Halbergvega7036

Objective Acute and subacute scaphoid fractures were assessed using 3D computer tomography (CT). The aims were to describe fracture morphology, to map fractures onto a 3D scaphoid model and to correlate this to scaphoid anatomy. Materials and methods A retrospective, multicentre database search was performed to identify CT studies of acute and subacute scaphoid fractures. CT scans of scaphoid fractures less than 6 weeks from time of injury were included in this retrospective, multicentre study. CTs were segmented and converted into three-dimensional models. Following virtual fracture reduction, fractures were mapped onto a three-dimensional scaphoid model. Results Seventy-five CT scans were included. The median delay from injury to CT was 29 days. Most studies were in male patients (89%). Most fractures were comminuted (52%) or displaced (64%). A total of 73% of displaced fractures had concomitant comminution. Waist fractures had higher rates of comminution and displacement when compared with all other fractures. Comminution was located along the dorsal ridge and the volar scaphoid waist. Conclusion Our study is the first to describe acute fracture morphology using 3D CT and to correlate comminution and displacement to fracture types. The dorsal ridge and volar waist need prudent assessment, especially in waist fractures.Radiomics is an emerging area in quantitative image analysis that aims to relate large-scale extracted imaging information to clinical and biological endpoints. The development of quantitative imaging methods along with machine learning has enabled the opportunity to move data science research towards translation for more personalized cancer treatments. Accumulating evidence has indeed demonstrated that noninvasive advanced imaging analytics, that is, radiomics, can reveal key components of tumor phenotype for multiple three-dimensional lesions at multiple time points over and beyond the course of treatment. These developments in the use of CT, PET, US, and MR imaging could augment patient stratification and prognostication buttressing emerging targeted therapeutic approaches. In recent years, deep learning architectures have demonstrated their tremendous potential for image segmentation, reconstruction, recognition, and classification. Many powerful open-source and commercial platforms are currently available to embark in new research areas of radiomics. Quantitative imaging research, however, is complex and key statistical principles should be followed to realize its full potential. The field of radiomics, in particular, requires a renewed focus on optimal study design/reporting practices and standardization of image acquisition, feature calculation, and rigorous statistical analysis for the field to move forward. In this article, the role of machine and deep learning as a major computational vehicle for advanced model building of radiomics-based signatures or classifiers, and diverse clinical applications, working principles, research opportunities, and available computational platforms for radiomics will be reviewed with examples drawn primarily from oncology. We also address issues related to common applications in medical physics, such as standardization, feature extraction, model building, and validation.Objective The objective of this study was to determine the attitudes of laboratory personnel toward the application of artificial intelligence (AI) in the laboratory. Methods We surveyed laboratory employees who covered a range of work roles, work environments, and educational levels. Results The survey response rate was 42%. Most respondents (79%) indicated that they were at least somewhat familiar with AI. Very few (4%) classified themselves as experts. Contact with AI varied by educational level (P = .005). Respondents believed that AI could help them perform their work by reducing errors (24%) and saving time (16%). The most common concern (27%) was job security (being replaced by AI). The majority (64%) of the respondents expressed support for the development of AI projects in the organization. Conclusions Laboratory employees see the potential for AI and generally support the adoption of AI tools but have concerns regarding job security and quality of AI performance.A 5-year-old female child, with known systemic juvenile idiopathic arthritis diagnosed at 18 months of age (on low dose Prednisolone + Methotrexate + Leflunomide + Tocilizumab), presented with fever for 1 day, vomiting, drowsiness followed by seizures. On admission to PICU, she was drowsy, tachycardic, tachypneic, with rashes, and hepatosplenomegaly. Lab findings showed thrombocytopenia, leucopenia, low ESR, normal CRP, elevated liver enzymes, high ferritin, LDH, and triglycerides suggestive of macrophage activation syndrome (MAS). Chest X-ray showed left basal pneumonia and DNA PCR of throat swab revealed adenovirus. She was diagnosed as adenovirus-triggered MAS and was initiated on pulse methylprednisolone (6 mg/kg). Because of suboptimal response after 2 doses, manifested by increasing drowsiness, further fall in platelets and rising ferritin, methylprednisolone dosage was increased to 30 mg/kg/day with the addition of oral cyclosporine (4 mg/kg/day). In view of worsening of the chest X-ray and increasing oxygen requirement, Cidofovir infusion (1 mg/kg thrice weekly) was also started simultaneously considering increased activity of the adenoviral infection concurrent to immunosuppression. Within 48 h, the child showed signs of recovery with improved consciousness, lower oxygen requirements, and improving lab parameters. She was discharged after 3 weeks of IV Cidofovir, on oral prednisolone and cyclosporine. To the best of our knowledge, this is the first reported use of Cidofovir in adenovirus-induced MAS.According to previous epidemiological studies, we can reduce the thickness of epicardial fat and improve cardiovascular risk factors through exercise, and the changes may depend on the form of exercise. We systemically reviewed published studies that evaluated exercise intervention on epicardial adipose tissue (EAT) levels. We included randomized controlled trials (RCTs) comparing one exercise with another exercise or diet for the treatment to reduce EAT. JAK inhibitors in development We used fixed effects models for meta-analyses; effects of exercise on outcomes were described as mean differences (MD) or standardized difference of means (SMD) was used, their 95% confidence intervals (CI). Five RCTs were included (n = 299), 156 in exercise group and 143 in the control. In comparison to the control group, exercise significantly reduced EAT (SMD - 0.57, 95%CI - 0.97 to - 0.18) and waist circumference (MD - 2.95 cm, 95%CI - 4.93 to - 0.97). Exercise did not have an effect on BMI (MD - 0.23 kg/m2, 95%CI - 0.73 to 0.27), weight (MD - 0.06 kg, 95%CI - 1.