Wearable sensing, big data technology for cardiovascular healthcare: current status and future prospective / 中华医学杂志(英文版)

Wearable technology, which can continuously and remotely monitor physiological and behavioral parameters by incorporated into clothing or worn as an accessory, introduces a new era for ubiquitous health care. With big data technology, wearable data can be analyzed to help long-term cardiovascular care. This review summarizes the recent developments of wearable technology related to cardiovascular care, highlighting the most common wearable devices and their accuracy. We also examined the application of these devices in cardiovascular healthcare, such as the early detection of arrhythmias, measuring blood pressure, and detecting prevalent diabetes. We provide an overview of the challenges that hinder the widespread application of wearable devices, such as inadequate device accuracy, data redundancy, concerns associated with data security, and lack of meaningful criteria, and offer potential solutions. Finally, the future research direction for cardiovascular care using wearable devices is discussed.

Analysis of big data characteristics of allergic rhinitis patients in Beijing City from 2016 to 2021 / 中华预防医学杂志

To explore the characteristics of big data of patients with allergic rhinitis, including the time, population and spatial distribution of allergic rhinitis in Beijing from 2016 to 2021, so as to provide reference for the prevention and treatment of this disease. Descriptive epidemiological methods were used to analyze the distribution (including gender, age and location)and trend of allergic rhinitis patients in 30 pilot hospitals from January 2016 to December 2021, T test and Kruskal-Wallis rank sum test were used to test the statistical differences. The results showed that the number of patients with allergic rhinitis in 30 hospitals increased year by year from 2016 to 2019, with an increase of 97.9%. In 2020, the number of patients decreased. In 2021, the number of visits returned to the pre-epidemic level (461 332); The number of patients with allergic rhinitis was the highest in September, with a seasonal index of 177.6%, while the lowest number was in February, accounting for only 47.2%; a significant difference was observed in the number of patients in different age groups(H=45 319.48, P<0.05), and patients under 15 years old accounted for the highest proportion(819 284 visits); There were significant differences between patients of different genders in the 45-59 year old group (t=-4.26, P<0.05).There were relatively more patients with allergic rhinitis in Dongcheng District(31.1%) than in Huairou District and Miyun District (0.4%). In conclusion, since 2016, the number of patients increased significantly, with a varied trend in different seasons. Most patients were children. There were more patients in the central urban area than in the outer suburbs.

Current situation and trend of medical laboratory results homogeneity management / 中华预防医学杂志

Medical test results are indispensable and important tools in diagnosis and treatment services. It is necessary to promote the homogenization of test results first, because homogenization is the basis for mutual recognition of test results. Mutual recognition of medical test results can help share resources among medical institutions, provide more reliable test results for early prevention, screening and treatment of diseases, and reduce repeated tests, thus improving people's medical experience. In recent years, with the deepening of medical system reform and the promotion of graded diagnosis and treatment, governments have continuously introduced policies of mutual recognition of test results around country. However, homogenization is a prerequisite for mutual recognition of test results, with the emergence of intelligent medicine in the era of internet big data, opportunities and challenges coexist in the development of homogeneity management. In the future, the homogeneity of medical test results will present a trend of digitalization, automation, informatization and intelligence.

Analysis of big data characteristics of allergic rhinitis patients in Beijing City from 2016 to 2021 / 中华预防医学杂志

To explore the characteristics of big data of patients with allergic rhinitis, including the time, population and spatial distribution of allergic rhinitis in Beijing from 2016 to 2021, so as to provide reference for the prevention and treatment of this disease. Descriptive epidemiological methods were used to analyze the distribution (including gender, age and location)and trend of allergic rhinitis patients in 30 pilot hospitals from January 2016 to December 2021, T test and Kruskal-Wallis rank sum test were used to test the statistical differences. The results showed that the number of patients with allergic rhinitis in 30 hospitals increased year by year from 2016 to 2019, with an increase of 97.9%. In 2020, the number of patients decreased. In 2021, the number of visits returned to the pre-epidemic level (461 332); The number of patients with allergic rhinitis was the highest in September, with a seasonal index of 177.6%, while the lowest number was in February, accounting for only 47.2%; a significant difference was observed in the number of patients in different age groups(H=45 319.48, P<0.05), and patients under 15 years old accounted for the highest proportion(819 284 visits); There were significant differences between patients of different genders in the 45-59 year old group (t=-4.26, P<0.05).There were relatively more patients with allergic rhinitis in Dongcheng District(31.1%) than in Huairou District and Miyun District (0.4%). In conclusion, since 2016, the number of patients increased significantly, with a varied trend in different seasons. Most patients were children. There were more patients in the central urban area than in the outer suburbs.

