Mexico has one of the highest incidences of childhood leukemia worldwide and significantly higher mortality rates for this disease compared with other countries. One possible cause is the high prevalence of gene rearrangements associated with the etiology or with a poor prognosis of childhood acute lymphoblastic leukemia (ALL). The aims of this multicenter study were to determine the prevalence of the four most common gene rearrangements [ETV6-RUNX1, TCF3-PBX1, BCR-ABL1, andMLLrearrangements] and to explore their relationship with mortality rates during the first year of treatment in ALL children from Mexico City. Patients were recruited from eight public hospitals during 2010–2012. A total of 282 bone marrow samples were obtained at each child’s diagnosis for screening by conventional and multiplex reverse transcription polymerase chain reaction to determine the gene rearrangements. Gene rearrangements were detected in 50 (17.7%) patients.ETV6-RUNX1was detected in 21 (7.4%) patients,TCF3-PBX1in 20 (7.1%) patients,BCR-ABL1in 5 (1.8%) patients, andMLLrearrangements in 4 (1.4%) patients. The earliest deaths occurred at months 1, 2, and 3 after diagnosis in patients withMLL, ETV6-RUNX1, andBCR-ABL1gene rearrangements, respectively. Gene rearrangements could be related to the aggressiveness of leukemia observed in Mexican children.

SARS-CoV-2 has spread throughout the world since 2019, changing in its genome and leading to the appearance of new variants. This gave it different evolutionary advantages, such as greater infectivity and/or a greater ability to avoid the immune response, which could lead to an increased severity of COVID-19 cases. There is no consistent information about the viral load that occurs in infection with the different SARS-CoV-2 variants, hence, in this study we quantify the viral load of more than 16,800 samples taken from the Mexican population with confirmed diagnosis of COVID-19 and we analyze the relation between different demographic and disease variables. We detected that the viral load caused by different variants differs only in the first two days after the onset of symptoms, being higher when infections are caused by the delta variant and lower when caused by omicron. Furthermore, the viral load appears to be higher in outpatients compared to hospitalized patients or in cases of death. On the other hand, no differences were found in the viral load produced in vaccinated and unvaccinated patients, nor did it differ between genders.

The selection of Arabidopsis as a model organism played a pivotal role in advancing genomic science, firmly establishing the cornerstone of today ‘s plant molecular biology. Competing frameworks to select an agricultural- or ecological-based model species, or to decentralize plant science and study a multitude of diverse species, were selected against in favor of building core knowledge in a species that would facilitate genome-enabled research that could assumedly be transferred to other plants. Here, we examine the ability of models based on Arabidopsis gene expression data to predict tissue identity in other flowering plant species. Comparing different machine learning algorithms, models trained and tested on Arabidopsis data achieved near perfect precision and recall values using the K-Nearest Neighbor method, whereas when tissue identity is predicted across the flowering plants using models trained on Arabidopsis data, precision values range from 0.69 to 0.74 and recall from 0.54 to 0.64, depending on the algorithm used. Below-ground tissue is more predictable than other tissue types, and the ability to predict tissue identity is not correlated with phylogenetic distance from Arabidopsis . This suggests that gene expression signatures rather than marker genes are more valuable to create models for tissue and cell type prediction in plants. Our data-driven results highlight that, in hindsight, the assertion that knowledge from Arabidopsis is translatable to other plants is not always true. Considering the current landscape of abundant sequencing data and computational resources, it may be prudent to reevaluate the scientific emphasis on Arabidopsis and to prioritize the exploration of plant diversity.