Development and Assessment of an E-Learning Course on Breast Imaging for Radiographers: A Stratified Randomized Controlled Trial

Background: Mammography is considered the best imaging technique for breast cancer screening, and the radiographer plays an important role in its performance. Therefore, continuing education is critical to improving the performance of these professionals and thus providing better health care services. Objective: Our goal was to develop an e-learning course on breast imaging for radiographers, assessing its efficacy , effectiveness, and user satisfaction. Methods: A stratified randomized controlled trial was performed with radiographers and radiology students who already had mammography training, using pre- and post-knowledge tests, and satisfaction questionnaires. The primary outcome was the improvement in test results (percentage of correct answers), using intention-to-treat and per-protocol analysis. Results: A total of 54 participants were assigned to the intervention (20 students plus 34 radiographers) with 53 controls (19+34). The intervention was completed by 40 participants (11+29), with 4 (2+2) discontinued interventions, and 10 (7+3) lost to follow-up. Differences in the primary outcome were found between intervention and control: 21 versus 4 percentage points (pp), P<.001. Stratified analysis showed effect in radiographers (23 pp vs 4 pp; P=.004) but was unclear in students (18 pp vs 5 pp; P=.098). Nonetheless, differences in students’ posttest results were found (88% vs 63%; P=.003), which were absent in pretest (63% vs 63%; P=.106). The per-protocol analysis showed a higher effect (26 pp vs 2 pp; P<.001), both in students (25 pp vs 3 pp; P=.004) and radiographers (27 pp vs 2 pp; P<.001). Overall, 85% were satisfied with the course, and 88% considered it successful. Conclusions: This e-learning course is effective, especially for radiographers, which highlights the need for continuing education

PaaSword: A Data Privacy and Context-aware Security Framework for Developing Secure Cloud Applications - Technical and Scientific Contributions

Most industries worldwide have entered a period of reaping the benefits and opportunities cloud offers. At the same time, many efforts are made to address engineering challenges for the secure development of cloud systems and software.With the majority of software engineering projects today relying on the cloud, the task to structure end-to-end secure-by-design cloud systems becomes challenging but at the same time mandatory. The PaaSword project has been commissioned to address security and data privacy in a holistic way by proposing a context-aware security-by-design framework to support software developers in constructing secure applications for the cloud. This chapter presents an overview of the PaaSword project results, including the scientific achievements as well as the description of the technical solution. The benefits offered by the framework are validated through two pilot implementations and conclusions are drawn based on the future research challenges which are discussed in a research agenda

Image Analysis and Processing with Applications in Proteomics and Medicine

This thesis introduces unsupervised image analysis algorithms for the segmentation of several types of images, with an emphasis on proteomics and medical images. Segmentation is a challenging task in computer vision with essential applications in biomedical engineering, remote sensing, robotics and automation. Typically, the target region is separated from the rest of image regions utilizing defining features including intensity, texture, color or motion cues. In this light, multiple segments are generated and the selection of the most significant segments becomes a controversial decision as it highly hinges on heuristic considerations. Moreover, the separation of the target regions is impeded by several daunting factors such as: background clutter, the presence of noise and artifacts as well as occlusions on multiple target regions. This thesis focuses on image segmentation using deformable models and specifically region-based Active Contours (ACs) because of their strong mathematical foundation and their appealing properties

An OpenEHR adoption in a portuguese healthcare facility

The quality and safety of clinical decisions depend to a large extent on the knowledge acquired by the records of health professionals. However, a traditional Electronic Health Record (EHR) has become insufficient in terms of knowledge acquisition and clinical decision support. The development of these aspects may bring marked improvements in the quality and safety of health care. The usage of open models promotes interoperability between systems, minimizing the impact of information systems on the efficient production of knowledge useful for clinical decisions. In this sense, this article describes an implementation project of a system that support the production and use of knowledge in clinical environments, based on OpenEHR two levels modelling open data approach, in a healthcare facility on the north of Portugal.he work has been supported by FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/202