Current situation and trend of medical laboratory results homogeneity management / 中华预防医学杂志

Medical test results are indispensable and important tools in diagnosis and treatment services. It is necessary to promote the homogenization of test results first, because homogenization is the basis for mutual recognition of test results. Mutual recognition of medical test results can help share resources among medical institutions, provide more reliable test results for early prevention, screening and treatment of diseases, and reduce repeated tests, thus improving people's medical experience. In recent years, with the deepening of medical system reform and the promotion of graded diagnosis and treatment, governments have continuously introduced policies of mutual recognition of test results around country. However, homogenization is a prerequisite for mutual recognition of test results, with the emergence of intelligent medicine in the era of internet big data, opportunities and challenges coexist in the development of homogeneity management. In the future, the homogeneity of medical test results will present a trend of digitalization, automation, informatization and intelligence.

Research Progress of Metabolomics Techniques Combined with Machine Learning Algorithm in Wound Age Estimation / 法医学杂志

Wound age estimation is the core content in the practice of forensic medicine. Accurate estimation of wound age is a scientific question that needs to be urgently solved by forensic scientists at home and abroad. Metabolomics techniques can effectively detect endogenous metabolites produced by internal or external stimulating factors and describe the dynamic changes of metabolites in vivo. It has the advantages of strong operability, high detection efficiency and accurate quantitative results. Machine learning algorithm has special advantages in processing high-dimensional data sets, which can effectively mine biological information and truly reflect the physiological, disease or injury state of the body. It is a new technical means for efficiently processing high-throughput big data. This paper reviews the status and advantages of metabolomic techniques combined with machine learning algorithm in the research of wound age estimation, and provides new ideas for this research.

Comparative study of medical common data models for FAIR data sharing / 中华流行病学杂志

The common data model (CDM) is an important tool to facilitate the standardized integration of multi-source heterogeneous healthcare big data, enhance the consistency of data semantic understanding, and promote multi-party collaborative analysis. The data collections standardized by CDM can provide powerful support for observational studies, such as large-scale population cohort study. This paper provides an in-depth comparative analysis of the data storage structure, term mapping pattern, and auxiliary tools development of the three international typical CDMs, then analyzes the advantages and limitations of each CDM and summarizes the challenges and opportunities faced in the CDM application in China. It is expected that exploring the advanced technical concepts and practical patterns of foreign countries in data management and sharing will provide references for promoting FAIR (findable, accessible, interoperable, reusable) construction of healthcare big data in China and solving the current practical problems, such as the poor quality of data resources, the low degree of semantization, and the inabilities of data sharing and reuse.

Application progress on data-driven technologies in intelligent manufacturing of traditional Chinese medicine extraction / 中国中药杂志

The application of new-generation information technologies such as big data, the internet of things(IoT), and cloud computing in the traditional Chinese medicine(TCM)manufacturing industry is gradually deepening, driving the intelligent transformation and upgrading of the TCM industry. At the current stage, there are challenges in understanding the extraction process and its mechanisms in TCM. Online detection technology faces difficulties in making breakthroughs, and data throughout the entire production process is scattered, lacking valuable mining and utilization, which significantly hinders the intelligent upgrading of the TCM industry. Applying data-driven technologies in the process of TCM extraction can enhance the understanding of the extraction process, achieve precise control, and effectively improve the quality of TCM products. This article analyzed the technological bottlenecks in the production process of TCM extraction, summarized commonly used data-driven algorithms in the research and production control of extraction processes, and reviewed the progress in the application of data-driven technologies in the following five aspects: mechanism analysis of the extraction process, process development and optimization, online detection, process control, and production management. This article is expected to provide references for optimizing the extraction process and intelligent production of TCM.

