A Novel Bioinspired Algorithm for Mixed and Incomplete Breast Cancer Data Classification.

The pre-diagnosis of cancer has been approached from various perspectives, so it is imperative to continue improving classification algorithms to achieve early diagnosis of the disease and improve patient survival. In the medical field, there are data that, for various reasons, are lost. There are also datasets that mix numerical and categorical values. Very few algorithms classify datasets with such characteristics. Therefore, this study proposes the modification of an existing algorithm for the classification of cancer. The said algorithm showed excellent results compared with classical classification algorithms. The AISAC-MMD (Mixed and Missing Data) is based on the AISAC and was modified to work with datasets with missing and mixed values. It showed significantly better performance than bio-inspired or classical classification algorithms. Statistical analysis established that the AISAC-MMD significantly outperformed the Nearest Neighbor, C4.5, Naïve Bayes, ALVOT, Naïve Associative Classifier, AIRS1, Immunos1, and CLONALG algorithms in conducting breast cancer classification.

NanoChest-Net: A Simple Convolutional Network for Radiological Studies Classification.

The new coronavirus disease (COVID-19), pneumonia, tuberculosis, and breast cancer have one thing in common: these diseases can be diagnosed using radiological studies such as X-rays images. With radiological studies and technology, computer-aided diagnosis (CAD) results in a very useful technique to analyze and detect abnormalities using the images generated by X-ray machines. Some deep-learning techniques such as a convolutional neural network (CNN) can help physicians to obtain an effective pre-diagnosis. However, popular CNNs are enormous models and need a huge amount of data to obtain good results. In this paper, we introduce NanoChest-net, which is a small but effective CNN model that can be used to classify among different diseases using images from radiological studies. NanoChest-net proves to be effective in classifying among different diseases such as tuberculosis, pneumonia, and COVID-19. In two of the five datasets used in the experiments, NanoChest-net obtained the best results, while on the remaining datasets our model proved to be as good as baseline models from the state of the art such as the ResNet50, Xception, and DenseNet121. In addition, NanoChest-net is useful to classify radiological studies on the same level as state-of-the-art algorithms with the advantage that it does not require a large number of operations.

Assessing the Protective Activity of a Recently Discovered Phenolic Compound against Oxidative Stress Using Computational Chemistry.

The protection exerted by 3,5-dihydroxy-4-methoxybenzyl alcohol (DHMBA), a phenolic compound recently isolated from the Pacific oyster, against oxidative stress (OS) is investigated using the density functional theory. Our results indicate that DHMBA is an outstanding peroxyl radical scavenger, being about 15 times and 4 orders of magnitude better than Trolox for that purpose in lipid and aqueous media, respectively. It was also found to react faster with HOO(•) than other known antioxidants such as resveratrol and ascorbic acid. DHMBA is also predicted to be able to sequester Cu(II) ions, consequently inhibiting the OS induced by Cu(II)-ascorbate mixtures and downgrading the (•)OH production via the Haber-Weiss reaction. However, it is proposed that DHMBA is more efficient as a primary antioxidant (free radical scavenger), than as a secondary antioxidant (metal ion chelator). In addition, it was found that DHMBA can be efficiently regenerated in aqueous solution, at physiological pH. Such regeneration is expected to contribute to increase the antioxidant protection exerted by DHMBA. These results suggest that probably synthetic routes for this compound should be pursued, because albeit its abundance in nature is rather low, its antioxidant activity is exceptional.