Potential use of artificial intelligence for vaginal swab analysis in the assessment of common genital disorders: a pilot study.

Genital disorders, such as vulvo-vaginal candidiasis (VVC), bacterial vaginosis (BV), and aerobic vaginitis (AV), are very common among fertile women and negatively impact their reproductive and relational life. Vaginal culture can help in the diagnostic workflow of these conditions. Recently, culture-based techniques have taken advantages of up-front specimen processing units, which also include a digital imaging system to record images of plates at programmable time points. In this proof-of-concept study, we assessed the characteristics of digital plate images of vaginal swabs plated by WASPLab system into different media, in order to detect microbial growth morphotypes specific for each genital disorder. A total of 104 vaginal specimens were included: 62 cases of normal lactobacilli-dominated flora, 12 of BV, 16 of VVC, and 14 of AV were analysed. Vaginal specimens were plated by WASPLab system into different chromogenic media and blood agar plates. Plate images were taken automatically by the digital imager at 38 h post-inoculation. We found that each genital condition was characterized by specific morphotypes in terms of microbial growth and colony colour, thus allowing the potential use of artificial intelligence not only to assess the presence of specific microbial genera/species but also to 'categorize' peculiar clinical conditions.

Clonal Dissemination of Candida auris Clinical Isolates in Northern Italy, 2021.

Candida auris is a concerning pathogen in health care due to its ability to spread in medical settings. In this study, we characterized the genome of three C. auris clinical isolates collected in the Emilia-Romagna region of Northeastern Italy from January 2020 to May 2021. Whole-genome sequencing was performed using Illumina iSeq 100 and Oxford Nanopore MinION systems. Genomes were assembled with Flye. Phylogenetic analysis was carried out with RaxML. The ERG11, TAC1b, and FKS1 genes were examined for known substitutions associated with resistance to azoles and caspofungin using Diamond. All three C. auris isolates belonged to clade I (South Asian lineage) and showed high minimum inhibitory concentrations for fluconazole. Two of the three isolates were closely related to the first Italian index case of C. auris occurred in the 2019 and carried similar mutations associated to azole resistance. The third isolate showed a greater phylogenetic distance from these strains and had a different genetic determinant not previously seen in Italy. Our data suggest that two C. auris clinical isolates may have been epidemiologically related to the first outbreak previously observed in Italy, while the remaining isolate may have originated from a different source. Further research is needed to understand C. auris transmission and resistance and to control its spread.

Critically ill patients with COVID-19 show lung fungal dysbiosis with reduced microbial diversity in patients colonized with Candida spp.

BACKGROUND: The COVID-19 pandemic has intensified interest in how the infection affects the lung microbiome of critically ill patients and how it contributes to acute respiratory distress syndrome (ARDS). We aimed to characterize the lower respiratory tract mycobiome of critically ill patients with COVID-19 in comparison to patients without COVID-19. METHODS: We performed an internal transcribed spacer 2 (ITS2) profiling with the Illumina MiSeq platform on 26 respiratory specimens from patients with COVID-19 as well as from 26 patients with non-COVID-19 pneumonia. RESULTS: Patients with COVID-19 were more likely to be colonized with Candida spp. ARDS was associated with lung dysbiosis characterized by a shift to Candida species colonization and a decrease of fungal diversity. We also observed higher bacterial phylogenetic distance among taxa in colonized patients with COVID-19. In patients with COVID-19 not colonized with Candida spp., ITS2 amplicon sequencing revealed an increase of Ascomycota unassigned spp. and 1 Aspergillus spp.-positive specimen. In addition, we found that corticosteroid therapy was frequently associated with positive Galactomannan cell wall component of Aspergillus spp. among patients with COVID-19. CONCLUSION: Our study underpins that ARDS in patients with COVID-19 is associated with lung dysbiosis and that an increased density of Ascomycota unassigned spp. is present in patients not colonized with Candida spp.

Machine learning-based typing of Salmonella enterica O-serogroups by the Fourier-Transform Infrared (FTIR) Spectroscopy-based IR Biotyper system.