Construction of integrated platform for emergency clinical scientific research based on big data / 中华危重病急救医学

OBJECTIVE@#To explore clinical rules based on the big data of the emergency department of the Second Affiliated Hospital of Guangzhou Medical University, and to establish an integrated platform for clinical research in emergency, which was finally applied to clinical practice.@*METHODS@#Based on the hospital information system (HIS), laboratory information system (LIS), emergency specialty system, picture archiving and communication systems (PACS) and electronic medical record system of the Second Affiliated Hospital of Guangzhou Medical University, the structural and unstructured information of patients in the emergency department from March 2019 to April 2022 was extracted. By means of extraction and fusion, normalization and desensitization quality control, the database was established. In addition, data were extracted from the database for adult patients with pre screening triage level III and below who underwent emergency visits from March 2019 to April 2022, such as demographic characteristics, vital signs during pre screening triage, diagnosis and treatment characteristics, diagnosis and grading, time indicators, and outcome indicators, independent risk factors for poor prognosis in patients were analyzed.@*RESULTS@#(1) The data of 338 681 patients in the emergency department of the Second Affiliated Hospital of Guangzhou Medical University from March 2019 to April 2022 were extracted, including 15 modules, such as demographic information, triage information, visit information, green pass and rescue information, diagnosis information, medical record information, laboratory examination overview, laboratory information, examination information, microbiological information, medication information, treatment information, hospitalization information, chest pain management and stroke management. The database ensured data visualization and operability. (2) Total 140 868 patients with pre-examination and triage level III and below were recruited from the emergency department database. The gender, age, type of admission to the hospital, pulse, blood pressure, Glasgow coma scale (GCS) and other indicators of the patients were included. Taking emergency admission to operating room, emergency admission to intervention room, emergency admission to intensive care unit (ICU) or emergency death as poor prognosis, the poor prognosis prediction model for patients with pre-examination and triage level III and below was constructed. The receiver operator characteristic curve and forest map results showed that the model had good predictive efficiency and could be used in clinical practice to reduce the risk of insufficient emergency pre-examination and triage.@*CONCLUSIONS@#The establishment of high-quality clinical database based on big data in emergency department is conducive to mining the clinical value of big data, assisting clinical decision-making, and improving the quality of clinical diagnosis and treatment.

As startups no contexto da organização capitalista financeira e as subjetividades empreendedoras / Startups in the context of financial capitalist organization and entrepreneurial subjectivities / Startups en el contexto de la organización capitalista financiera y subjetividades emprendedoras

As startups são empresas que apresentam modelos de negócios marcados pela inovação, rapidez, flexibilidade e alta capacidade de adaptação aos mercados. Atuando em diferentes setores socioeconômicos, elas prometem criar e transformar produtos e serviços. A emergência e disseminação dessas empresas ocorrem em um momento histórico de mudanças iniciadas a partir de 1970 e marcadas pelas crises geradas com o esgotamento do paradigma da sociedade urbano industrial. No Brasil, o número desse modelo de negócio apresentou uma expansão expressiva, alcançando a marca de 13.374 nos últimos cinco anos. Atento a esse cenário, o objetivo desta pesquisa consistiu em compreender como sujeitos, grupos e instituições atribuem sentidos à experiência de trabalho nas chamadas startups. Na parte teórica, as condições sociais e econômicas que possibilitaram a emergência e disseminação das startups são analisadas em uma perspectiva crítica. A parte empírica, por sua vez, apresenta depoimentos de empreendedores relatando o contexto geral de atuação nas startups. Ao final deste artigo, conclui-se que há uma instrumentalização capitalística de componentes subjetivos específicos selecionados e colocados em circulação para fortalecer o modo de produção capitalista financeirizado.(AU)

Startups are companies that have business models characterized by innovation, speed, flexibility, and a high capacity to adapt to markets. Operating in different socioeconomic sectors, they promise to create and transform products and services. The emergence and dissemination of these companies occur at a historical moment of changes that began from 1970 and are marked by the crises generated by the exhaustion of the paradigm of industrial urban society. In Brazil, the number of businesses in this model showed a significant expansion, reaching 13,374 companies in the last five years. Attentive to this scenario, the objective of this research was to understand how subjects, groups, and institutions attribute meanings to the work experience in so-called startups. In the theoretical part, the social and economic conditions that enabled the emergence and dissemination of startups are analyzed in a critical perspective. The empirical part presents entrepreneurs reporting the general context of action in startups. At the end of this article, it is concluded that there is a capitalistic instrumentalization of specific subjective components that are selected and put into circulation to strengthen the financed capitalist production.(AU)

