Abstract
Background: The clinical decision-making process in pressure ulcer management is complex, and its quality depends on both the nurse's experience and the availability of scientific knowledge. This process should follow evidence-based practices incorporating health information technologies to assist health care professionals, such as the use of clinical decision support systems. These systems, in addition to increasing the quality of care provided, can reduce errors and costs in health care. However, the widespread use of clinical decision support systems still has limited evidence, indicating the need to identify and evaluate its effects on nursing clinical practice. Objective: The goal of the review was to identify the effects of nurses using clinical decision support systems on clinical decision making for pressure ulcer management. Methods: The systematic review was conducted in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) recommendations. The search was conducted in April 2019 on 5 electronic databases: MEDLINE, SCOPUS, Web of Science, Cochrane, and CINAHL, without publication date or study design restrictions. Articles that addressed the use of computerized clinical decision support systems in pressure ulcer care applied in clinical practice were included. The reference lists of eligible articles were searched manually. The Mixed Methods Appraisal Tool was used to assess the methodological quality of the studies. Results: The search strategy resulted in 998 articles, 16 of which were included. The year of publication ranged from 1995 to 2017, with 45% of studies conducted in the United States. Most addressed the use of clinical decision support systems by nurses in pressure ulcers prevention in inpatient units. All studies described knowledge-based systems that assessed the effects on clinical decision making, clinical effects secondary to clinical decision support system use, or factors that influenced the use or intention to use clinical decision support systems by health professionals and the success of their implementation in nursing practice. Conclusions: The evidence in the available literature about the effects of clinical decision support systems (used by nurses) on decision making for pressure ulcer prevention and treatment is still insufficient. No significant effects were found on nurses' knowledge following the integration of clinical decision support systems into the workflow, with assessments made for a brief period of up to 6 months. Clinical effects, such as outcomes in the incidence and prevalence of pressure ulcers, remain limited in the studies, and most found clinically but nonstatistically significant results in decreasing pressure ulcers. It is necessary to carry out studies that prioritize better adoption and interaction of nurses with clinical decision support systems, as well as studies with a representative sample of health care professionals, randomized study designs, and application of assessment instruments appropriate to the professional and institutional profile. In addition, long-term follow-up is necessary to assess the effects of clinical decision support systems that can demonstrate a more real, measurable, and significant effect on clinical decision making.

Abstract
Quantum annealing provides a method to solve combinatorial optimization problems in complex energy landscapes by exploiting thermal fluctuations that exist in a physical system. This work introduces the mapping of a graph coloring problem based on pseudo-Boolean constraints to a working graph of the D-Wave Systems Inc. We start from the problem formulated as a set of constraints represented in propositional logic. We use the SATyrus approach to transform this set of constraints to an energy minimization problem. We convert the formulation to a quadratic unconstrained binary optimization problem (QUBO), applying polynomial reduction when needed, and solve the problem using different approaches: (a) classical QUBO using simulated annealing in a von Neumann machine; (b) QUBO in a simulated quantum environment; (c) actual quantum 1, QUBO using the D-Wave quantum machine and reducing polynomial degree using a D-Wave library; and (d) actual quantum 2, QUBO using the D-Wave quantum machine and reducing polynomial degree using our own implementation. We study how the implementations using these approaches vary in terms of the impact on the number of solutions found (a) when varying the penalties associated with the constraints and (b) when varying the annealing approach, simulated (SA) versus quantum (QA). Results show that both SA and QA produce good heuristics for this specific problem, although we found more solutions through the QA approach.

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Abstract
One important problem in Bioinformatics is the discovery of new interactions between cellular lines and chemical compounds. In silico methods for cell-line screening are fundamental to optimize cost and time in the drug discovery processes. In order to build these methods, we need to computationally represent cell lines. Current methods for modeling cell line interactions rely on comparing genetic expression profiles. However, these profiles are usually unknown. In this work, we present a method to characterize and represent cell lines by text processing the related scientific literature. We collect abstracts of scientific papers about cellular lines from Cellosaurus and PubMed. These documents are then represented as TF-IDF vectors. We build a data set for classification with the document vectors having the cell line identifier as the target class. We then apply a multiclass SVM classification method. We use Support Vector Domain Description to describe and characterize each cell line with its corresponding hyperplane obtained with a one-vs-rest training. We evaluated several configurations of classifiers, using micro-averaged precision as metric to choose the best classifier, and were able to differentiate cellular lines from a set of 200+.

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Abstract
The proliferation of electronic health (e-Health) initiatives in Brazil over the last 2 decades has resulted in a considerable fragmentation within health information technology (IT), with a strong political interference. The problem regarding this issue became twofold: 1) there are considerable flaws regarding interoperability and security involving patient data; and 2) it is difficult even for an experienced company to enter the Brazilian health IT market. In this article, the authors aim to assess the current state of IT interoperability and security in hospitals in Brazil and evaluate the best business strategy for an IT company to enter this difficult but very promising health IT market. A face-to-face questionnaire was conducted among 11 hospital units to assess their current status regarding IT interoperability and security aspects. Global Brazilian socio-economic data was also collected, and helped to not only identify areas of investment regarding health IT security and interoperability, but also to derive a business strategy, composed out of recommendations listed in the paper.