The Application of Internet-Based Sources for Public Health Surveillance (Infoveillance): Systematic Review.

BACKGROUND: Public health surveillance is based on the continuous and systematic collection, analysis, and interpretation of data. This informs the development of early warning systems to monitor epidemics and documents the impact of intervention measures. The introduction of digital data sources, and specifically sources available on the internet, has impacted the field of public health surveillance. New opportunities enabled by the underlying availability and scale of internet-based sources (IBSs) have paved the way for novel approaches for disease surveillance, exploration of health communities, and the study of epidemic dynamics. This field and approach is also known as infodemiology or infoveillance. OBJECTIVE: This review aimed to assess research findings regarding the application of IBSs for public health surveillance (infodemiology or infoveillance). To achieve this, we have presented a comprehensive systematic literature review with a focus on these sources and their limitations, the diseases targeted, and commonly applied methods. METHODS: A systematic literature review was conducted targeting publications between 2012 and 2018 that leveraged IBSs for public health surveillance, outbreak forecasting, disease characterization, diagnosis prediction, content analysis, and health-topic identification. The search results were filtered according to previously defined inclusion and exclusion criteria. RESULTS: Spanning a total of 162 publications, we determined infectious diseases to be the preferred case study (108/162, 66.7%). Of the eight categories of IBSs (search queries, social media, news, discussion forums, websites, web encyclopedia, and online obituaries), search queries and social media were applied in 95.1% (154/162) of the reviewed publications. We also identified limitations in representativeness and biased user age groups, as well as high susceptibility to media events by search queries, social media, and web encyclopedias. CONCLUSIONS: IBSs are a valuable proxy to study illnesses affecting the general population; however, it is important to characterize which diseases are best suited for the available sources; the literature shows that the level of engagement among online platforms can be a potential indicator. There is a necessity to understand the population's online behavior; in addition, the exploration of health information dissemination and its content is significantly unexplored. With this information, we can understand how the population communicates about illnesses online and, in the process, benefit public health.

Hydrothermal Synthesis and Properties of Nanostructured Silica Containing Lanthanide Type Ln-SiO2 (Ln = La, Ce, Pr, Nd, Eu, Gd, Dy, Yb, Lu).

The nanostructured lanthanide-silica materials of the Ln-SiO2 type (Ln = La, Ce, Pr, Nd, Eu, Gd, Dy, Yb, Lu) were synthesized by the hydrothermal method at 100 °C, using cetyltrimethylammonium as a structural template, silica gel and sodium silicate as a source of silicon, and lanthanide oxides, with Si/Ln molar ratio = 50. The resulting materials were calcined at 500 °C using nitrogen and air, and characterized by X-ray diffraction (XRD), Fourier-Transform infrared absorption spectroscopy, scanning electron microscopy, thermogravimetry (TG), surface area by the BET method and acidity measurements by n-butylamine adsorption. The XRD and chemical analysis indicated that the SiO2 presented a hexagonal structure and the incorporation of lanthanides in the structure changes the properties of the Ln-SiO2 materials. The heavier the lanthanide element, the higher the Si/Ln ratio. The TG curves showed that the decomposition of the structural template occurs in the materials at temperatures below 500 °C. The samples showed variations in specific surface area, mean pore diameter and silica wall thickness, depending on the nature of the lanthanide. The incorporation of different lanthanides in the silica generated acid sites of varied strength. The hydrothermal stability of the Ln-SiO2 materials evaluated at high temperatures, evidenced that the properties can be controlled for application in adsorption and catalysis processes.

Rethinking clinical study data: why we should respect analysis results as data.

The development and approval of new treatments generates large volumes of results, such as summaries of efficacy and safety. However, it is commonly overlooked that analyzing clinical study data also produces data in the form of results. For example, descriptive statistics and model predictions are data. Although integrating and putting findings into context is a cornerstone of scientific work, analysis results are often neglected as a data source. Results end up stored as "data products" such as PDF documents that are not machine readable or amenable to future analyses. We propose a solution to "calculate once, use many times" by combining analysis results standards with a common data model. This analysis results data model re-frames the target of analyses from static representations of the results (e.g., tables and figures) to a data model with applications in various contexts, including knowledge discovery. Further, we provide a working proof of concept detailing how to approach standardization and construct a schema to store and query analysis results.