Las startups son empresas que tienen modelos de negocio marcados por la innovación, la velocidad, la flexibilidad y una alta capacidad de adaptación a los mercados. Desde diferentes sectores socioeconómicos, las startups prometen crear y transformar productos y servicios. La aparición y difusión de estas empresas se produce en un momento histórico de cambios que comenzó a partir de 1970 y que está marcado por crisis generadas por el agotamiento del paradigma de la sociedad urbana industrial. En Brasil, estas empresas se expandieron significativamente alcanzando la marca de 13.374 empresas en los últimos cinco años. En este escenario, el objetivo de esta investigación fue entender cómo los sujetos, grupos e instituciones atribuyen significados a la experiencia laboral en las startups. En la parte teórica, se analizan las condiciones sociales y económicas que permitieron el surgimiento y la difusión de las startups en una perspectiva crítica. La parte empírica presenta testimonios de emprendedores que informan sobre el trabajo en startups. La investigación concluye que hay una instrumentalización capitalista de componentes subjetivos específicos que se seleccionan y ponen en circulación para fortalecer el modo de producción capitalista financiero.(AU)

Artificial intelligence in health and bioethical implications: a systematic review / Inteligencia artificial en salud y sus implicaciones bioéticas: una revisión sistemática / Inteligência artificial em saúde e implicações bioéticas: uma revisão sistemática

Abstract The presence of artificial intelligence in healthcare is growing, helping in diagnosis and decision making. However, its application raises doubts, mostly related to ethics. This study aimed to identify its uses in health and its bioethical implications from a systematic literature review using the PRISMA guidelines. The ScienceDirect and Scopus databases were searched, using the descriptors "artificial intelligence," "bioethics" and "health." Works in English, published between 2017 and 2021 were considered, resulting in 102 articles found and, after applying the established criteria, 11 were selected. The studies reported on the bioethical principles of beneficence, non-maleficence, autonomy and justice, adding an element, explainability. Relationships were found between artificial intelligence in health and unpredictability, predictability, trust, physicians' role, systems development, privacy, data security, financial and social aspects. Developers, healthcare professionals and patients must maximize the benefits and limit the risks of tools that use this technology.

Resumen El uso de la inteligencia artificial en salud va en aumento por facilitar el diagnóstico y la toma de decisiones, pero sus implicaciones plantean dudas relacionadas con la ética. Esta revisión sistemática desde las directrices Prisma identificó los usos de la inteligencia artificial en salud y sus implicaciones bioéticas. Las búsquedas se realizaron en Science Direct y Scopus utilizando los descriptores "artificial intelligence", "bioethics" y "health". De los trabajos en inglés publicados entre 2017 y 2021, se obtuvo 102 artículos. Aplicados los criterios, quedaron 11. Los estudios abordaron los principios bioéticos de beneficencia, no maleficencia, autonomía y justicia, añadiendo el elemento explicabilidad. La inteligencia artificial se correlacionó con la imprevisibilidad, previsibilidad, confianza, papel de los médicos, desarrollo de sistemas, privacidad, seguridad de los datos y aspectos financieros y sociales. Los desarrolladores, los profesionales sanitarios y los pacientes deben maximizar los beneficios y limitar los riesgos que involucra esta tecnología.

Resumo A presença de inteligência artificial na saúde vem crescendo, ajudando em diagnósticos e tomadas de decisão, mas suas implicações geram dúvidas relacionadas à ética. Esta revisão sistemática, baseada nas diretrizes Prisma, identificou os usos de inteligência artificial na saúde e suas implicações bioéticas. Foi realizada busca nas bases de dados Science Direct e Scopus usando os descritores "artificial intelligence", "bioethics" e "health". Trabalhos em inglês, publicados entre 2017 e 2021 foram considerados, resultando em 102 artigos. Após aplicação dos critérios estabelecidos, 11 foram selecionados. Os estudos discutiram os princípios bioéticos da beneficência, não maleficência, autonomia e justiça, adicionando o elemento explicabilidade. Inteligência artificial mostrou correlação com imprevisibilidade, previsibilidade, confiança, papel do médico, desenvolvimento de sistemas, privacidade, segurança de dados, e aspectos sociais e financeiros. Desenvolvedores, profissionais da saúde e pacientes devem maximizar os benefícios e limitar os riscos das ferramentas que usam essa tecnologia.

