Raul Isea

 Dengue is a global arbovirus disease primarily carried by Aedes aegypti and Aedes albopictus mosquitoes. It has four serotypes (DENV1, DENV2, DENV3, and DENV4) and is classified into distinct genotypes. The epidemic is complicated by immunological interactions and viral lineage turnover. Neurological problems are commonly associated with DENV2 and DENV3, with DENV2 displaying the most severe symptoms. Direct viral invasion, host-mediated immune system reactions, or host-mediated metabolic alterations can all result in dengue-related neurological issues. The three dengue vaccinations and the significance of meta-analyses for genetic data will also be covered. Finally, establish a connection with the microRNAs associated with dengue fever, creating new opportunities for the creation of dengue treatment regimens involving microRNAs. Available at  https://openaccesspub.org/article/2133/jbd-24-5077.pdf

  The purpose of this work is to emphasize the possible benefits of a microRNA-based  diet in reducing the negative effects of a number of diseases, such as cancer, diabetes mellitus, obesity, hypertension, chronic respiratory conditions, and others. Available at  https://openaccesspub.org/ijn/article/2126

 El Diario de Santa María Faustina Kowalska registra las palabras de Cristo reveladas a ella entre 1934 y 1938. La frecuencia de las palabras usadas por Dios y Sor Faustina se determinó estadísticamente gracias a un análisis estilométrico donde se identificaron dos huellas lingüísticas diferentes. Para ello, analizamos diferentes párrafos del Diario en tres ediciones diferentes (Polaco, Español, e Inglés). Los resultados muestran un estilo lingüístico característico de la Hermana Faustina y otro diferente de Dios. Así que este documento confirma que hubo una conversación entre ellos dos.  Detalles en https://cvraulisea.wordpress.com/wp-content/uploads/2024/05/faustina_espanol.pdf

  ABSTRACT Saint Maria Faustina Kowalska’s Diary records the words of Christ revealed to her between 1934 and 1938. The frequency of the words used by God and Sor Faustina could be statistically determined by stylometric analysis since each has its own linguistic footprint. To do this, we analyze various parts of the Diary in three different editions (Polish, Spanish, and English). The results show a linguistic style characteristic of Sister Faustina and another to God. So this paper confirms that there was a conversation between the two. Acepted .

Citation : Isea, R. (2024). A Brief Summary of the Ways in Which our Genes Influence our Perceptions of Attraction and Love. Archives Clin Med Microbiol, 3(2), 01-03.  Abstract The connection between genetics and love has been a topic of interest for decades. Romantic love involves closeness, passion, and commitment, with distinct emotional, behavioral, hormonal, and neuropsychological characteristics. Neurobiological changes, immune system involvement, and genetic polymorphisms can help therapists and counselors provide personalized guidance and develop targeted interventions. Finally, it concludes that love is a combination of processes influenced by genes,  environment, and commitment. Available at https://www.opastpublishers.com/open-access-articles/a-brief-summary-of-the-ways-in-which-our-genes-influence-our-perceptions-of-attraction-and-love.pdf

 The electrocardiogram is a test that records the electrical activity of the heart, and has recently been shown that it can also detect other non-cardiovascular conditions, such as diabetes, measles, Alzheimer’s, arterial hypertension, fatty liver, hiperpotasemia, hypothyroidism, malaria, etc. For this reason, this paper proposes a computational technique to analyze and detect patterns based on the position and length of the segments between the peaks that make up the electrocardiogram signals, using deep learning techniques created by Artificial Intelligence. The program started by evaluating a database of heart arrhythmia signals, which included more than 120 electrocardiograms grouped between signals from normal and arrhythmia patients, employing convolutional neural network (CNN). The program had a prediction accuracy rate of 94.3% Citation: Raul Isea. Artificial Intelligence Can Diagnose any Disease from the Data of an Electrocardiogram. J Biomed Sci Biotech Res. 2024. 2(1): 1-4.