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Missing data is a prevalent problem that requires attention, as most data analysis techniques are unable to handle it. This is particularly critical in Multi-Label Classification (MLC), where only a few studies have investigated missing data in this application domain. MLC differs from Single-Label Classification (SLC) by allowing an instance to be associated with multiple classes. Movie classification is a didactic example since it can be "drama" and "bibliography" simultaneously. One of the most usual missing data treatment methods is data imputation, which seeks plausible values to fill in the missing ones. In this scenario, we propose a novel imputation method based on a multi-objective genetic algorithm for optimizing multiple data imputations called Multiple Imputation of Multi-label Classification data with a genetic algorithm, or simply EvoImp. We applied the proposed method in multi-label learning and evaluated its performance using six synthetic databases, considering various missing values distribution scenarios. The method was compared with other state-of-the-art imputation strategies, such as K-Means Imputation (KMI) and weighted K-Nearest Neighbors Imputation (WKNNI). The results proved that the proposed method outperformed the baseline in all the scenarios by achieving the best evaluation measures considering the Exact Match, Accuracy, and Hamming Loss. The superior results were constant in different dataset domains and sizes, demonstrating the EvoImp robustness. Thus, EvoImp represents a feasible solution to missing data treatment for multi-label learning.

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Emerging deep learning-based applications in precision medicine include computational histopathological analysis. However, there is a lack of the required training image datasets to generate classification and detection models. This phenomenon occurs mainly due to human factors that make it difficult to obtain well-annotated data. The present study provides a curated public collection of histopathological images (DeepHP) and a convolutional neural network model for diagnosing gastritis. Images from gastric biopsy histopathological exams were used to investigate the performance of the proposed model in detecting gastric mucosa with Helicobacter pylori infection. The DeepHP database comprises 394,926 histopathological images, of which 111 K were labeled as Helicobacter pylori positive and 283 K were Helicobacter pylori negative. We investigated the classification performance of three Convolutional Neural Network architectures. The models were tested and validated with two distinct image sets of 15% (59K patches) chosen randomly. The VGG16 architecture showed the best results with an Area Under the Curve of 0.998%. The results showed that CNN could be used to classify histopathological images from gastric mucosa with marked precision. Our model evidenced high potential and application in the computational pathology field.

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Objetivo: Examinar a relação entre uso de aplicativos geossociais com adoção de práticas preventivas de Infecções Sexualmente Transmissíveis (ISTs). Métodos: Estudo descritivo, prospectivo e transversal com abordagem quantitativa, com 256 estudantes da Universidade Federal do Oeste do Pará. Os dados foram analisados com auxílio do software Bioestat® 5.0 e da biblioteca SciPy da linguagem Python. Resultados: Houve predominância do sexo feminino (62%), faixa etária de 18-23 anos (68%) e heterossexuais (79%). Foram fatores associados ao uso de aplicativos: Orientação sexual (p = 0,0001), frequência de utilização de proteção sexual (p = 0,0350), finalidade da utilização de proteção sexual (p = 0,0004) e periodicidade de testes de ISTs (p = 0,0029). Conclusão: Usuários de aplicativos geossociais são jovens. Indivíduos homossexuais apresentam maior tendência a busca destas plataformas. Características e particularidades do consumo dos aplicativos estão associadas a utilização inconsistente de proteção sexual e propensão a realização de testes de ISTs.

Objective: To examine the relationship between geosocial application usage and adoption of preventive practices for Sexually Transmitted Infections (STIs). Methods: Descriptive, prospective and cross-sectional study with a quantitative approach, with 256 students from the Federal University of Western Pará. The data were analyzed with Bioestat® 5.0 software and the SciPy library from Python. Results: There was a predominance of females (62%), 18-23 years old (68%) and heterosexuals (79%). Factors associated with the use of applications were: sexual orientation (p = 0.0001), frequency of using sexual protection (p = 0.0350), purpose of using sexual protection (p = 0.0004) and frequency of STIs (p = 0.0029). Conclusion: Users of geosocial applications are young. Homosexual individuals are more likely to look for these platforms. Characteristics and particularities of application consumption are associated with inconsistent use of sexual protection and propensity to perform STI tests.

Objetivo: Examinar la relación entre el uso de aplicaciones geo sociales y la adopción de prácticas preventivas para las enfermedades de transmisión sexual (ITS). Métodos: Estudio descriptivo, prospectivo y transversal con enfoque cuantitativo, con 256 estudiantes de la Universidade Federal do Oeste do Pará. Los datos fueron analizados con la ayuda del software Bioestat® 5.0 y la biblioteca SciPy del lenguaje Python. Resultados: Predominó el sexo femenino (62%), 18-23 años (68%) y heterosexuales (79%). Los factores asociados con el uso de aplicaciones fueron: orientación sexual (p = 0.0001), frecuencia de uso de protección sexual (p = 0.0350), propósito de usar protección sexual (p = 0.0004) y frecuencia de ITS (p = 0.0029). Conclusión: Los usuarios de aplicaciones geo sociales son jóvenes. Las personas homosexuales tienen más probabilidades de buscar por estas plataformas. Las características y particularidades del consumo de las aplicaciones están asociadas con el uso inconsistente de la protección sexual y la propensión a realizar pruebas de ITS.

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Background: In recent years, deep learning has gained remarkable attention in medical image analysis due to its capacity to provide results comparable to specialists and, in some cases, surpass them. Despite the emergence of deep learning research on gastric tissues diseases, few intensive reviews are addressing this topic. Method: We performed a systematic review related to applications of deep learning in gastric tissue disease analysis by digital histology, endoscopy and radiology images. Conclusions: This review highlighted the high potential and shortcomings in deep learning research studies applied to gastric cancer, ulcer, gastritis and non-malignant diseases. Our results demonstrate the effectiveness of gastric tissue analysis by deep learning applications. Moreover, we also identified gaps of evaluation metrics, and image collection availability, therefore, impacting experimental reproducibility.

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