Inteligencia artificial, una mirada al futuro. Perspectiva histórica y evolución de la Ortodoncia / Artificial intelligence, a look to the future. Historical perspective and evolution of orthodontics

A lo largo del tiempo, la Ortodoncia fue pasando por distintos periodos con características propias y bien definidas, hasta llegar a la época actual, en la que el descubrimiento de inteligencia artificial (IA), que combina la ciencia informática, algoritmos y recopilación de miles de datos, logra simplificar nuestro trabajo y nos conduce a un fin muy claro, que es un tratamiento personalizado.

Orthodontics, through the ages, went through different periods with its own welldefined characteristics, until we reach the present time, where the discovery of artificial intelligence (AI), which combines computer science, algorithms and the collection of thousands of data, manage to simplify our work, and lead us to a very clear goal, which is a personalized treatment.

A pesquisa científica na era do Big data: cinco maneiras que mostram como o Big data prejudica a ciência, e como podemos salvá-la / The scientific research in the age of Big data: five ways that show how the Big data harms the science, and how we can save it / La investigación científica en la era del Big data: cinco maneras que muestran como el Big data perjudica la ciencia, y como la salvar

O livro A pesquisa científica na era do Big data: cinco maneiras que mostram como o Big data prejudica a ciência, e como podemos salvá-la, de Sabina Leonelli, publicado pela Editora Fiocruz em 2022, explora em seus capítulos as definições do termo Big data e os seus impactos negativos na pesquisa científica. Em seguida, a autora revela uma nova abordagem epistemológica para o Big data e, por fim, apresenta um conjunto de propostas para a pesquisa científica. A revisão e atualização de definições, tanto quanto as importantes reflexões e os questionamentos por um uso consciente do Big data na pesquisa científica fazem com que a obra adicione importantes contribuições à biblioteca do pesquisador de informação e comunicação em saúde

The book titled A pesquisa científica na era do Big Data: cinco maneiras que mostram como o Big Data prejudica a ciência, e como podemos salvá-la [The scientific research in the age of Big Data: five ways that show how the Big Data harms the science, and how we can save it], by Sabina Leonelli, published in 2002, by Editora Fiocruz, explores in its chapters the definitions of Big Data and its negative impacts on scientific research. Then, the author reveals a new epistemological approach to Big data and finally she presents a set of proposals for developing a good scientific research. The literature review and updating of definitions as well as the important reflections and questions for a conscious use of Big data in scientific research make the work an important contribution to the researcher's library of the information and communication about health.

El libro denominado A pesquisa científica na era do Big data: cinco maneiras que mostram como o Big data prejudica a ciência, e como podemos salvá-la [La investigación científica en la era del Big data: cinco maneras que muestran como el Big data perjudica la ciencia, y como la salvar], de Sabina Leonelli, publicado en 2002, por la Editora Fiocruz, explora em sus capítulos las definiciones de Big data y sus impactos negativos en la investigación científica. A continuación, la autora revela un nuevo enfoque epistemológico del Big data y, al fin y al cabo, presenta un conjunto de propuestas para desarrollar una investigación científica de cualidad. La revisión de literatura y la actualización de las definiciones, así como las importantes reflexiones y discusiones para un uso consciente del Big data en la investigación científica, hacen de la obra un aporte importante a la biblioteca del investigador de la información y la comunicación acerca de la salud

Impactos da insuficiência cardíaca no sistema de saúde e previdenciário brasileiro: qual é o custo da doença? / Impacts of heart failure on the Brazilian health and pension system: what is the cost of the disease?

