Although some Countries in South America have implemented laboratory and patient management protocols for the new coronavirus, the lack of access to basic sanitation and hygiene measures, as well as the lack of drugs and vaccines, has significantly interfered with the epidemiological mechanics of the virus, emphasizing its implications. Therefore, trying to understand the evolutionary aspects of the virus, emerges as another strategy that can help in the most varied measures of prophylaxis. In this work, we evaluated the levels of genetic diversity in 38 complete Genomes of SARS-CoV-2 from six countries in South America, using specific methodologies for paired FST, AMOVA, mismatch, demographic and spatial expansions, molecular diversity and for the time of evolutionary divergence. The analyses showed non-significant evolutionary divergences within and between the six countries, as well as a significant similarity to the time of genetic evolutionary divergence between all populations. Thus, it seems safe to affirm that we will find similar results for the other Countries of South America, reducing speculation about the existence of rapid and silent mutations that, although there are as we have shown in this work, do not increase, until this moment, the genetic variability of the Virus, a fact that would hinder the work with molecular targets for vaccines and drugs in general.

Biosensors are small devices that use biological reactions to detect target analytes. Such devices combine a biological component with a physical transducer, which converts bio-recognition processes into measurable signals. Its use brings a number of advantages, as they are highly sensitive and selective, relatively easy in terms of development, as well as accessible and ready to use. Biosensors can be of direct detection, using a non-catalytic ligand, such as cell receptors and antibodies, or indirect detection, in which there is the use of fluorescently marked antibodies or catalytic elements, such as enzymes. They also appear as bio-affinity devices, depending only on the selective binding of the target analyte to the ligative attached to the surface (e.g., oligonucleotide probe). The objectives were to evaluate the levels of genetic diversity existing in fragments of the TP53 gene deposited in molecular databases and to study its viability as a biosensor in the detection of breast cancer. The methodology used was to recover and analyze 301 sequences of a fragment of the TP53 gene of humans from GENBANK, which, after being aligned with the MEGA software version 6.06, were tested for the phylogenetic signal using TREE-PUZZLE 5.2. Trees of maximum likelihood were generated through PAUP version 4.0b10 and the consistency of the branches was verified with the bootstrap test with 1000 pseudo-replications. After aligning, 783 of the 791 sites remained conserved. The maximum likelihood had a slight manifestation since the gamma distribution used 05 categories + G for the evolutionary rates between sites with (0.90 0.96, 1.00, 1.04 and 1.10 substitutions per site). To estimate ML values, a tree topology was automatically computed with a maximum Log of −1058,195 for this calculation. All positions containing missing gaps or data were deleted, leaving a total of 755 sites in the final dataset. The evolutionary history was represented by consensus trees generated by 500 replications, which according to neighbor-join and BioNJ algorithms set up a matrix with minimal distances between haplotypes, corroborating the high degree of conservation for the TP53 gene. GENE TP53 seems to be a strong candidate in the construction of Biosensors for breast cancer diagnosis in human populations.