Gout is an arthritis characterized by the deposition of sodium monoacid crystals in the synovial membrane, articular cartilage, and periarticular tissues that leads to an inflamatory process. In most cases, the diagnosis is established by clinical criteria and analysis of the synovial fluid for MSU crystals. However, gout may manifest in atypical ways and make diagnosis difficult. In these situations, imaging studies play a fundamental role in helping to confirm the diagnosis or even exclude other differential diagnoses. Conventional radiography is still the most commonly used method in the follow-up of these patients, but it is a very insensitive test, because it only detects late changes. In recent years, advances in imaging methods have emerged in relation to gout. Ultrasound has proven to be a highly accurate test in the diagnosis of gout, identifying MSU deposits in articular cartilage and periarticular tissues, and detecting and characterizing tophi, tendinopathies, and tophi enthesopathies. Computed tomography is an excellent exam for the detection of bone erosions and evaluation of spinal involvement. Dual-energy computed tomography, a new method that provides information on the chemical composition of tissues, allows identification of MSU deposits with high accuracy. MRI can be useful in the evaluation of deep tissues not accessible by ultrasound. In addition to diagnosis, with the emergence of drugs that aim to reduce the tophaceous burden, imaging examinations become a useful tool in the follow-up treatment of gout patients.

Objective: To investigate the value of differential diagnosis of hepatocellular carcinoma (HCC) and cirrhotic nodules via radiomics models based on magnetic resonance images. Background: This study is to distinguish hepatocellular carcinoma and cirrhotic nodules using MR-radiomics features extracted from four different phases of MRI images, concluded T1WI, T2WI, T2 SPIR and delay phase of contrast MRI. Methods: In this study, the four kind of magnetic resonance images of 23 patients with hepatocellular carcinoma (HCC) were collected. Among them, 12 patients with liver cirrhosis were used to obtain cirrhotic nodules (CN). The dataset was used to extract MR-radiomics features from regions of interest (ROI). The statistical methods of MRradiomics features could distinguish HCC and CN. And the ability of radiomics features between HCC and CN was estimated by receiver operating characteristic curve (ROC). Results: A total of 424 radiomics features were extracted from four kind of magnetic resonance images. 86 features in delay phase of contrast MRI，86 features in spir phase of T2WI，86 features in T1WI and 88 features in T2WI showed statistical difference (p < 0.05). Among them, the area under the curves (AUC) of these features larger than 0.85 were 58 features in delay phase of contrast MRI, 54 features in spir phase of T2WI, 62 features in T1WI and 57 features in T2WI. Conclusions: Radiomics features extracted from MRI images have the potential to distinguish HCC and CN.

Imaging technology plays a key role in guiding endovascular treatment of aortic aneurysm, especially in the complex thoracoabdominal aorta. The combination of high quality images with a sterile and functional environment in the surgical suite can reduce contrast and radiation exposure for both patient and operator, in addition to better outcomes. This presentation aims to describe the current use of this technique, combining angiotomography and intraoperative cone beam computed tomography, image “fusion” and intravascular ultrasound, to guide procedures and thus improve the intraoperative success rate and reduce the need for reoperation. On the other hand, a procedure is described to create customized 3D templates with the high-definition images of the patient’s arterial anatomy, which serve as specific guides for making fenestrated stents in the operating room. These customized fenestration templates could expand the number of patients with complex aneurysms treated minimally invasively.

Introduction: Chest trauma has a high incidence and pneumothorax is the most frequent finding. The literature is scarce on what to do with asymptomatic patients with pneumothorax due to penetrating chest trauma. The aim of this study was to evaluate what are the findings of the control radiography of patients with penetrating chest trauma who are not initially taken to surgery, and their usefulness in determining the need for further treatment. Methods: A retrospective cohort study was performed, including patients older than 15 years who were admitted for penetrating chest trauma between January 2015 and December 2017 and who did not require initial surgical management. We analyzed the results of chest radiography, the time of its acquisition, and the behavior decided according to the findings in patients initially left under observation. Results: A total of 1,554 patients were included, whose average age was 30 years, 92.5% were male and 97% had a sharp weapon wound. Of these, 186 (51.5%) had no alterations in their initial X-ray, 142 had pneumothorax less than 30% and 33 had pneumothorax greater than 30 %, hemopneumothorax or hemothorax. Closed thoracostomy was required as the final procedure in 78 cases, sternotomy or thoracotomy in 2 cases and discharged in 281. Conclusion: In asymptomatic patients with small or moderate pneumothorax and no other significant lesions, longer observation times, radiographs and closed thoracostomy may be unnecessary.

The use of artificial intelligence (AI) in the detection and diagnosis of plant diseases has gained significant interest in modern agriculture. The appeal of AI arises from its ability to rapidly and precisely analyze extensive and complex information, allowing farmers and agricultural experts to quickly identify plant diseases. The use of artificial intelligence (AI) in the detection and diagnosis of plant diseases has gained significant attention in the world of agriculture and agronomy. By harnessing the power of AI to identify and diagnose plant diseases, it is expected that farmers and agricultural experts will have improved capabilities to tackle the challenges posed by these diseases. This will lead to increased effectiveness and efficiency, ultimately resulting in higher agricultural productivity and reduced losses caused by plant diseases. The use of artificial intelligence (AI) in the detection and diagnosis of plant diseases has resulted in significant benefits in the field of agriculture. By using AI technology, farmers and agricultural professionals can quickly and accurately identify illnesses affecting their crops. This allows for the prompt adoption of appropriate preventative and corrective actions, therefore reducing losses caused by plant diseases.

Diagnosis-related groups (DRGs) are gaining prominence in healthcare systems worldwide to standardize potential payments to hospitals. This study, conducted across public hospitals, investigates the impact of DRG implementation on human resource allocation and management practices. The research findings reveal significant changes in job roles and skill requirements based on a mixed-methods approach involving 70 healthcare professionals across various roles. 50% of respondents reported changes in daily responsibilities, and 42% noted the creation of new roles in their organizations. Significant challenges include inadequate training (46%), and coding complexity (38%). Factor analysis revealed a complex relationship between DRG familiarity, job satisfaction, and staff morale. The study also found a moderate negative correlation between the impact on morale and years of service in the current hospital, suggesting that longer-tenured staff may require additional support in adapting to DRG systems. This study addresses a knowledge gap in the human resource aspects of DRG implementation. It provides healthcare administrators and policymakers with evidence to inform strategies for effective DRG adoption and workforce management in public hospitals.