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Breast cancer has an important incidence in women mortality worldwide. Currently, mammography is considered the gold standard for breast abnormalities screening examinations, since it aids in the early detection and diagnosis of the illness. However, both identification of mass lesions and its malignancy classification is a challenging problem for artificial intelligence. In this work, we extend our previous research in mammogram classification, where we studied NasNet and MobileNet in transfer learning to train a breast abnormality malignancy classifier, and include models like: VGG, Resnet, Xception and Resnext. However, training deep learning models tends to overfit. This problem is also carried out in this work. Our results show that Fine Tuning achieves the best classifier performance in VGG16 with AUC value of 0.844 in the CBIS-DDSM dataset.

Matrix-Assisted Laser Desorption/Ionization-Time of Flight Mass Spectrometry is a widely used proteomic technique in clinical microbiology laboratories, and enables microbial identification directly from clinical samples. This study seeks to establish a protocol for bacterial identification from monomicrobial urine samples that have tested positive in the screening with Sysmex UF-1000i (Sysmex Corporation, Kobe, Japan). Sysmex UF-1000i counts ≥1 × 10(7) bacteria/mL indicate a sufficient bacterial concentration to allow direct identification from urine, with 87.5% sensitivity. Microbial identification from urine with Sysmex UF-1000i counts between 1 × 10(5) and 1 × 10(7) bacteria/ml requires preincubation to obtain the adequate amount of bacteria needed for analysis, and 91.7% sensitivity thus being achieved.

Standard video games are applications whose development process often follows a traditional software methodology. Serious Games (SGs) are a tool with an immensely positive impact and great success. SGs enable learning and provide entertainment and self-empowerment, which motivates students. The development of an SG consists of complex processes requiring multi-disciplinary knowledge in multiple domains, including knowing the learning domain and adding the appropriate game mechanics to foster high intrinsic motivation and positive player experience that makes the players feel like they are having fun while learning. Otherwise, the game is viewed as boring and not as a fun and engaging activity. Nevertheless, despite their potential, the application of SGs in education has been limited in terms of pedagogy. Several authors assert that this lack is because SG standards and guidelines have not been developed. There is an imbalance between experts’ contributions to education and game design specialists for the SG development. Not all the SGs that have been developed have applied appropriate design methodologies that incorporate both the entertainment mechanics and the serious component. To ensure that an SG meets the user’s expectations, it must be designed using an appropriate method. This work aims to present iPlus, a methodology for designing SGs based on a participatory, flexible, and user-centered approach. Additionally, this paper analyses several case studies with the iPlus methodology.

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