The Smad7 protein is negative regulator of the TGF-β signaling pathway, which is upregulated in patients with breast cancer. miRNAs regulate proteins expressions by arresting or degrading the mRNAs. The purpose of this work is to identify a miRNAs profile that regulates the expression of the mRNA coding for Smad7 in breast cancer using the data from patients with breast cancer obtained from the Cancer Genome Atlas Project. We develop an automatic search method based on genetic algorithms to find a predictive model based on deep neural networks (DNN) which fit the set of biological data and apply the Olden algorithm to identify the relative importance of each miRNAs. A computational model of non-linear regression is shown, based on deep neural networks that predict the regulation given by the miRNA target transcripts mRNA coding for Smad7 protein in patients with breast cancer, with R2 of 0.99 is shown and MSE of 0.00001. In addition, the model is validated with the results in vivo and in vitro experiments reported in the literature. The set of miRNAs hsa-mir-146a, hsa-mir-93, hsa-mir-375, hsa-mir-205, hsa-mir-15a, hsa-mir-21, hsa-mir-20a, hsa-mir-503, hsa-mir-29c, hsa-mir-497, hsa-mir-107, hsa-mir-125a, hsa-mir-200c, hsa-mir-212, hsa-mir-429, hsa-mir-34a, hsa-let-7c, hsa-mir-92b, hsa-mir-33a, hsa-mir-15b, hsa-mir-224, hsa-mir-185 and hsa-mir-10b integrate a profile that critically regulates the expression of the mRNA coding for Smad7 in breast cancer. We developed a genetic algorithm to select best features as DNN inputs (miRNAs). The genetic algorithm also builds the best DNN architecture by optimizing the parameters. Although the confirmation of the results by laboratory experiments has not occurred, the results allow suggesting that miRNAs profile could be used as biomarkers or targets in targeted therapies.

Gap junction (GJ) channels in invertebrates have been used to understand cell-to-cell communication in vertebrates. GJs are a common form of intercellular communication channels which connect the cytoplasm of adjacent cells. Dysregulation and structural alteration of the gap junction-mediated communication have been proven to be associated with a myriad of symptoms and tissue-specific pathologies. Animal models relying on the invertebrate nervous system have exposed a relationship between GJs and the formation of electrical synapses during embryogenesis and adulthood. The modulation of GJs as a therapeutic and clinical tool may eventually provide an alternative for treating tissue formation-related diseases and cell propagation. This review concerns the similarities between Hirudo medicinalis innexins and human connexins from nucleotide and protein sequence level perspectives. It also sets forth evidence of computational techniques applied to the study of proteins, sequences, and molecular dynamics. Furthermore, we propose machine learning techniques as a method that could be used to study protein structure, gap junction inhibition, metabolism, and drug development.

In recent years, there has been an increasing interest in developing multifunctional materials that can be used in different areas, such as artificial lighting. White light‐emitting diodes offer the possibility of efficient, safe, and reliable solid‐state lighting. This study presents a synthesis and characterisation process of a white‐light emitting phosphor based on the combination of rare earth‐activated Y ₂ SiO ₅ :Ce ³⁺ , Tb ³⁺ (YSO) and Sr ₂ Si ₅ N ₈ :Eu ²⁺ (SSN) phosphors. The YSO phosphor was prepared by combustion synthesis, whereas the SSN phosphor was prepared by solid‐state reaction under N ₂ /H ₂ atmosphere. Photoluminescence measurements showed the contribution of a broad‐band emission of Ce ³⁺ ions located in the blue region of the electromagnetic spectrum, a well‐defined green emission of Tb ³⁺ ions with the main peak located at λ _Em = 545 nm in the YSO phosphor, and a broad‐band emission in the red region due to Eu ²⁺ ions in the SSN phosphor. It is worth mentioning that excitation wavelength was in long UV radiation at 360 nm. In addition, a predictive model simulating and modelling the rare earths’ molar percentages to approximate to the D65 white light chromaticity coordinates is presented.