Objetivo: Estimar os principais custos indiretos da insuficiência cardíaca (IC) na população brasileira, sobre o sistema de saúde, o custo previdenciário e o quanto se perde em produtividade pelas complicações da doença. Métodos: Estudo ecológico desenvolvido com dados secundários, para a série histórica de 2018 a 2021, minerados do Departamento de Informática do Sistema Único de Saúde (Datasus), do Instituto Brasileiro de Geografia e Estatística (IBGE), e indicadores previdenciários coletados da Previdência Social e Instituto Nacional do Seguro Social (INSS). Resultados: Foram registrados 77.290 óbitos por IC no Brasil para o período, distribuídos uniformemente em relação ao sexo. A taxa de mortalidade foi diversificada entre as regiões brasileiras, com ênfase para Sudeste e Nordeste. As projeções indicam um gasto total de mais de R$ 1 bilhão com hospitalizações, com custo médio hospitalar de R$ 1.725,27 por pessoa. O custo médio por internação ultrapassou os R$ 2 bilhões de reais. Aproximadamente 3% das despesas federais são destinadas a pagamentos de benefícios relacionados a IC. Do total de afastamentos, 65% correspondem a homens e 35%, a mulheres, com custos que podem chegar a R$ 6 bilhões perdidos por ano. Conclusão: Os resultados sugerem um aumento do afastamento de portadores de IC da força de trabalho, o que acarreta maiores dispêndios para o sistema de saúde e pagamentos de benefícios previdenciários, como auxílio-doença e aposentadoria por incapacidade de longa duração. Este é o primeiro estudo que estima e correlaciona os dados socioepidemiológicos e os custos de saúde e previdenciários da IC no Brasil.

Objective: To estimate the main indirect costs of heart failure (HF) in the Brazilian population, on the health system, social security cost, and how much is lost in productivity due to the complications of the disease. Methods: Ecological study developed with secondary data, for the historical series from 2018 to 2021, mined from the Department of Informatics of the Unified Health System (Datasus), from the Brazilian Institute of Geography and Statistics (IBGE), and social security indicators collected from Social Security and the National Social Security Institute (INSS). Results: There were 77,290 deaths from HF in Brazil for the period, evenly distributed according to sex. The mortality rate was diversified among Brazilian regions, with emphasis on the Southeast and Northeast. Projections indicate a total expenditure of more than BRL 1 billion with hospitalizations, with an average hospital cost of BRL 1,725.27 per person. The average cost per hospitalization exceeded BRL 2 billion. Approximately 3% of federal expenditures are earmarked for IC benefit payments. Of the total number of absences, 65% correspond to men and 35% to women, with costs that can reach R$ 6 billion lost per year. Conclusion: The results suggest an increase in the removal of HF patients from the workforce, which leads to higher expenditures for the health system and payments of social security benefits, such as sick pay and retirement due to long-term disability. This is the first study that estimates and correlates socio-epidemiological data, health and social security costs of HF in Brazil.

Plataformização e o uso da informação para a criação de estímulos de consumo / Platformization and the use of information for the creation of consumption response / La plataformización y el uso de la información para crear estímulos al consumidor

Resumo Vivemos numa sociedade em que a existência está diretamente associada a visibilidade dos indivíduos. As construções narrativas que validam este processo trabalham com imagens e vídeos que projetam e constroem as nossas vivências em múltiplas plataformas digitais. Através delas é possível mapear boa parte de nossas ações e interações. Esses dados são valiosos indicativos de nosso comportamento social e emocional diante de variados temas e situações. As plataformas digitais utilizam essas informações na dinâmica do capitalismo de dados, extraindo valor a partir de mecanismos automatizados de coleta e operados por sujeitos algorítmicos. Por meio da organização de Big Data novos padrões de consumo são estimulados através de entregas customizadas para determinados grupos de pessoas interconectadas. Este estudo mostra as características deste processo operado em ambientes heterotópicos em que o espaço-tempo é formatado pela lógica das plataformas. Além disso, será apresentado um panorama sobre como esse artifício se tornou possível por causa da necessidade de relevância em que a autonomia do indivíduo nas redes é proporcional a sua submissão as regras de vigilância e exploração econômica. Através desta premissa este estudo apresenta dados recentes sobre a relação de confiança dos brasileiros nestas plataformas digitais que, paradoxalmente, ocupam lugar de destaque como fonte de informação primordial para boa parte da população no Brasil.

Abstract We live in a society in which the existence is directly associated with the visibility of the individuals. The narrative constructions that validate these processes work with images and videos that project and build our experiences on multiple digital platforms. Through them it is possible to map part of our actions and interactions. These data are valuable indicators of our social and emotional behavior in the face of many themes and situations. Digital platforms use this information in the dynamic of data capitalism, extracting value from automated collection mechanisms operated by algorithmic subjects. Through the Big Data organization, consumption patterns are stimulated through customized deliveries for certain groups of interconnected people. This study shows the characteristics of this process operated in heterotopic environments in which space-time is operated by the platform logic. In addition to that, an overview will be presented on how this artifice became possible because of the need for relevance in which the individuals autonomy in networks is proportional to their submission to the rules of surveillance and economic exploitation. Through this premise, this study also presents recent data on the trust relationship of Brazilians in these digital platforms that, paradoxically, occupy a prominent place as a primary source of information for a large part of the population in Brazil.

