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The COVID-19 pandemic has been accompanied by an : excess information, including false or misleading information, in digital and physical environments during an acute public health event. This infodemic is leading to confusion and risk-taking behaviors that can be harmful to health, as well as to mistrust in health authorities and public health responses. The World Health Organization (WHO) is working to develop tools to provide an evidence-based response to the infodemic, enabling prioritization of health response activities. In this work, we aimed to develop a practical, structured approach to identify narratives in public online conversations on social media platforms where concerns or confusion exist or where narratives are gaining traction, thus providing actionable data to help the WHO prioritize its response efforts to address the COVID-19 infodemic. We developed a taxonomy to filter global public conversations in English and French related to COVID-19 on social media into 5 categories with 35 subcategories. The taxonomy and its implementation were validated for retrieval precision and recall, and they were reviewed and adapted as language about the pandemic in online conversations changed over time. The aggregated data for each subcategory were analyzed on a weekly basis by volume, velocity, and presence of questions to detect signals of information voids with potential for confusion or where mis- or disinformation may thrive. A human analyst reviewed and identified potential information voids and sources of confusion, and quantitative data were used to provide insights on emerging narratives, influencers, and public reactions to COVID-19-related topics. A COVID-19 public health social listening taxonomy was developed, validated, and applied to filter relevant content for more focused analysis. A weekly analysis of public online conversations since March 23, 2020, enabled quantification of shifting interests in public health-related topics concerning the pandemic, and the analysis demonstrated recurring voids of verified health information. This approach therefore focuses on the detection of infodemic signals to generate actionable insights to rapidly inform decision-making for a more targeted and adaptive response, including risk communication. This approach has been successfully applied to identify and analyze infodemic signals, particularly information voids, to inform the COVID-19 pandemic response. More broadly, the results have demonstrated the importance of ongoing monitoring and analysis of public online conversations, as information voids frequently recur and narratives shift over time. The approach is being piloted in individual countries and WHO regions to generate localized insights and actions; meanwhile, a pilot of an artificial intelligence-based social listening platform is using this taxonomy to aggregate and compare online conversations across 20 countries. Beyond the COVID-19 pandemic, the taxonomy and methodology may be adapted for fast deployment in future public health events, and they could form the basis of a routine social listening program for health preparedness and response planning.

There is an urgent need to raise public awareness of antimicrobial resistance in the region, say Natasha Godinho and colleagues

To conduct assessments of Ebola virus disease preparedness in countries of the World Health Organization (WHO) South-East Asia Region. Nine of 11 countries in the region agreed to be assessed. During February to November 2015 a joint team from WHO and ministries of health conducted 4-5 day missions to Bangladesh, Bhutan, Indonesia, Maldives, Myanmar, Nepal, Sri Lanka, Thailand and Timor-Leste. We collected information through guided discussions with senior technical leaders and visits to hospitals, laboratories and airports. We assessed each country's Ebola virus disease preparedness on 41 tasks under nine key components adapted from the WHO Ebola preparedness checklist of January 2015. Political commitment to Ebola preparedness was high in all countries. Planning was most advanced for components that had been previously planned or tested for influenza pandemics: multilevel and multisectoral coordination; multidisciplinary rapid response teams; public communication and social mobilization; drills in international airports; and training on personal protective equipment. Major vulnerabilities included inadequate risk assessment and risk communication; gaps in data management and analysis for event surveillance; and limited capacity in molecular diagnostic techniques. Many countries had limited planning for a surge of Ebola cases. Other tasks needing improvement included: advice to inbound travellers; adequate isolation rooms; appropriate infection control practices; triage systems in hospitals; laboratory diagnostic capacity; contact tracing; and danger pay to staff to ensure continuity of care. Joint assessment and feedback about the functionality of Ebola virus preparedness systems help countries strengthen their core capacities to meet the International Health Regulations.

Metodos Nueve de once paises de la region aceptaron ser evaluados. De febrero a noviembre de 2015, un equipo conjunto de la OMS y los ministerios de sanidad llevaron a cabo misiones de entre 4 y 5 dias en Bangladesh, Butan, Indonesia, Maldivas, Myanmar, Nepal, Sri Lanka, Tailandia y Timor-Leste. Se recopilo informacion a traves de conversaciones dirigidas con jefes tecnicos y visitas a hospitales, laboratorios y aeropuertos. Se evaluo la preparacion ante el virus del Ebola de cada pais en 41 tareas con 9 componentes clave adaptados de la lista de preparacion ante el ebola de la OMS de enero de 2015.

The mammalian microtubule associated proteins (MAPs) tau, MAP2c and MAP4 were subcloned and expressed in the fission yeast Schizosaccharomyces pombe using the thiamine repressible pREPl vector. Tau, MAP2c and MAP4 have similar C-terminal microtubule binding domains, but unique N-terminal projection domains. At the start of this study, there were no known MAPs in S. pombe and therefore this appeared to be a unique in vivo system to both study the roles of the three mammalian MAPs and to further define the function of microtubules. All three MAPs inhibited growth of wild type cells at 36°C over a range of temperatures. However each MAP produced distinct phenotypes in fission yeast, indicating that their effect was specific for that MAP. Tau expression resulted in a weak phenotype of long, multiseptate or branched cells. The MAP2c-induced phenotype was stronger, and resulted in long cells with bulbous ends, whilst MAP4 expression produced bent or hammer shaped cells. The MAPs tau and MAP2c accelerated and slowed entry into mitosis, respectively, of G2-arrested cdc25.211 cells. Expression of tau and MAP2c in teal cells (which plays a role in directing the cell to grow along a perfectly opposed longitudinal axis) and tea2 cells (which codes for a kinesin) result in distorted shapes and loss of the original phenotype. Immunofluorescence studies showed that each MAP had a unique effect not only on the cell phenotype but also on microtubule organisation of the cell. Tau caused microtubule bundling and displacement towards the cell periphery, MAP2c resulted in short microtubules around the nucleus, whilst MAP4 caused total depolymerisation of interphase microtubules. Both tau and MAP2c appeared to bind to microtubules, but MAP4 was seen distributed throughout the cell. Similarly, MAP2c caused the formation of short microtubules in teal cells, but tau had very little effect. tea2 control cells have short microtubules, and tau expression resulted in even shorter microtubules near the nucleus. MAP2c expression in tea2, however, resulted in microtubule depolymerisation, and assymetrical distribution towards one end of the cell. Tau and MAP2c also appeared to rescue the combined sensitivity of fission yeast to the cold and the microtubule depolymerisation agent, thiabendazole (TBZ). This observation was used as a strategy to isolate possible S. pombe MAPs by expressing an S. pombe cDNA library subcloned in pREP. Eight clones were isolated, and sequence analysis of two of those clones revealed that they coded for fatty acid synthetase (lsd1+) and the ribosomal protein L19.