Computer-aided detection and diagnosis of breast cancer in 2D and 3D medical imaging through multifractal analysis

This Thesis describes the research work performed in the scope of a doctoral research program and presents its conclusions and contributions. The research activities were carried on in the industry with Siemens S.A. Healthcare Sector, in integration with a research team. Siemens S.A. Healthcare Sector is one of the world biggest suppliers of products, services and complete solutions in the medical sector. The company offers a wide selection of diagnostic and therapeutic equipment and information systems. Siemens products for medical imaging and in vivo diagnostics include: ultrasound, computer tomography, mammography, digital breast tomosynthesis, magnetic resonance, equipment to angiography and coronary angiography, nuclear imaging, and many others. Siemens has a vast experience in Healthcare and at the beginning of this project it was strategically interested in solutions to improve the detection of Breast Cancer, to increase its competitiveness in the sector. The company owns several patents related with self-similarity analysis, which formed the background of this Thesis. Furthermore, Siemens intended to explore commercially the computer- aided automatic detection and diagnosis eld for portfolio integration. Therefore, with the high knowledge acquired by University of Beira Interior in this area together with this Thesis, will allow Siemens to apply the most recent scienti c progress in the detection of the breast cancer, and it is foreseeable that together we can develop a new technology with high potential. The project resulted in the submission of two invention disclosures for evaluation in Siemens A.G., two articles published in peer-reviewed journals indexed in ISI Science Citation Index, two other articles submitted in peer-reviewed journals, and several international conference papers. This work on computer-aided-diagnosis in breast led to innovative software and novel processes of research and development, for which the project received the Siemens Innovation Award in 2012. It was very rewarding to carry on such technological and innovative project in a socially sensitive area as Breast Cancer.No cancro da mama a deteção precoce e o diagnóstico correto são de extrema importância na prescrição terapêutica e caz e e ciente, que potencie o aumento da taxa de sobrevivência à doença. A teoria multifractal foi inicialmente introduzida no contexto da análise de sinal e a sua utilidade foi demonstrada na descrição de comportamentos siológicos de bio-sinais e até na deteção e predição de patologias. Nesta Tese, três métodos multifractais foram estendidos para imagens bi-dimensionais (2D) e comparados na deteção de microcalci cações em mamogramas. Um destes métodos foi também adaptado para a classi cação de massas da mama, em cortes transversais 2D obtidos por ressonância magnética (RM) de mama, em grupos de massas provavelmente benignas e com suspeição de malignidade. Um novo método de análise multifractal usando a lacunaridade tri-dimensional (3D) foi proposto para classi cação de massas da mama em imagens volumétricas 3D de RM de mama. A análise multifractal revelou diferenças na complexidade subjacente às localizações das microcalci cações em relação aos tecidos normais, permitindo uma boa exatidão da sua deteção em mamogramas. Adicionalmente, foram extraídas por análise multifractal características dos tecidos que permitiram identi car os casos tipicamente recomendados para biópsia em imagens 2D de RM de mama. A análise multifractal 3D foi e caz na classi cação de lesões mamárias benignas e malignas em imagens 3D de RM de mama. Este método foi mais exato para esta classi cação do que o método 2D ou o método padrão de análise de contraste cinético tumoral. Em conclusão, a análise multifractal fornece informação útil para deteção auxiliada por computador em mamogra a e diagnóstico auxiliado por computador em imagens 2D e 3D de RM de mama, tendo o potencial de complementar a interpretação dos radiologistas

Self‐care nursing interventions: A qualitative study into electronic health records’ contents

Aims:This study aims to (1) analyse all self-care–related interventions Portuguesenurses documented, (2) determine potential issues that may impair semantic interoper-ability and (3) propose a new set of interventions representing nursing actions regardingself-care that may integrate any HER application.Background:As populations age and chronic diseases increase, self-care concerns rise.Individuals who seek healthcare, regardless of context, need prompt access to accuratehealth information. Healthcare professionals need to understand the information in allplaces where care is provided, creating the need for semantic interoperability withinelectronic health records.Methods:A qualitative descriptive and exploratory study was conducted in two phases:(1) a content analysis of nursing interventions e-documentation and (2) a focusgroup with fifteen registered nurses exploring latent criteria or insights gleaned fromthe findings of content analysis. The COREQ statement was used to guide researchreporting.Results:We extracted 1529 nursing intervention sentences from the electronic healthrecords and created 209 intervention categories. We identified the main issues withsemantic interoperability in nursing intervention identification.Conclusion:According to the findings, nurses cooperate with clients, offering phys-ical aid and encouraging them to overcome functional limitations to self-care taskshampered by their conditions.Implications for nursing policy and health policy:This article provides evidence towarn policy makers against decisions to use locally customised electronic health records,as well as evidence on the importance of policy promoting the adoption of a nursingontologyforelectronichealthrecords.And,asaresult,theharmonisationandeffec-tive provision of high-quality nursing care and the reduction of healthcare costs acrossnations.info:eu-repo/semantics/publishedVersio