Resumen Vivimos en una sociedad en la que la existencia está directamente asociada a la visibilidad de los individuos. Las construcciones narrativas que validan este proceso funcionan con imágenes y vídeos que proyectan y construyen nuestras experiencias en múltiples plataformas digitales. A través de ellos es posible mapear la mayoría de nuestras acciones e interacciones. Estos datos son valiosos indicadores de nuestro comportamiento social y emocional ante diversos temas y situaciones. Las plataformas digitales utilizan esta información en la dinámica del capitalismo de datos, extrayendo valor de los mecanismos de recogida automatizada operados por sujetos algorítmicos. A través de la organización de Big Data se estimulan nuevos patrones de consumo mediante entregas personalizadas a determinados grupos de personas interconectadas. Este estudio muestra las características de este proceso operado en entornos heterotópicos en los que el espacio-tiempo está formateado por la lógica de las plataformas. Además, se presentará una visión general de cómo este artificio fue posible debido a la necesidad de relevancia en la que la autonomía del individuo en las redes es proporcional a su sometimiento a las reglas de vigilancia y explotación económica. A través de esta premisa, este estudio presenta datos recientes sobre la relación de confianza de los brasileños en estas plataformas digitales que, paradójicamente, ocupan un lugar destacado como fuente primaria de información para gran parte de la población en Brasil.

Epidemiological analysis of lower limb revascularization for peripheral arterial disease over 12 years on the public healthcare system in Brazil / Análise epidemiológica da revascularização de membros inferiores para doença arterial periférica em 12 anos no sistema público de saúde do Brasil

Abstract Background Worldwide, peripheral arterial disease (PAD) is a disorder with high morbidity, affecting more than 200 million people. Objectives Our objective was to analyze surgical treatment for PAD provided on the Brazilian Public Healthcare System over 12 years using publicly available data. Methods The study was conducted with analysis of data available on the Brazilian Health Ministry's database platform, assessing distributions of procedures and techniques over the years and their associated mortality and costs. Results A total of 129,424 procedures were analyzed (performed either for claudication or critical ischemia, proportion unknown). The vast majority of procedures were endovascular (65.49%) and this disproportion exhibited a rising trend (p<0.001). There were 3,306 in-hospital deaths (mortality of 2.55%), with lower mortality in the endovascular group (1.2% vs. 5.0%, p=0.008). The overall governmental expenditure on these procedures was U$ 238,010,096.51, and endovascular procedures were on average significantly more expensive than open surgery (U$ 1,932.27 vs. U$ 1,517.32; p=0.016). Conclusions Lower limb revascularizations were performed on the Brazilian Public Healthcare System with gradually increasing frequency from 2008 to 2019. Endovascular procedures were vastly more common and were associated with lower in-hospital mortality rates, but higher procedure costs.

Resumo Contexto A doença arterial periférica (DAP) é uma doença com alta morbidade global, afetando mais de 200 milhões de pessoas. Objetivos Neste estudo, analisamos o tratamento cirúrgico para DAP no sistema público de saúde do Brasil no período de 12 anos, com base em dados publicamente disponíveis. Métodos O estudo foi conduzido a partir da análise de dados disponíveis na plataforma do Departamento de Informática do Sistema Único de Saúde (DATASUS), do Ministério da Saúde, avaliando a distribuição da técnica cirúrgica utilizada, a mortalidade e o custo ao longo dos anos. Resultados Um total de 129.424 procedimentos foram analisados (para claudicantes e isquemia crítica, em proporção desconhecida). A maiora dos procedimentos foi via endovascular (65,49%), com tendência de aumento nessa desproporção (p < 0,001). Houve 3.306 mortes intra-hospitalares (mortalidade de 2,55%) com menor mortalidade no grupo endovascular (1,2% vs. 5,0%; p = 0,008). O investimento governamental total para esses procedimentos foi de US$ 238.010.096,51, e os procedimentos endovasculares foram significativamente mais caros que a cirurgia aberta convencional (US$ 1.932,27 vs. US$ 1.517,32; p = 0,016). Conclusões No sistema público de saúde brasileiro, as revascularizações de membros inferiores ocorreram com frequência crescente entre 2008 e 2019. Os procedimentos endovasculares foram mais comuns e relacionados a menor mortalidade intra-hospitalar, mas a maiores custos.

