Evolving trends in the management of acute appendicitis during COVID-19 waves. The ACIE appy II study

Background: In 2020, ACIE Appy study showed that COVID-19 pandemic heavily affected the management of patients with acute appendicitis (AA) worldwide, with an increased rate of non-operative management (NOM) strategies and a trend toward open surgery due to concern of virus transmission by laparoscopy and controversial recommendations on this issue. The aim of this study was to survey again the same group of surgeons to assess if any difference in management attitudes of AA had occurred in the later stages of the outbreak. Methods: From August 15 to September 30, 2021, an online questionnaire was sent to all 709 participants of the ACIE Appy study. The questionnaire included questions on personal protective equipment (PPE), local policies and screening for SARS-CoV-2 infection, NOM, surgical approach and disease presentations in 2021. The results were compared with the results from the previous study. Results: A total of 476 answers were collected (response rate 67.1%). Screening policies were significatively improved with most patients screened regardless of symptoms (89.5% vs. 37.4%) with PCR and antigenic test as the preferred test (74.1% vs. 26.3%). More patients tested positive before surgery and commercial systems were the preferred ones to filter smoke plumes during laparoscopy. Laparoscopic appendicectomy was the first option in the treatment of AA, with a declined use of NOM. Conclusion: Management of AA has improved in the last waves of pandemic. Increased evidence regarding SARS-COV-2 infection along with a timely healthcare systems response has been translated into tailored attitudes and a better care for patients with AA worldwide

European sourcebook of crime and criminal justice statistics – 2021

This is the sixth edition of a data collection initiative that started in 1993 under the umbrella of the Council of Europe and has been continued since 2000 by an international group of experts that created the European Sourcebook of Criminal Justice e.V.1 and is also a Working Group of the European Society of Criminology. These experts act as regional coordinators of a network of national correspondents whose contribution has been decisive in collecting and validating data on a variety of subjects from 42 countries.2 This edition of the Sourcebook is composed of six chapters. The first five cover the current main types of national crime and criminal justice statistics – police, prosecution, conviction, prison, and probation statistics – for the years 2011 to 2016, providing detailed analysis for 2015. The sixth chapter covers national victimization surveys, providing rates for the main indicators every five years from 1990 to 2015. As with every new edition of the Sourcebook, the group has tried to improve data quality as well as comparability and, where appropriate, increase the scope of data collection. For example, offence definitions were updated to reflect the lessons learned from previous editions.peer-reviewe

Artificial intelligence to improve ischemia prediction in Rubidium Positron Emission Tomography—a validation study

Background: Patients are referred to functional coronary artery disease (CAD) testing based on their pre-test probability (PTP) to search for myocardial ischemia. The recommended prediction tools incorporate three variables (symptoms, age, sex) and are easy to use, but have a limited diagnostic accuracy. Hence, a substantial proportion of non-invasive functional tests reveal no myocardial ischemia, leading to unnecessary radiation exposure and costs. Therefore, preselection of patients before ischemia testing needs to be improved using a more predictive and personalised approach. Aims: Using multiple variables (symptoms, vitals, ECG, biomarkers), artificial intelligence–based tools can provide a detailed and individualised profile of each patient. This could improve PTP assessment and provide a more personalised diagnostic approach in the framework of predictive, preventive and personalised medicine (PPPM). Methods: Consecutive patients (n = 2417) referred for Rubidium-82 positron emission tomography were evaluated. PTP was calculated using the ESC 2013/2019 and ACC 2012/2021 guidelines, and a memetic pattern–based algorithm (MPA) was applied incorporating symptoms, vitals, ECG and biomarkers. Five PTP categories from very low to very high PTP were defined (i.e., < 5%, 5–15%, 15–50%, 50–85%, > 85%). Ischemia was defined as summed difference score (SDS) = 2. Results: Ischemia was present in 37.1%. The MPA model was most accurate to predict ischemia (AUC: 0.758, p < 0.001 compared to ESC 2013, 0.661; ESC 2019, 0.673; ACC 2012, 0.585; ACC 2021, 0.667). Using the < 5% threshold, the MPA’s sensitivity and negative predictive value to rule out ischemia were 99.1% and 96.4%, respectively. The model allocated patients more evenly across PTP categories, reduced the proportion of patients in the intermediate (15–85%) range by 29% (ACC 2012)–51% (ESC 2019), and was the only tool to correctly predict ischemia prevalence in the very low PTP category. Conclusion: The MPA model enhanced ischemia testing according to the PPPM framework: 1)The MPA model improved individual prediction of ischemia significantly and could safely exclude ischemia based on readily available variables without advanced testing (“predictive”).2)It reduced the proportion of patients in the intermediate PTP range. Therefore, it could be used as a gatekeeper to prevent patients from further unnecessary downstream testing, radiation exposure and costs (“preventive”).3)Consequently, the MPA model could transform ischemia testing towards a more personalised diagnostic algorithm (“personalised”)

The FAIR Guiding Principles for scientific data management and stewardship.

There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders-representing academia, industry, funding agencies, and scholarly publishers-have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community

Spray computers: Explorations in self-organization

We envision a future in which clouds of microcomputers can be sprayed in an environment to provide, by spontaneously networking with each other, an endlessly range of futuristic applications. However, beside the vision, spraying may also act as a powerful metaphor for a range of other scenarios that are already under formation, from ad-hoc networks of embedded and mobile devices to worldwide distributed computing. After having detailed the different spray computers scenarios and their applications, this paper discusses the issues related to the design and development of spray computer applications, issues which call for novel autonomic approaches exploiting self-organization as first-class tools. Finally, this paper presents the key research efforts being taken in the area and attempt at defining a rough research agenda