This work aimed to identify and compare the bacterial patterns present in endometriotic lesions, eutopic endometrium and vaginal fluid from endometriosis patients with those found in the vaginal fluid and eutopic endometrium of control patients. Vaginal fluid, eutopic endometrium and endometriotic lesions were collected. DNA was extracted and the samples were analyzed to identify microbiome by high-throughput DNA sequencing of the 16S rRNA marker gene. Amplicon sequencing from vaginal fluid, eutopic endometrium and endometriotic lesion resulted in similar profiles of microorganisms, composed most abundantly by the genus Lactobacillus, Gardnerella, Streptococcus and Prevotella. No significant differences were found in the diversity analysis of microbiome profiles between control and endometriotic patients; however deep endometriotic lesions seems to present different bacterial composition, less predominant of Lactobacillus and with more abundant Alishewanella, Enterococcus and Pseudomonas.

Breast cancer (BC) is a complex disease and obesity is a well-known risk factor for its development, especially after menopause. Several studies have shown Single Nucleotide Polymorphisms (SNPs) linked to overweight and obesity, such as: rs1121980 (T/C) and rs9939609 (A/T) in Fat Mass and Obesity Associated gene (FTO) and rs17782313 (T/C) in Melanocortin 4 Receptor gene (MC4R). Thus, we aimed to investigate the association between these obesity-related SNPs and BC risk. One hundred BC patients and 148 healthy women from Santa Catarina, Brazil entered the study. SNPs were genotyped using Taqman assays. For statistical analyses SNPStats and SPSS softwares were used. Association analyses were performed by logistic regression and were adjusted for age and Body mass index (BMI). Multiple SNPs inheritance models (log-additive, dominant, recessive, codominant) were performed to determine odds ratios (ORs), assuming 95 % confidence interval (CI) and P value = 0.05 as the significance limit. When analyzed alone, FTO rs1121980 and rs9939609 did not show significant associations with BC development, however MC4R rs17782313 showed increased risk for BC even after adjustments (P-value = 0.032). Interestingly, the interaction of FTO and MC4R polymorphisms showed a powerful association with BC. We observed a 4.59-fold increased risk for woman who have the allele combination C/T/C (FTO rs1121980/FTO rs9939609/MC4R rs17782313) (P-value = 0.0011, adjusted for age and BMI). We found important and unpublished associations between these obesity-related genes and BC risk. These associations seem to be independent of their effect on BMI, indicating a direct role of the interaction between FTO and MC4R polymorphisms in BC development.

The aim of this work was to analyze and compare the bacterial communities of 663 samples from a Brazilian hospital by using high-throughput sequencing of the 16S rRNA gene. To increase taxonomic profiling and specificity of 16S-based identification, a strict sequence quality filtering process was applied for the accurate identification of clinically relevant bacterial taxa. Our results indicate that the hospital environment is predominantly inhabited by closely related species. A massive dominance of a few taxa in all taxonomic levels down to the genera was observed, where the ten most abundant genera in each facility represented 64.4% of all observed taxa, with a major predominance of Acinetobacter and Pseudomonas. The presence of several nosocomial pathogens was revealed. Co-occurrence analysis indicated that the present hospital microbial network had low connectedness, forming a clustered topology, but not structured among groups of nodes (i.e., modules). Furthermore, we were able to detect ecologically relevant relationships between specific microbial taxa, in particular, potential competition between pathogens and non-pathogens. Overall, these results provide new insight into different aspects of a hospital microbiome and indicate that 16S rRNA sequencing may serve as a robust one-step tool for microbiological identification and characterization of a wide range of clinically relevant bacterial taxa in hospital settings with a high resolution.

To minimize sample dilution effect on SARS-CoV-2 pool testing, we assessed analytical and diagnostic performance of a new methodology, namely swab pooling. In this method, swabs are pooled at the time of collection, as opposed to pooling of equal volumes from individually collected samples. Paired analysis of pooled and individual samples from 613 patients revealed 94 positive individuals. Having individual testing as reference, no false-positives or false-negatives were observed for swab pooling. In additional 18,922 patients screened with swab pooling (1,344 pools), mean Cq differences between individual and pool samples ranged from 0.1 (Cr.I. -0.98 to 1.17) to 2.09 (Cr.I. 1.24 to 2.94). Overall, 19,535 asymptomatic patients were screened using 4,400 RT-qPCR assays. This corresponds to an increase of 4.4 times in laboratory capacity and a reduction of 77% in required tests. Therefore, swab pooling represents a major alternative for reliable and large-scale screening of SARS-CoV-2 in low prevalence populations.

Several studies have shown the ubiquitous presence of bacteria in hospital surfaces, staff, and patients. Frequently, these bacteria are related to HAI (healthcare-associated infections) and carry antimicrobial resistance (AMR). These HAI-related bacteria contribute to a major public health issue by increasing patient morbidity and mortality during or after hospital stay. Bacterial high-throughput amplicon gene sequencing along with identification of AMR genes, as well as whole genome sequencing (WGS), are biotechnological tools that allow multiple-sample screening for a diversity of bacteria. In this paper, we used these methods to perform a one-year cross sectional profiling of bacteria and AMR genes in adult and neonatal intensive care units (ICU and NICU) in a Brazilian public, tertiary hospital. Our results showed high abundances of HAI-related bacteria such as S. epidermidis, S. aureus, K. pneumoniae, A. baumannii complex, E. coli, E. faecalis, and P. aeruginosa in patients and hospital surfaces. Most abundant AMR genes detected throughout ICU and NICU were mecA, bla CTX-M-1 group , bla SHV-like , and bla KPC-like. We found that NICU environment and patients were more widely contaminated with pathogenic bacteria than ICU. Patient samples, despite the higher bacterial load, have lower bacterial diversity than environmental samples in both units. Finally, we also identified contamination hotspots in the hospital environment showing constant frequencies of bacterial and AMR contamination throughout the year. Whole genome sequencing (WGS), 16S rRNA oligotypes, and AMR identification allowed a high-resolution characterization of the hospital microbiome profile.