Desafios e avanços na personalização diagnóstica e terapêutica na era da inteligência artificial na saúde

The integration of artificial intelligence (AI) and machine learning (ML) in medicine represents a rapidly growing field, promising significant advances in diagnostic and treatment processes. Given this scenario, this integrative review seeks to consolidate and critically analyze the available scientific evidence on the application of these innovative technologies in medical practice. The methodology adopted for this integrative review involved a comprehensive search of the main databases, such as PubMed, Scielo and ScienceDirect, using the relevant descriptors, such as "Artificial Intelligence", "Machine Learning", "Clinical Diagnosis", "Machine Learning" and "Deep Learning". The careful selection of references included relevant studies that address the application of AI and ML in various domains of medicine, with a special focus on the references indicated in Vancouver in this abstract. The results of this review reveal a wide range of successful applications of AI and AM in medical diagnosis and treatment. Studies such as Wang et al. (2019) highlight the progress and challenges of using deep learning in medicine, while work by Erickson et al. (2017) highlights the effectiveness of ML in medical imaging, contributing to advances in clinical practice. Ethical approaches and future impacts on the actions of healthcare professionals, as discussed by Ahuja (2019) and Farhud and Zokaei (2021), emerge as crucial points in the integration of these technologies. The conclusion of this integrative review reinforces the significant transformation provided by the integration of AI and AM in medicine, offering faster and more accurate diagnoses, as well as outlining intrinsic ethical challenges. Patient privacy and ethical considerations become critical factors in this scenario. This comprehensive analysis highlights the continued need for responsible research and development, promoting advances that optimize clinical efficacy and ensure the trust of healthcare professionals and patients in the face of these transformative innovations.A integração de inteligência artificial (IA) e aprendizado de máquina (AM) na medicina representa um campo em rápido crescimento, prometendo avanços significativos nos processos de diagnóstico e tratamento. Diante desse cenário, a presente revisão integrativa busca consolidar e analisar criticamente as evidências científicas disponíveis sobre a aplicação dessas tecnologias inovadoras na prática médica. A metodologia adotada para esta revisão integrativa envolveu uma busca abrangente nas principais bases de dados, como PubMed, Scielo e Scopus, utilizando os descritores pertinentes, tais como "Inteligência Artificial", "Aprendizado de Máquina", "Diagnóstico Clínico", "Machine Learning" e "Deep Learning". A seleção criteriosa das referências incluiu estudos relevantes que abordam a aplicação de IA e AM em diversos domínios da medicina, com foco especial nas referências indicadas em Vancouver neste resumo. Os resultados desta revisão revelam uma ampla gama de aplicações bem-sucedidas de IA e AM em diagnósticos e tratamentos médicos. Estudos como o de Wang et al. (2019) destacam os progressos e desafios do uso de deep learning na medicina, enquanto trabalhos de Erickson et al. (2017) evidenciam a eficácia do AM em imagens médicas, contribuindo para avanços na prática clínica. Abordagens éticas e impactos futuros na atuação dos profissionais de saúde, conforme discutido por Ahuja (2019) e Farhud e Zokaei (2021), emergem como pontos cruciais na integração dessas tecnologias. A conclusão desta revisão integrativa reforça a transformação significativa proporcionada pela integração de IA e AM na medicina, oferecendo diagnósticos mais rápidos e precisos, bem como delineando desafios éticos intrínsecos. A privacidade do paciente e as considerações éticas tornam-se fatores críticos nesse cenário. Esta análise abrangente destaca a necessidade contínua de pesquisa e desenvolvimento responsável, promovendo avanços que otimizem a eficácia clínica e garantam a confiança dos profissionais de saúde e dos pacientes diante dessas inovações transformadoras

