A Two-Time Point Analysis of Gut Microbiota in the General Population of Buenos Aires and Its Variation Due to Preventive and Compulsory Social Isolation During the COVID-19 Pandemic.

The COVID-19 pandemic poses a great challenge to global public health. The extraordinary daily use of household disinfectants and cleaning products, social distancing and the loss of everyday situations that allow contact between individuals, have a direct impact on the transfer of microorganisms within the population. Together, these changes, in addition to those that occur in eating habits, can affect the composition and diversity of the gut microbiota. A two-time point analysis of the fecal microbiota of 23 Metropolitan Buenos Aires (BA) inhabitants was carried out, to compare pre-pandemic data and its variation during preventive and compulsory social isolation (PCSI) in 2020. To this end, 23 healthy subjects, who were previously studied by our group in 2016, were recruited for a second time during the COVID-19 pandemic, and stool samples were collected from each subject at each time point (n = 46). The hypervariable region V3-V4 of the 16S rRNA gene was high-throughput sequenced. We found significant differences in the estimated number of observed features (p < 0.001), Shannon entropy index (p = 0.026) and in Faith phylogenetic diversity (p < 0.001) between pre-pandemic group (PPG) vs. pandemic group (PG), being significantly lower in the PG. Although no strong change was observed in the core microbiota between the groups in this study, a significant decrease was observed during PCSI in the phylum Verrucomicrobia, which contributes to intestinal health and glucose homeostasis. Microbial community structure (beta diversity) was also compared between PPG and PG. The differences observed in the microbiota structure by unweighted UniFrac PCoA could be explained by six differential abundant genera that were absent during PCSI. Furthermore, putative functional genes prediction using PICRUSt infers a smaller predicted prevalence of genes in the intestinal tryptophan, glycine-betaine, taurine, benzoate degradation, as well as in the synthesis of vitamin B12 during PCSI. This data supports the hypothesis that the microbiome of the inhabitants of BA changed in the context of isolation during PCSI. Therefore, these results could increase the knowledge necessary to propose strategic nutraceutical, functional food, probiotics or similar interventions that contribute to improving public health in the post-pandemic era.

Covidex: An ultrafast and accurate tool for SARS-CoV-2 subtyping.

The epidemiological surveillance of SARS-CoV-2 by means of whole-genome sequencing has revealed the emergence and co-existence of multiple viral lineages or subtypes throughout the world. Moreover, it has been shown that several subtypes of this virus display particular phenotypes, such as increased transmissibility or reduced susceptibility to neutralizing antibodies, leading to the denomination of Variants of Interest (VOI) or Variants of Concern (VOC). Thus, subtyping of SARS-CoV-2 is a crucial step for the surveillance of this pathogen. Here, we present Covidex, an open-source, alignment-free machine learning subtyping tool. It is a shiny web app that allows an ultra-fast and accurate classification of SARS-CoV-2 genome sequences into the three most used nomenclature systems (GISAID, Nextstrain, Pango lineages). It also categorizes input sequences as VOI or VOC, according to current definitions. The program is cross-platform compatible and it is available via Source-Forge https://sourceforge.net/projects/covidex or via the web application http://covidex.unlu.edu.ar.

Mortality and evolution between community and hospital-acquired COVID-AKI.

BACKGROUND: Acute kidney injury (AKI) is associated with poor outcomes in COVID patients. Differences between hospital-acquired (HA-AKI) and community-acquired AKI (CA-AKI) are not well established. METHODS: Prospective, observational cohort study. We included 877 patients hospitalized with COVID diagnosis at two third-level hospitals in Mexico. Primary outcome was all-cause mortality at 28 days compared between COVID patients with CA-AKI and HA-AKI. Secondary outcomes included the need for KRT, and risk factors associated with the development of CA-AKI and HA-AKI. RESULTS: A total of 377 patients (33.7%) developed AKI. CA-AKI occurred in 202 patients (59.9%) and HA-AKI occurred in 135 (40.1%). Patients with CA-AKI had more significant comorbidities, including diabetes (52.4% vs 38.5%), hypertension (58.4% vs 39.2%), CKD (30.1% vs 14.8%), and COPD (5.9% vs 1.4%), than those with HA-AKI. Patients' survival without AKI was 87.1%, with CA-AKI it was 75.4%, and with HA-AKI it was 69.6%, log-rank test p < 0.001. Only age > 60 years (OR 1.12, 95% CI 1.06-1.18, p <0.001), COVID severity (OR 1.09, 95% CI 1.03-1.16, p = 0.002), the need in mechanical lung ventilation (OR 1.67, 95% CI 1.56-1.78, p <0.001), and HA-AKI stage 3 (OR 1.16, 95% CI 1.05-1.29, p = 0.003) had a significant increase in mortality. The presence of CKD (OR 1.48, 95% CI 1.391.56, p < 0.001), serum lymphocytes < 1000 µL (OR 1.03, 95% CI 1.00-1.07, p = 0.03), the need in mechanical lung ventilation (OR 1.06, 95% CI 1.02-1.11, p = 0.003), and CA-AKI stage 3 (OR 1.37, 95% CI 1.29-1.46, p < 0.001) were the only variables associated with a KRT start. CONCLUSIONS: We found that COVID patients who are complicated by CA-AKI have more comorbidities and worse biochemical parameters at the time of hospitalization than HA-AKI patients, but despite these differences, their probability of dying is similar.

Clinical Prediction Tool to Assess the Likelihood of a Positive SARS-Cov-2 (COVID-19) Polymerase Chain Reaction Test in Patients with Flu-like Symptoms.

INTRODUCTION: The clinical presentation of coronavirus disease 2019 (COVID-19) overlaps with many other common cold and influenza viruses. Identifying patients with a higher probability of infection becomes crucial in settings with limited access to testing. We developed a prediction instrument to assess the likelihood of a positive polymerase chain reaction (PCR) test, based solely on clinical variables that can be determined within the time frame of an emergency department (ED) patient encounter. METHODS: We derived and prospectively validated a model to predict SARS-CoV-2 PCR positivity in patients visiting the ED with symptoms consistent with the disease. RESULTS: Our model was based on 617 ED visits. In the derivation cohort, the median age was 36 years, 43% were men, and 9% had a positive result. The median time to testing from the onset of initial symptoms was four days (interquartile range [IQR]: 2-5 days, range 0-23 days), and 91% of all patients were discharged home. The final model based on a multivariable logistic regression included a history of close contact (adjusted odds ratio [AOR] 2.47, 95% confidence interval [CI], 1.29-4.7); fever (AOR 3.63, 95% CI, 1.931-6.85); anosmia or dysgeusia (AOR 9.7, 95% CI, 2.72-34.5); headache (AOR 1.95, 95% CI, 1.06-3.58), myalgia (AOR 2.6, 95% CI, 1.39-4.89); and dry cough (AOR 1.93, 95% CI, 1.02-3.64). The area under the curve (AUC) from the derivation cohort was 0.79 (95% CI, 0.73-0.85) and AUC 0.7 (95% CI, 0.61-0.75) in the validation cohort (N = 379). CONCLUSION: We developed and validated a clinical tool to predict SARS-CoV-2 PCR positivity in patients presenting to the ED to assist with patient disposition in environments where COVID-19 tests or timely results are not readily available.