The Validity of Google Trends Search Volumes for Behavioral Forecasting of National Suicide Rates in Ireland.

Annual suicide figures are critical in identifying trends and guiding research, yet challenges arising from significant lags in reporting can delay and complicate real-time interventions. In this paper, we utilized Google Trends search volumes for behavioral forecasting of national suicide rates in Ireland between 2004 and 2015. Official suicide rates are recorded by the Central Statistics Office in Ireland. While similar investigations using Google trends data have been carried out in other jurisdictions (e.g., United Kingdom, United Stated of America), such research had not yet been completed in Ireland. We compiled a collection of suicide- and depression-related search terms suggested by Google Trends and manually sourced from the literature. Monthly search rate terms at different lags were compared with suicide occurrences to determine the degree of correlation. Following two approaches based on vector autoregression and neural network autoregression, we achieved mean absolute error values between 4.14 and 9.61 when incorporating search query data, with the highest performance for the neural network approach. The application of this process to United Kingdom suicide and search query data showed similar results, supporting the benefit of Google Trends, neural network approach, and the applied search terms to forecast suicide risk increase. Overall, the combination of societal data and online behavior provide a good indication of societal risks; building on past research, our improvements led to robust models integrating search query and unemployment data for suicide risk forecasting in Ireland.

Disease mentions in airport and hospital geolocations expose dominance of news events for disease concerns.

BACKGROUND: In recent years, Twitter has been applied to monitor diseases through its facility to monitor users' comments and concerns in real-time. The analysis of tweets for disease mentions should reflect not only user specific concerns but also disease outbreaks. This requires the use of standard terminological resources and can be focused on selected geographic locations. In our study, we differentiate between hospital and airport locations to better distinguish disease outbreaks from background mentions of disease concerns. RESULTS: Our analysis covers all geolocated tweets over a 6 months time period, uses SNOMED-CT as a standard medical terminology, and explores language patterns (as well as MetaMap) to identify mentions of diseases in reference to the geolocation of tweets. Contrary to our expectation, hospital and airport geolocations are not suitable to collect significant portions of tweets concerned with disease outcomes. Overall, geolocated tweets exposed a large number of messages commenting on disease-related news articles. Furthermore, the geolocated messages exposed an over-representation of non-communicable diseases in contrast to infectious diseases. CONCLUSIONS: Our findings suggest that disease mentions on Twitter not only serve the purpose to share personal statements but also to share concerns about news articles. In particular, our assumption about the relevance of hospital and airport geolocations for an increased frequency of diseases mentions has not been met. To further address the linguistic cues, we propose the study of health forums to understand how a change in medium affects the language applied by the users. Finally, our research on the language use may provide essential clues to distinguish complementary trends in the use of language in Twitter when analysing health-related topics.

Carcinoma adenóide cístico da mama / Adenoid cystic carcinoma

Os autores apresentam um caso de carcinoma adenóide cístico da mama, o qual é um tumor maligno similar a tumores de glândulas salivares, brônquios, próstata e glândulas lacrimais. É caracterizado por uma combinaçäo de tecidos glandular, estromal e de membrana basal. De acordo com Haagensen, representa o mais raro e o menos maligno dos carcinomas de mama, correspondendo a aproximadamente 0,1 por cento dos tumores malignos de mama, e apresenta excelente prognóstico. O tratamento de escolha referido na literatura é a mastectomia. No presente caso, a paciente foi tratada por mastectomia e dissecçäo axilar. Dezesseis linfonodos foram retirados, todos sem neoplasia. No seguimento a paciente apresentou-se bem, sem sinais de recorrência.