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Emerging infectious diseases (EIDs) are newly emerging and reemerging infectious diseases. The National Institute of Allergy and Infectious Diseases identifies the following as emerging infectious diseases: SARS, MERS, COVID-19, influenza, fungal diseases, plague, schistosomiasis, smallpox, tick-borne diseases, and West Nile fever. The factors that should be taken into consideration are the genetic adaptation of microbial agents and the characteristics of the human host or environment. The new approach to identifying new possible pathogens will have to go through the One Health approach and omics integration data, which are capable of identifying high-priority microorganisms in a short period of time. New bioinformatics technologies enable global integration and sharing of surveillance data for rapid public health decision-making to detect and prevent epidemics and pandemics, ensuring timely response and effective prevention measures. Machine learning tools are being more frequently utilized in the realm of infectious diseases to predict sepsis in patients, diagnose infectious diseases early, and forecast the effectiveness of treatment or the appropriate choice of antibiotic regimen based on clinical data. We will discuss emerging microorganisms, omics techniques applied to infectious diseases, new computational solutions to evaluate biomarkers, and innovative tools that are useful for integrating omics data and electronic medical records data for the clinical management of emerging infectious diseases. [ABSTRACT FROM AUTHOR]

The present study aimed to investigate the pharyngeal and nasal microbiota composition in children with adenotonsillar hypertrophy (AH) and assess longitudinal alterations in both microbiota after a probiotic oral spray treatment. A cohort of 57 AH patients were enrolled and randomly assigned to the probiotic and placebo groups for a 5-month treatment course. Pharyngeal and nasal swabs were collected before and after treatment and analyzed by 16S rRNA-based metataxonomics and axenic cultures for pathobiont identification. 16S rRNA sequences from pharyngeal and nasal swabs of 65 healthy children (HC) were used as microbiota reference profiles. We found that the pharyngeal and nasal microbiota of AH children were similar. When compared to HC, we observed an increase of the genera Rothia , Granulicatella , Streptococcus , Neisseria , and Haemophilus , as well as a reduction of Corynebacterium , Pseudomonas, Acinetobacter, and Moraxella in both microbiota of AH patients. After probiotic treatment, we confirmed the absence of adverse effects and a reduction of upper respiratory tract infections (URTI). Moreover, the composition of pharyngeal microbiota was positively influenced by the reduction of potential pathobionts, like Haemophilus spp., with an increase of beneficial microbial metabolic pathways. Finally, the probiotic reduced the abundance of the pathobionts Streptococcus mitis and Gemella haemolysans in relation to AH severity. In conclusion, our results highlight the alterations of the pharyngeal and nasal microbiota associated with AH. Moreover, probiotic administration conferred protection against URTI and reduced the presence of potential pathobionts in patients with AH., Importance: Adenotonsillar hypertrophy (AH) is considered the main cause of breathing disorders during sleep in children. AH patients, after significant morbidity and often multiple courses of antibiotics, often proceed to tonsillectomy and/or adenoidectomy. Given the potential risks associated with these procedures, there is a growing interest in the use of nonsurgical adjuvant therapies, such as probiotics, that could potentially reduce their need for surgical intervention. In this study, we investigated the pharyngeal and nasal microbiota in patients with AH compared with healthy children. Furthermore, we tested the effects of probiotic spray administration on both disease symptoms and microbiota profiles, to evaluate the possible use of this microbial therapy as an adjuvant for AH patients.

This is the first study on gut microbiota (GM) in children affected by coronavirus disease 2019 (COVID-19). Stool samples from 88 patients with suspected severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and 95 healthy subjects were collected (admission: 3-7 days, discharge) to study GM profile by 16S rRNA gene sequencing and relationship to disease severity. The study group was divided in COVID-19 (68), Non-COVID-19 (16), and MIS-C (multisystem inflammatory syndrome in children) (4). Correlations among GM ecology, predicted functions, multiple machine learning (ML) models, and inflammatory response were provided for COVID-19 and Non-COVID-19 cohorts. The GM of COVID-19 cohort resulted as dysbiotic, with the lowest α-diversity compared with Non-COVID-19 and CTRLs and by a specific β-diversity. Its profile appeared enriched in Faecalibacterium , Fusobacterium , and Neisseria and reduced in Bifidobacterium , Blautia , Ruminococcus , Collinsella , Coprococcus , Eggerthella , and Akkermansia , compared with CTRLs ( p < 0.05). All GM paired-comparisons disclosed comparable results through all time points. The comparison between COVID-19 and Non-COVID-19 cohorts highlighted a reduction of Abiotrophia in the COVID-19 cohort ( p < 0.05). The GM of MIS-C cohort was characterized by an increase of Veillonella , Clostridium , Dialister , Ruminococcus , and Streptococcus and a decrease of Bifidobacterium , Blautia , Granulicatella , and Prevotella , compared with CTRLs. Stratifying for disease severity, the GM associated to "moderate" COVID-19 was characterized by lower α-diversity compared with "mild" and "asymptomatic" and by a GM profile deprived in Neisseria , Lachnospira , Streptococcus , and Prevotella and enriched in Dialister , Acidaminococcus , Oscillospora , Ruminococcus , Clostridium , Alistipes , and Bacteroides. The ML models identified Staphylococcus , Anaerostipes , Faecalibacterium , Dorea , Dialister , Streptococcus , Roseburia , Haemophilus , Granulicatella , Gemmiger , Lachnospira , Corynebacterium , Prevotella , Bilophila , Phascolarctobacterium , Oscillospira , and Veillonella as microbial markers of COVID-19. The KEGG ortholog (KO)-based prediction of GM functional profile highlighted 28 and 39 KO-associated pathways to COVID-19 and CTRLs, respectively. Finally, Bacteroides and Sutterella correlated with proinflammatory cytokines regardless disease severity. Unlike adult GM profiles, Faecalibacterium was a specific marker of pediatric COVID-19 GM. The durable modification of patients' GM profile suggested a prompt GM quenching response to SARS-CoV-2 infection since the first symptoms. Faecalibacterium and reduced fatty acid and amino acid degradation were proposed as specific COVID-19 disease traits, possibly associated to restrained severity of SARS-CoV-2-infected children. Altogether, this evidence provides a characterization of the pediatric COVID-19-related GM., Competing Interests: Author GM, VG, and SG are employed by GenomeUp SRL, Viale Pasteur, 6, 00144, Rome, Italy. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Romani, Del Chierico, Macari, Pane, Ristori, Guarrasi, Gardini, Pascucci, Cotugno, Perno, Rossi, Villani, Bernardi, Campana, Palma, Putignani and the CACTUS Study Team.)