Burnout among surgeons before and during the SARS-CoV-2 pandemic: an international survey

Background: SARS-CoV-2 pandemic has had many significant impacts within the surgical realm, and surgeons have been obligated to reconsider almost every aspect of daily clinical practice. Methods: This is a cross-sectional study reported in compliance with the CHERRIES guidelines and conducted through an online platform from June 14th to July 15th, 2020. The primary outcome was the burden of burnout during the pandemic indicated by the validated Shirom-Melamed Burnout Measure. Results: Nine hundred fifty-four surgeons completed the survey. The median length of practice was 10 years; 78.2% included were male with a median age of 37 years old, 39.5% were consultants, 68.9% were general surgeons, and 55.7% were affiliated with an academic institution. Overall, there was a significant increase in the mean burnout score during the pandemic; longer years of practice and older age were significantly associated with less burnout. There were significant reductions in the median number of outpatient visits, operated cases, on-call hours, emergency visits, and research work, so, 48.2% of respondents felt that the training resources were insufficient. The majority (81.3%) of respondents reported that their hospitals were included in the management of COVID-19, 66.5% felt their roles had been minimized; 41% were asked to assist in non-surgical medical practices, and 37.6% of respondents were included in COVID-19 management. Conclusions: There was a significant burnout among trainees. Almost all aspects of clinical and research activities were affected with a significant reduction in the volume of research, outpatient clinic visits, surgical procedures, on-call hours, and emergency cases hindering the training. Trial registration: The study was registered on clicaltrials.gov "NCT04433286" on 16/06/2020

Quantitatively mimicking wet colloidal suspensions with dry granular media

Athermal two-dimensional granular systems are exposed to external mechanical noise leading to Brownian-like motion. Using tunable repulsive interparticle interaction, it is shown that the same microstructure as that observed in colloidal suspensions can be quantitatively recovered at a macroscopic scale. To that end, experiments on granular and colloidal systems made up of magnetized particles as well as computer simulations are performed and compared. Excellent agreement throughout the range of the magnetic coupling parameter Γ is found for the pair distribution as well as the bond-orientational correlation functions. This finding opens new ways to efficiently and very conveniently explore phase transitions, crystallization, nucleation, etc in confined geometries

Advancements in AI-driven diagnostic radiology: Enhancing accuracy and efficiency

Background: Healthcare delivery has transformed significantly with the integration of clinical decision support systems (CDS) and medical imaging. Convolutional neural networks (CNNs), a type of artificial intelligence (AI) algorithm, have exhibited remarkable accuracy in discerning intricate patterns and anomalies within medical images, surpassing human capability. Aim: This study aims to explore the impact of AI augmentation on diagnostic tasks, focusing on enhancing sensitivity, accuracy, and interrater agreement across various medical conditions. Additionally, it seeks to investigate how AI simplifies complex processes and integrates with existing technologies, extending its role in CDS systems beyond diagnostic accuracy. Methods: The research examines the effectiveness of AI in interpreting CT imaging and diagnosis. Furthermore, it assesses the integration of AI with radiology to enhance the detection of cerebral hemorrhages on head CT scans in time-pressed clinical settings. The research was performed using search engines such as google scholar and Pubmed. Results: The findings indicate that AI augmentation significantly enhances diagnostic capabilities, improves physician confidence, reduces interpretation time, and optimizes workflow efficiency. AI not only improves accuracy but also simplifies processes, thereby revolutionizing healthcare delivery. Conclusion: As artificial intelligence continues to evolve, its revolutionary potential in healthcare becomes increasingly evident

Radiological manifestations of COVID-19 variants and their impact on patient management

Background:  The COVID-19 pandemic has significantly impacted the field of radiology, leading to changes in the utilization and application of various imaging modalities. Initially, chest computed tomography (CT) was widely employed for screening and diagnosing COVID-19. However, the current recommendation is to use CT primarily for high-risk patients, individuals with severe disease, or in regions where polymerase chain reaction (PCR) testing is not widely accessible. Aim of Work: The aim of this research paper is to examine the evolving role of radiology, particularly chest radiography, in the management of COVID-19 patients, as well as to highlight the operational changes and technological advancements that have been implemented in the field of radiology during the pandemic. Methods: This research paper is a comprehensive review of the existing literature on the changing role of radiology in the COVID-19 pandemic. It synthesizes the available information on the utilization of various imaging modalities, such as chest radiography and CT, for the screening, diagnosis, and monitoring of COVID-19 patients. Additionally, it explores the operational changes and technological advancements that have been implemented in the field of radiology to address the challenges posed by the pandemic