Inovações cirúrgicas na abordagem de doenças neurodegenerativas

This article is an integrative review of surgical innovations in the treatment of neurodegenerative diseases. In response to the growing interest in the intersection between surgery, neuroscience and emerging technologies, it seeks to understand current and future trends in surgical interventions for the treatment of these conditions. The methodology adopted followed strict guidelines for conducting an integrative review. A systematic search was carried out in scientific databases such as PubMed, Scopus and Scielo, using specific keywords. Inclusion and exclusion criteria were established for the selection of references, taking into account their relevance to surgical innovations in neurodegenerative diseases. Critical analysis of the selected references revealed a wide range of surgical innovations applied in the context of neurodegenerative diseases. Approaches such as neurostimulation, brain-machine interfaces, deep brain stimulation and other advanced surgical techniques stand out. Results indicate significant benefits in terms of cognitive improvement, motor control and quality of life for patients undergoing these interventions. The integrative review provided a comprehensive overview of surgical innovations in the approach to neurodegenerative diseases. Technological advances and innovative surgical approaches present promising therapeutic prospects. However, challenges such as personalization of treatments and long-term evaluation still require further investigation. The conclusion underscores the continued importance of research in this field to improve the efficacy and safety of these interventions, thus contributing to advances in the treatment of neurodegenerative diseases.O presente artigo realiza uma revisão integrativa abordando as inovações cirúrgicas na abordagem de doenças neurodegenerativas. Em resposta ao crescente interesse na interseção entre cirurgia, neurociência e tecnologias emergentes, busca-se compreender as tendências atuais e futuras nas intervenções cirúrgicas para o tratamento dessas condições. A metodologia adotada seguiu diretrizes rigorosas para a realização de uma revisão integrativa. Foi realizada uma busca sistemática em bases de dados científicas, como PubMed, Scopus e Scielo, utilizando palavras-chave específicas. Foram estabelecidos critérios de inclusão e exclusão para a seleção das referências, levando em consideração a relevância para inovações cirúrgicas em doenças neurodegenerativas. A análise crítica das referências selecionadas revelou uma ampla gama de inovações cirúrgicas aplicadas no contexto das doenças neurodegenerativas. Destacam-se abordagens como neuroestimulação, interfaces cérebro-máquina, estimulação cerebral profunda e outras técnicas cirúrgicas avançadas. Resultados indicam benefícios significativos em termos de melhoria cognitiva, controle motor e qualidade de vida para os pacientes submetidos a essas intervenções. A revisão integrativa proporcionou uma visão abrangente das inovações cirúrgicas na abordagem de doenças neurodegenerativas. Os avanços tecnológicos e as abordagens cirúrgicas inovadoras apresentam promissoras perspectivas terapêuticas. Contudo, desafios como a personalização dos tratamentos e a avaliação a longo prazo ainda requerem investigações mais aprofundadas. A conclusão ressalta a importância contínua da pesquisa nesse campo para aprimorar a eficácia e a segurança dessas intervenções, contribuindo assim para o avanço do tratamento de doenças neurodegenerativas

Transtornos psiquiátricos prevalentes na infância: lidando com desafios comportamentais.

Objective: This study aims to synthesize the evidence on psychiatric disorders in childhood, identifying intervention strategies. The aim is to offer a comprehensive view to guide clinical practices and promote understanding of these conditions. Methodology: The careful selection of the integrative review through critical reading and comprehensive analysis of articles aims to synthesize evidence on psychiatric disorders in childhood and intervention strategies, contributing to a practical understanding of these conditions, through the health descriptors: “Psychiatric Disorders”, “Behavioral Disorders”, “Child Psychiatry”. Results: The comparative analysis of psychiatric disorders in childhood reveals patterns and differences in therapeutic approaches, covering conditions such as ADHD, ASD, GAD and OCD. The coexistence of multiple disorders amplifies the complexity of treatment, requiring an integrated approach. Practitioners face the challenge of personalizing interventions, emphasizing the need for collaboration and adaptation in the holistic management of conditions. Conclusion: In conclusion, understanding the therapeutic nuances and challenges associated with co-existing childhood psychiatric disorders highlights the importance of personalized approaches and interdisciplinary collaboration to optimize treatment and improve children's well-being. The flexibility and adaptability of professionals are fundamental to face the complexity of these conditions.Objetivo: Este estudo visa sintetizar as evidências sobre transtornos psiquiátricos na infância, identificando estratégias de intervenção. Busca-se oferecer uma visão abrangente para orientar práticas clínicas e promover o entendimento dessas condições. Metodologia: A seleção criteriosa da revisão integrativa por meio da leitura crítica e análise abrangente de artigos visam sintetizar evidências sobre transtornos psiquiátricos na infância e estratégias de intervenção, contribuindo para uma compreensão prática dessas condições, através dos descritores de saúde: “Transtornos Psiquiátricos”, “Transtornos de Comportamento”, “Psiquiatria Infantil”. Resultados: A análise comparativa de transtornos psiquiátricos na infância revela padrões e diferenças nas abordagens terapêuticas, abrangendo condições como TDAH, TEA, TAG e TOC. A coexistência de múltiplos transtornos amplifica a complexidade do tratamento, exigindo uma abordagem integrada. Profissionais enfrentam o desafio de personalizar intervenções, enfatizando a necessidade de colaboração e adaptação na gestão holística das condições. Conclusão: Em conclusão, a compreensão das nuances terapêuticas e desafios associados à coexistência de transtornos psiquiátricos na infância destaca a importância de abordagens personalizadas e da colaboração interdisciplinar para otimizar o tratamento e melhorar o bem-estar das crianças. A flexibilidade e adaptabilidade dos profissionais são fundamentais para enfrentar a complexidade dessas condições