Correlation analysis of smell and taste loss with COVID-19 outbreak trend based on big data of internet / 中华耳鼻咽喉头颈外科杂志

Objective: To analyze the correlation between loss of smell/taste and the number of real confirmed cases of coronavirus disease 2019 (COVID-19) worldwide based on Google Trends data, and to explore the guiding role of smell/taste loss for the COVID-19 prevention and control. Methods: "Loss of smell" and "loss of taste" related keywords were searched in the Google Trends platform, the data were obtained from Jan. 1 2019 to Jul. 11 2021. The daily and newly confirmed COVID-19 case number were collected from World Health Organization (WHO) since Dec. 30 2019. All data were statistically analyzed by SPSS 23.0 software. The correlation was finally tested by Spearman correlation analysis. Results: A total of data from 80 weeks were collected. The retrospective analysis was performed on the new trend of COVID-19 confirmed cases in a total of 186 292 441 cases worldwide. Since the epidemic of COVID-19 was recorded on the WHO website, the relative searches related to loss of smell/taste in the Google Trends platform had been increasing globally. The global relative search volumes of "loss of smell" and "loss of taste" on Google Trends was 10.23±2.58 and 16.33±2.47 before the record of epidemic while 80.25±39.81 and 80.45±40.04 after (t value was 8.67, 14.43, respectively, both P<0.001). In the United States and India, the relative searches for "loss of smell" and "loss of taste" after the record of epidemic were also much higher than before (all P<0.001). The correlation coefficients between the trend of weekly new COVID-19 cases and the Google Trends of "loss of smell" in the global, United States, and India was 0.53, 0.76, and 0.82 respectively (all P<0.001), the correlation coefficients with Google Trends of "loss of taste" was 0.54, 0.78, and 0.82 respectively (all P<0.001). The lowest and highest point of loss of smell/taste search curves of Google Trends in different periods appeared 7 to 14 days earlier than that of the weekly newly COVID-19 confirmed cases curves, respectively. Conclusions: There is a significant positive correlation between the number of newly confirmed cases of COVID-19 worldwide and the amount of keywords, such as "loss of smell" and "loss of taste", retrieved in Google Trends. The trend of big data based on Google Trends might predict the outbreak trend of COVID-19 in advance.

Research progress of micronucleus visualization analysis and artificial intelligence detection strategy / 中华预防医学杂志

The micronucleomics test can comprehensively display a variety of harmful endpoints, such as DNA damage and repair, chromosome breakage or loss and cell growth inhibition, with fast, simple and economical feature. Micronucleomics is not only widely used in the comprehensive assessment of the types and modes of genetic action of exogenous chemicals (such as drugs, food additives, cosmetics, environmental pollutants, etc.), but also plays an important role in the screening and risk assessment of cancer population at high risk. However, the traditional micronucleomics image counting method has the characteristics of time-consuming, low accuracy, and high cost, which cannot meet the current analysis requirements of large-scale, multi-index, rapidity, high precision and visualization. In recent years, with the rapid development of the era of precision medicine based on big data, visualized analysis of new micronucleomics based on machine learning and detection strategies based on deep learning have shown a good application prospect. This review, based on the application value of micronucleomics, systematically compares the traditional and new artificial intelligence counting of micronucleus images, and discusses the future direction of micronucleus image detection.

Methodology progress and challenges on assessing the appropriateness of real-world data / 中华流行病学杂志

From the perspective of data users, ensuring the relevance and reliability of big data in healthcare and medicine via assessments on data appropriateness is a prerequisite for generating high-quality real-world evidence, which could guarantee good representativeness and generalizability of real-world studies. This review summarized the quality dimensions, definitions, evaluation indexes and calculating methods of assessment on the appropriateness of real-world data (RWD) according to guidance from different countries and international organizations, as well as exploring the opportunities and challenges for better assessing RWD appropriateness.