Prevalence of Chagas disease and strongyloidiasis among HIV-infected Latin American immigrants in Italy – The CHILI study

INTRODUCTION: Screening HIV-positive migrants for neglected tropical diseases having potential for life-threatening reactivation, such as Chagas disease and strongyloidiasis is not widely implemented. We evaluated the prevalence of these infections among a large cohort of HIV-infected migrants from Latin America living in Italy. METHOD: Cross-sectional study evaluating the prevalence of Trypanosoma cruzi and Strongyloides stercoralis infections in HIV-infected migrants from Latin America enrolled in the Italian Cohort of Antiretroviral-Naïve patients (ICONA) between 1997 and 2018, based on serology performed on sera stored in the ICONA Foundation biobank. Screening for Chagas disease was performed using two commercial ELISA complemented by commercial Immunoblot and CLIA if discordant. Strongyloidiasis was evaluated using a commercial ELISA. RESULTS: 389 patients were analysed. Fifteen (3.86%) had at least one positive Chagas ELISA test. Prevalence of Chagas disease was 0.5% or 1.29% depending on the confirmatory technique. Serology for strongyloidiasis was positive in 16 (4.11%) patients. Only Nadir CD4+ T cell count was associated with discordant serology for Chagas disease (p = 0.046). CONCLUSIONS: The accuracy of seroassays for Chagas disease and strongyloidiasis in HIV-positive patients is unclear. To avoid missing potentially life-threatening infections, we suggest implementing additional diagnostic strategies in at-risk patients with inconclusive serology results

Gut microbiota composition in COVID-19 hospitalized patients with mild or severe symptoms

Background and aimCOVID-19, the infectious disease caused by SARS-CoV-2 virus that has been causing a severe pandemic worldwide for more than 2 years, is characterized by a high heterogeneity of clinical presentations and evolution and, particularly, by a varying severity of respiratory involvement. This study aimed to analyze the diversity and taxonomic composition of the gut microbiota at hospital admission, in order to evaluate its association with COVID-19 outcome. In particular, the association between gut microbiota and a combination of several clinical covariates was analyzed in order to characterize the bacterial signature associate to mild or severe symptoms during the SARS-CoV-2 infection.Materials and methodsV3–V4 hypervariable region of 16S rRNA gene sequencing of 97 rectal swabs from a retrospective cohort of COVID-19 hospitalized patients was employed to study the gut microbiota composition. Patients were divided in two groups according to their outcome considering the respiratory supports they needed during hospital stay: (i) group “mild,” including 47 patients with a good prognosis and (ii) group “severe,” including 50 patients who experienced a more severe disease due to severe respiratory distress that required non-invasive or invasive ventilation. Identification of the clusters of bacterial population between patients with mild or severe outcome was assessed by PEnalized LOgistic Regression Analysis (PELORA).ResultsAlthough no changes for Chao1 and Shannon index were observed between the two groups a significant greater proportion of Campylobacterota and Actinobacteriota at phylum level was found in patients affected by SARS-CoV-2 infection who developed a more severe disease characterized by respiratory distress requiring invasive or non-invasive ventilation. Clusters have been identified with a useful early potential prognostic marker of the disease evolution.DiscussionMicroorganisms residing within the gut of the patients at hospital admission, were able to significantly discriminate the clinical evolution of COVID-19 patients, in particular who will develop mild or severe respiratory involvement. Our data show that patients affected by SARS-CoV-2 with mild or severe symptoms display different gut microbiota profiles which can be exploited as potential prognostic biomarkers paving also the way to new integrative therapeutic approaches

Imported severe malaria and risk factors for intensive care: A single-centre retrospective analysis.

OBJECTIVES: This study aims to identify the risk factors for intensive care (IC) in severe malaria patients admitted to the "Lazzaro Spallanzani" National Institute for Infectious Diseases, Rome, Italy. METHODS: All patients with confirmed severe malaria and hospitalized between 2007 and 2015 were included in the analysis and stratified into two groups: those requiring IC and those who did not. Five prognostic malaria scores were estimated; clinical severity at IC unit admission was assessed using the Sequential Organ Failure Assessment and the quick-Sequential Organ Failure Assessment scores. Univariate and multivariate analysis were performed to assess factors independently associated to IC. RESULTS: A total of 98 severe malaria patients were included; 10 of them required IC. There were no deaths or sequelae. Patients requiring IC had higher severity scores. At the multivariate analysis, only the number of World Health Organization criteria and the aspartate aminotransferase value were independently associated with the need of IC. CONCLUSIONS: An early and accurate assessment of the severity score is essential for the management of severe malaria patients

A machine learning approach for early identification of patients with severe imported malaria

Abstract Background The aim of this study is to design ad hoc malaria learning (ML) approaches to predict clinical outcome in all patients with imported malaria and, therefore, to identify the best clinical setting. Methods This is a single-centre cross-sectional study, patients with confirmed malaria, consecutively hospitalized to the Lazzaro Spallanzani National Institute for Infectious Diseases, Rome, Italy from January 2007 to December 2020, were recruited. Different ML approaches were used to perform the analysis of this dataset: support vector machines, random forests, feature selection approaches and clustering analysis. Results A total of 259 patients with malaria were enrolled, 89.5% patients were male with a median age of 39 y/o. In 78.3% cases, Plasmodium falciparum was found. The patients were classified as severe malaria in 111 cases. From ML analyses, four parameters, AST, platelet count, total bilirubin and parasitaemia, are associated to a negative outcome. Interestingly, two of them, aminotransferase and platelet are not included in the current list of World Health Organization (WHO) criteria for defining severe malaria. Conclusion In conclusion, the application of ML algorithms as a decision support tool could enable the clinicians to predict the clinical outcome of patients with malaria and consequently to optimize and personalize clinical allocation and treatment

SARS-CoV-2 Isolation From Ocular Secretions of a Patient With COVID-19 in Italy With Prolonged Viral RNA Detection

Coronavirus disease 2019 (COVID-19), the disease caused by the novel severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) that originated in China in December 2019, was recently recognized as pandemic threat by the World Health Organization, with the potential of rapidly overloading health care systems and causing substantial mortality worldwide. Human-to-human transmission occurs mainly through respiratory droplets, but other routes are under investigation, because SARS-CoV-2 has been detected in several body fluids. So far, few data are available on ocular samples from patients with COVID-19, although conjunctivitis has been occasionally reported among COVID-19 symptoms, similar to infections caused by other human coronaviruses. During the SARS epidemic, eye exposure to infectious fluids was associated with an increased risk for SARS-CoV transmission to health care workers. Although SARS-CoV RNA was occasionally found in ocular specimens during the early phase of illness, its infectivity is unknown