ABSTRACT
Fungi exhibit three adverse effects on human health: inflammatory, allergic and toxic effects, these implications affect mainly immunodepressed patients. The objective of this work was to analyze the fungal microbiota of the ambient air of an Intensive Care Unit. Three collections were carried out in an Intensive Care Unit in the city of Rio Branco, Acre, Western Amazon, Brazil from March to May 2017. 126 Petri dishes were exposed with the culture medium Agar Sabouraud with chloramphenicol and Agar Mycosel, considering the distribution of the 21 air conditioners, split residential model. The plates were incubated for seven days at room temperature and the growth of Colony Forming Units was observed. Colony counting and isolation for the morphological characterization of the granted fungi was performed. After quantification, the concentration of fungi per cubic meters of air (CFU.m-3) was settled. The third collection had a larger number of colony forming units with 48.6%. In the total of the analyzed samples, filamentous fungi (85.5%) and yeasts (14.5%) were isolated. Thirteen genera of fungi were identified, with the most frequent filaments being Cladosporium spp. 33.0%, Aspergillus spp. 30.4% and Penicillium spp. 19.6%, and yeasts Candida spp. 52.6%, Trichosporon spp. 36.9%. The colony-forming unit per cubic meter (CFU.m-3) did not shown any difference between the Cores in the same collection period, however in the 1st and 3rd collection, Core 1 had the highest average. The fungal microbiota of this Unit presented thirteen different genera potentially pathogenic, revealing the need for monitoring microorganisms and prevention actions.
Subject(s)
Mycobiome , Humans , Brazil , Agar , Air Microbiology , Fungi , Intensive Care UnitsABSTRACT
MicroRNAs (miRNAs) are small non-coding RNAs that regulate target gene expression and hence play important roles in metabolic pathways. Recent studies have evidenced the interrelation of miRNAs with cell proliferation, differentiation, development, and diseases. Since they are involved in gene regulation, they are intrinsically related to metabolic pathways. This leads to questions that are particularly interesting for investigating medical and laboratorial applications. We developed an miRNApath online database that uses miRNA target genes to link miRNAs to metabolic pathways. Currently, databases about miRNA target genes (DIANA miRGen), genomic maps (miRNAMap) and sequences (miRBase) do not provide such correlations. Additionally, miRNApath offers five search services and a download area. For each search, there is a specific type of input, which can be a list of target genes, miRNAs, or metabolic pathways, which results in different views, depending upon the input data, concerning relationships between the target genes, miRNAs and metabolic pathways. There are also internal links that lead to a deeper analysis and cross-links to other databases with more detailed information. miRNApath is being continually updated and is available at http://lgmb.fmrp.usp.br/mirnapath.
Subject(s)
Computational Biology/methods , Databases, Nucleic Acid , Metabolic Networks and Pathways , MicroRNAs/genetics , Software , Animals , HumansABSTRACT
MicroRNAs (miRNAs) are small non-coding RNAs that regulate target gene expression and hence play important roles in metabolic pathways. Recent studies have evidenced the interrelation of miRNAs with cell proliferation, differentiation, development, and diseases. Since they are involved in gene regulation, they are intrinsically related to metabolic pathways. This leads to questions that are particularly interesting for investigating medical and laboratorial applications. We developed an miRNApath online database that uses miRNA target genes to link miRNAs to metabolic pathways. Currently, databases about miRNA target genes (DIANA miRGen), genomic maps (miRNAMap) and sequences (miRBase) do not provide such correlations. Additionally, miRNApath offers five search services and a download area. For each search, there is a specific type of input, which can be a list of target genes, miRNAs, or metabolic pathways, which results in different views, depending upon the input data, concerning relationships between the target genes, miRNAs and metabolic pathways. There are also internal links that lead to a deeper analysis and cross-links to other databases with more detailed information. miRNApath is being continually updated and is available at http://lgmb.fmrp.usp.br/mirnapath.
