipidemia; Hypertriglyceridemia; Pancreatitis

Inclusion Body Myositis: Navigating diagnostic challenges, case report

Inclusion body myositis (IBM) is a rare progressive myopathy affecting individuals older than 50 years. It is associated with significant morbidity once restricting the patient's mobility, and it has a relatively low mortality risk with respiratory muscles involvement. Muscle biopsy is the gold standard method for diagnosis. In this complex scenario, we present a case involving a 72-year-old woman admitted to our hospital with progressive weakness of lower limbs. The diagnostic process was challenging due to the case's complexity necessitating a multidisciplinary team approach. This case highlights the intricate nature of the diagnostic journey, as diagnosing IBM remains a challenge in clinical practice, requiring a high suspicion and precise application of available diagnostic tools with the guidance of a collaborative multidisciplinary approach in investigating and providing patient care. This case report contributes valuable insights to the understanding of this complex myopathy, facilitating more accurate diagnosis and enhancing patient care strategies Keywords: Sporadic inclusion body myositis; idiopathic inflammatory myopathy; rimmed vacuole

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