Comparative Study of rK39 Leishmania Antigen for Serodiagnosis of Visceral Leishmaniasis: Systematic Review with Meta-Analysis

Visceral Leishmaniasis (VL) is a neglected tropical disease for which serodiagnostic tests are available, but not yet widely implemented in rural areas. The rK39 recombinant protein is derived from a kinesin-like protein of parasites belonging to the Leishmania donovani complex, and has been used in the last two decades for the serodiagnosis of VL. We present here a systematic review and meta-analysis of studies evaluating serologic assays (rK39 strip-test, rK39 ELISA, Direct Agglutination Test [DAT], Indirect Immunofluorescence test [IFAT] and ELISA with a promastigote antigen preparation [p-ELISA]) to diagnose VL to determine the accuracy of rK39 antigen in comparison to the use of other antigen preparations. Fourteen papers fulfilled the inclusion and exclusion selection criteria. The summarized sensitivity for the rK39-ELISA was 92% followed by IFAT 88% and p-ELISA 87%. The summarized specificity for the three diagnostic tests was 81%, 90%, and 77%. Studies comparing the rK39 strip test with DAT found a similar sensitivity (94%) and specificity (89%). However, the rK39 strip test was more specific than the IFAT and p-ELISA. In conclusion, we found the rK39 protein used either in a strip test or in an ELISA is a good choice for the serodiagnosis of VL

Status and Trends of Physical Activity Surveillance, Policy, and Research in 164 Countries: Findings From the Global Observatory for Physical Activity—GoPA! 2015 and 2020 Surveys

Background: Physical activity (PA) surveillance, policy, and research efforts need to be periodically appraised to gain insight into national and global capacities for PA promotion. The aim of this paper was to assess the status and trends in PA surveillance, policy, and research in 164 countries. Methods: We used data from the Global Observatory for Physical Activity (GoPA!) 2015 and 2020 surveys. Comprehensive searches were performed for each country to determine the level of development of their PA surveillance, policy, and research, and the findings were verified by the GoPA! Country Contacts. Trends were analyzed based on the data available for both survey years. Results: The global 5-year progress in all 3 indicators was modest, with most countries either improving or staying at the same level. PA surveillance, policy, and research improved or remained at a high level in 48.1%, 40.6%, and 42.1% of the countries, respectively. PA surveillance, policy, and research scores decreased or remained at a low level in 8.3%, 15.8%, and 28.6% of the countries, respectively. The highest capacity for PA promotion was found in Europe, the lowest in Africa and low- and lower-middle-income countries. Although a large percentage of the world’s population benefit from at least some PA policy, surveillance, and research efforts in their countries, 49.6 million people are without PA surveillance, 629.4 million people are without PA policy, and 108.7 million live in countries without any PA research output. A total of 6.3 billion people or 88.2% of the world’s population live in countries where PA promotion capacity should be significantly improved. Conclusion: Despite PA is essential for health, there are large inequalities between countries and world regions in their capacity to promote PA. Coordinated efforts are needed to reduce the inequalities and improve the global capacity for PA promotion

Artificial Intelligence in Protecting Smart Building’s Cloud Service Infrastructure from Cyberattacks

Gathering and utilizing stored data is gaining popularity and has become a crucial component of smart building infrastructure. The data collected can be stored, for example, into private, public, or hybrid cloud service infrastructure or distributed service by utilizing data platforms. The stored data can be used when implementing services, such as building automation (BAS). Cloud services, IoT sensors, and data platforms can face several kinds of cybersecurity attack vectors such as adversarial, AI-based, DoS/DDoS, insider attacks. If a perpetrator can penetrate the defenses of a data platform, she can cause significant harm to the system. For example, the perpetrator can disrupt a building’s automatic heating system or break the heating equipment by using a suitable attack vector for a data platform. This chapter focuses on examining possibilities to protect cloud storage or data platforms from incoming cyberattacks by using, for instance, artificial-intelligence-based tools or trained neural networks that can detect and prevent typical attack vectors.peerReviewe