Crystallization and preliminary X-ray analysis of BigR, a transcription repressor from Xylella fastidiosa involved in biofilm formation

In order to gain new insights into the protein structure and its possible interaction with a metal ion or effector ligand, BigR from X. fastidiosa was crystallized in native and selenomethionine (SeMet) labelled forms using the hanging-drop vapour-diffusion method

BioTIME:a database of biodiversity time series for the Anthropocene

Motivation: The BioTIME database contains raw data on species identities and abundances in ecological assemblages through time. These data enable users to calculate temporal trends in biodiversity within and amongst assemblages using a broad range of metrics. BioTIME is being developed as a community led open-source database of biodiversity time series. Our goal is to accelerate and facilitate quantitative analysis of temporal patterns of biodiversity in the Anthropocene.Main types of variables included: The database contains 8,777,413 species abundance records, from assemblages consistently sampled for a minimum of two, which need not necessarily be consecutive. In addition, the database contains metadata relating to sampling methodology andcontextual information about each record.Spatial location and grain: BioTIME is a global database of 547,161 unique sampling locations spanning the marine, freshwater and terrestrial realms. Grain size varies across datasets from 0.0000000158 km2 (158 cm2) to 100 km2 (1 000 000 000 000 cm2).Time period and grain: BioTIME records span from 1874 to 2016. The minimum temporal grain across all datasets in BioTIME is year.Major taxa and level of measurement: BioTIME includes data from 44,440 species across the plant and animal kingdoms, ranging from plants, plankton, and terrestrial invertebrates to small and large vertebrates.Software format: .csv and .SQ

Are rare species useful species? Obstacles to the conservation of tree diversity in the dry forest zone agro-ecosystems of Mesoamerica

Aim To test the potential to conserve rare dry forest tree and shrub species circa situm.Location Oaxaca, Mexico and Southern Honduras.Methods Local uses (timber, posts and firewood) of species were determined principally through semistructured interviews with 20 rural householders in each of four communities in Honduras and four in Oaxaca. Tree and shrub diversity inventories were carried out in a total of 227 forest patches and parcels of farmland in those eight communities. Species’ conservation priorities were determined using the star system of Hawthorne (1996) and IUCN listings.Results Despite a large number of useful species, remarkably few were also conservation priorities. Useful species were found to be substitutable as is illustrated by Bombacopsis quinata, Cordia alliodora, Guaiacum sanctum and G. coulteri.Conclusions In these areas, circa situm conservation is inhibited by the lack of species that are both rare and useful. Usefulness must be interpreted as a function of substitutability. Natural regeneration provides an abundance of diversity, farmers are unlikely to invest in the management of a species when suitable substitutes are freely available. The key to conserving rare species may be in maintaining or enhancing the value of the landscape elements in which they are found

BioTIME:a database of biodiversity time series for the Anthropocene

Abstract Motivation: The BioTIME database contains raw data on species identities and abundances in ecological assemblages through time. These data enable users to calculate temporal trends in biodiversity within and amongst assemblages using a broad range of metrics. BioTIME is being developed as a community‐led open‐source database of biodiversity time series. Our goal is to accelerate and facilitate quantitative analysis of temporal patterns of biodiversity in the Anthropocene. Main types of variables included: The database contains 8,777,413 species abundance records, from assemblages consistently sampled for a minimum of 2 years, which need not necessarily be consecutive. In addition, the database contains metadata relating to sampling methodology and contextual information about each record. Spatial location and grain: BioTIME is a global database of 547,161 unique sampling locations spanning the marine, freshwater and terrestrial realms. Grain size varies across datasets from 0.0000000158 km² (158 cm²) to 100 km² (1,000,000,000,000 cm²). Time period and grain: BioTIME records span from 1874 to 2016. The minimal temporal grain across all datasets in BioTIME is a year. Major taxa and level of measurement: BioTIME includes data from 44,440 species across the plant and animal kingdoms, ranging from plants, plankton and terrestrial invertebrates to small and large vertebrates. Software format: .csv and .SQL