Nursing knowledge on skin ulcer healing: a living scoping review protocol

Objective: This review aims to continuously map the nursing knowledge on skin ulcer healing in any context of care. Introduction: Chronic wounds are an increasing concern for society and health care providers. Pressure ulcers and venous ulcers, among others, have devastating effects on morbidity and quality of life and require a systematic approach. The nursing process is an important method that allows a better organization and overall care quality for a systematic and continuous professional approach to nursing management of skin ulcers. The integration of this nursing knowledge in informatics systems creates an opportunity to embed decision-support models in clinical activity, promoting evidence-based practice. Inclusion criteria: This scoping review will consider articles on nursing data, diagnosis, interventions, and outcomes focused on people with skin ulcers in all contexts of care. This review will include quantitative, qualitative, and mixed methods study designs as well as systematic reviews and dissertations. Methods: JBI’s scoping review guidance, as well as the Cochrane Collaboration’s guidance on living reviews, will be followed to meet the review’s objective. Screening of new literature will be performed regularly, with the review updated according to new findings. The search strategy will map published and unpublished studies. The databases to be searched include MEDLINE, CINAHL, Scopus, JBI Evidence Synthesis, Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials, and PEDro. Searches for unpublished studies will include OpenGrey and Reposito´ rios Cientı´ficos de Acesso Aberto de Portugal. Studies published in English and Portuguese since 2010 will be considered for inclusion.info:eu-repo/semantics/publishedVersio

Nursing knowledge of people with paresis of voluntary muscles: a living scoping review protocol

Objective: This review aims to continuously map the nursing knowledge about people with paresis of voluntary muscles in any context of care. Introduction: Muscle paresis is a condition that significantly impacts quality of life. Nurses have a crucial role in managing this condition, particularly paresis of voluntary movement muscles. However, nursing knowledge about patients with paresis of voluntary muscles is dispersed, hampering the integration of evidence within the structure of information systems. Mapping how the nursing process components are identified is the first step in creating a Nursing Clinical Information Model for this condition, capable of integrating evidence into information systems. Inclusion criteria: This scoping review will consider studies focusing on the nursing process regarding people with paresis of voluntary muscles in all care contexts. The review will include quantitative, qualitative, and mixed-methods study designs, systematic reviews, clinical guidelines, dissertations, and theses. Methods: The review process will follow JBI's scoping review guidance, as well as the Cochrane Collaboration's guidance on living reviews. Screening of new literature will be performed regularly, with the review being updated according to new findings. The search strategy will map published and unpublished studies. The databases to be searched will include MEDLINE, CINAHL, Scopus, JBI Evidence Synthesis, and the Cochrane Central Register of Controlled Trials. Searches for unpublished studies will include OpenGrey and Repositorios Cientificos de Acesso Aberto de Portugal. Studies published in English and Portuguese from 1975 will be considered for inclusion.info:eu-repo/semantics/publishedVersio

A list of land plants of Parque Nacional do Caparaó, Brazil, highlights the presence of sampling gaps within this protected area

Brazilian protected areas are essential for plant conservation in the Atlantic Forest domain, one of the 36 global biodiversity hotspots. A major challenge for improving conservation actions is to know the plant richness, protected by these areas. Online databases offer an accessible way to build plant species lists and to provide relevant information about biodiversity. A list of land plants of “Parque Nacional do Caparaó” (PNC) was previously built using online databases and published on the website "Catálogo de Plantas das Unidades de Conservação do Brasil." Here, we provide and discuss additional information about plant species richness, endemism and conservation in the PNC that could not be included in the List. We documented 1,791 species of land plants as occurring in PNC, of which 63 are cited as threatened (CR, EN or VU) by the Brazilian National Red List, seven as data deficient (DD) and five as priorities for conservation. Fifity-one species were possible new ocurrences for ES and MG states

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost