Cryptococcus neoformans is an encapsulated yeast, aetiological agent of cryptococcosis, commonly associated with pigeon droppings and plant materials. The species has also been associated with tree hollows. The aim of the present work was to verify the presence of the yeast in hollows of living trees and identify the isolates obtained in varieties and serotypes. Three samples were collected from 18 trees of five different species totalling 54 samples. Wood samples were collected by scraping the surface of the trunks and the inner face of the hollows. Samples were inoculated on to agar Niger medium for fungal isolation. The serotypes were determined by PCR using specific primers. Among the 54 samples evaluated, two were positive for the presence of C. n. var. neoformans (serotype A and MATalpha). The trees belonged to Caesalpinia peltophoroides and Anadenanthera peregrina species. The results of this study suggest that decayed wood obtained from hollows of C. peltophoroides and A. peregrina can be used as natural habitat for C. n. var. neoformans.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) quickly spread worldwide, leading coronavirus disease 2019 (COVID-19) to hit pandemic level less than 4 months after the first official cases. Hence, the search for drugs and vaccines that could prevent or treat infections by SARS-CoV-2 began, intending to reduce a possible collapse of health systems. After 2 years, efforts to find therapies to treat COVID-19 continue. However, there is still much to be understood about the virus’ pathology. Tools such as transcriptomics have been used to understand the impact of SARS-CoV-2 on different cells isolated from various tissues, leaving datasets in the databases that integrate genes and differentially expressed pathways during SARS-CoV-2 infection. After retrieving transcriptome datasets from different human cells infected with SARS-CoV-2 available in the database, we performed an integrative analysis associated with deep learning algorithms to determine differentially expressed targets mainly after infection. The targets found represented a fructose transporter (GLUT5) and a component of proteasome 26s. These targets were then molecularly modeled, followed by molecular docking that identified potential inhibitors for both structures. Once the inhibition of structures that have the expression increased by the virus can represent a strategy for reducing the viral replication by selecting infected cells, associating these bioinformatics tools, therefore, can be helpful in the screening of molecules being tested for new uses, saving financial resources, time, and making a personalized screening for each infectious disease. Supplementary information The online version contains supplementary material available at 10.1007/s42770-022-00875-2.

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