Finding the Most Uniform Changes in Vowel Polygon Caused by Psychological Stress

Using vowel polygons, exactly their parameters, is chosen as the criterion for achievement of differences between normal state of speaker and relevant speech under real psychological stress. All results were experimentally obtained by created software for vowel polygon analysis applied on ExamStress database. Selected 6 methods based on cross-correlation of different features were classified by the coefficient of variation and for each individual vowel polygon, the efficiency coefficient marking the most significant and uniform differences between stressed and normal speech were calculated. As the best method for observing generated differences resulted method considered mean of cross correlation values received for difference area value with vector length and angle parameter couples. Generally, best results for stress detection are achieved by vowel triangles created by /i/-/o/-/u/ and /a/-/i/-/o/ vowel triangles in formant planes containing the fifth formant F5 combined with other formants

Attributes for causal inference in electronic healthcare databases

Side effects of prescription drugs present a serious issue. Existing algorithms that detect side effects generally require further analysis to confirm causality. In this paper we investigate attributes based on the Bradford-Hill causality criteria that could be used by a classifying algorithm to definitively identify side effects directly. We found that it would be advantageous to use attributes based on the association strength, temporality and specificity criteria

Attributes for causal inference in electronic healthcare databases

Side effects of prescription drugs present a serious issue. Existing algorithms that detect side effects generally require further analysis to confirm causality. In this paper we investigate attributes based on the Bradford-Hill causality criteria that could be used by a classifying algorithm to definitively identify side effects directly. We found that it would be advantageous to use attributes based on the association strength, temporality and specificity criteria

Functional and sequence-based metagenomics to uncover carbohydrate-degrading enzymes from composting samples

The online version contains supplementary material available at: https://doi.org/10.1007/s00253-023-12627-9.The renewable, abundant, and low-cost nature of lignocellulosic biomass can play an important role in the sustainable production of bioenergy and several added-value bioproducts, thus providing alternative solutions to counteract the global energetic and industrial demands. The efficient conversion of lignocellulosic biomass greatly relies on the catalytic activity of carbohydrate-active enzymes (CAZymes). Finding novel and robust biocatalysts, capable of being active under harsh industrial conditions, is thus imperative to achieve an economically feasible process. In this study, thermophilic compost samples from three Portuguese companies were collected, and their metagenomic DNA was extracted and sequenced through shotgun sequencing. A novel multi-step bioinformatic pipeline was developed to find CAZymes and characterize the taxonomic and functional profiles of the microbial communities, using both reads and metagenome-assembled genomes (MAGs) as input. The samples' microbiome was dominated by bacteria, where the classes Gammaproteobacteria, Alphaproteobacteria, and Balneolia stood out for their higher abundance, indicating that the degradation of compost biomass is mainly driven by bacterial enzymatic activity. Furthermore, the functional studies revealed that our samples are a rich reservoir of glycoside hydrolases (GH), particularly of GH5 and GH9 cellulases, and GH3 oligosaccharide-degrading enzymes. We further constructed metagenomic fosmid libraries with the compost DNA and demonstrated that a great number of clones exhibited $\beta$-glucosidase activity. The comparison of our samples with others from the literature showed that, independently of the composition and process conditions, composting is an excellent source of lignocellulose-degrading enzymes. To the best of our knowledge, this is the first comparative study on the CAZyme abundance and taxonomic/functional profiles of Portuguese compost samples.Open access funding provided by FCT|FCCN (b-on). This study was supported by the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UIDB/04469/2020 unit, the projects LIGNOZYMES—Metagenomics approach to unravel the potential of lignocellulosic residues towards the discovery of novel enzymes (POCI-01–0145-FEDER-029773), and B3iS—Biodiversity and Bioprospecting of Biosurfactants in Saline Environments (PTDC/BII-BIO/5554/2020); and by RNCA Advanced Computing Project MetaLignoZymes, metagenomic analysis of lignocellulosic residues towards the discovery of novel enzymes (CPCA/A0/408464/2021).info:eu-repo/semantics/publishedVersio