BACKGROUND: Salmonella enterica is among the major burdens for public health at global level. Typing of salmonellae below the species level is fundamental for different purposes, but traditional methods are expensive, technically demanding, and time-consuming, and therefore limited to reference centers. Fourier transform infrared (FTIR) spectroscopy is an alternative method for bacterial typing, successfully applied for classification at different infra-species levels. AIM: This study aimed to address the challenge of subtyping Salmonella enterica at O-serogroup level by using FTIR spectroscopy. We applied machine learning to develop a novel approach for S. enterica typing, using the FTIR-based IR Biotyper® system (IRBT; Bruker Daltonics GmbH & Co. KG, Germany). We investigated a multicentric collection of isolates, and we compared the novel approach with classical serotyping-based and molecular methods. METHODS: A total of 958 well characterized Salmonella isolates (25 serogroups, 138 serovars), collected in 11 different centers (in Europe and Japan), from clinical, environmental and food samples were included in this study and analyzed by IRBT. Infrared absorption spectra were acquired from water-ethanol bacterial suspensions, from culture isolates grown on seven different agar media. In the first part of the study, the discriminatory potential of the IRBT system was evaluated by comparison with reference typing method/s. In the second part of the study, the artificial intelligence capabilities of the IRBT software were applied to develop a classifier for Salmonella isolates at serogroup level. Different machine learning algorithms were investigated (artificial neural networks and support vector machine). A subset of 88 pre-characterized isolates (corresponding to 25 serogroups and 53 serovars) were included in the training set. The remaining 870 samples were used as validation set. The classifiers were evaluated in terms of accuracy, error rate and failed classification rate. RESULTS: The classifier that provided the highest accuracy in the cross-validation was selected to be tested with four external testing sets. Considering all the testing sites, accuracy ranged from 97.0% to 99.2% for non-selective media, and from 94.7% to 96.4% for selective media. CONCLUSIONS: The IRBT system proved to be a very promising, user-friendly, and cost-effective tool for Salmonella typing at serogroup level. The application of machine learning algorithms proved to enable a novel approach for typing, which relies on automated analysis and result interpretation, and it is therefore free of potential human biases. The system demonstrated a high robustness and adaptability to routine workflows, without the need of highly trained personnel, and proving to be suitable to be applied with isolates grown on different agar media, both selective and unselective. Further tests with currently circulating clinical, food and environmental isolates would be necessary before implementing it as a potentially stand-alone standard method for routine use.

Pseudozyma aphidis bloodstream infection in a patient with aggressive lymphoma and a history of intravenous drug use: Case report and review of the literature.

Pseudozyma aphidis is an environmental fungus which causes opportunistic infections in immunocompromised patients. Here we report the case of a 54-year-old, intravenous drug user woman, newly diagnosed to have an aggressive lymphoma, who developed a bloodstream infection caused by P. aphidis treated successfully with amphotericin-B therapy. The precise identification was assessed by sequencing. We propose to consider intravenous drug use as a risk factor for invasive infections due to this environmental yeast.

Respiratory bacterial co-infections in intensive care unit-hospitalized COVID-19 patients: Conventional culture vs BioFire FilmArray pneumonia Plus panel.

The prevalence and microbiology of concomitant respiratory bacterial infections in patients with SARS-CoV-2 infection are not yet fully understood. In this retrospective study, we assessed respiratory bacterial co-infections in lower respiratory tract samples taken from intensive care unit-hospitalized COVID-19 patients, by comparing the conventional culture approach to an innovative molecular diagnostic technology. A total of 230 lower respiratory tract samples (i.e., bronchial aspirates or bronchoalveolar lavages) were taken from 178 critically ill COVID-19 patients. Each sample was processed by a semi-quantitative culture and by a multiplex PCR panel (FilmArray Pneumonia Plus panel), allowing rapid detection of a wide range of clinically relevant pathogens and a limited number of antimicrobial resistance markers. More than 30% of samples showed a positive bacterial culture, with Pseudomonas aeruginosa, Klebsiella pneumoniae and Staphylococcus aureus the most detected pathogens. FilmArray showed an overall sensitivity and specificity of 89.6% and 98.3%, respectively, with a negative predictive value of 99.7%. The molecular test significantly reduced the turn-around-time (TAT) and increased the rates of microbial detection. Most cases missed by culture were characterized by low bacterial loads (104-105 copies/mL). FilmArray missed a list of pathogens not included in the molecular panel, especially Stenotrophomonas maltophilia (8 cases). FilmArray can be useful to detect bacterial pathogens in lower respiratory tract specimens of COVID-19 patients, with a significant decrease of TAT. The test is particularly useful to rule out bacterial co-infections and avoid the inappropriate prescription of antibiotics.

Metabolic profiling of Candida clinical isolates of different species and infection sources.

Candida species are the most common cause of opportunistic fungal infections. Rapid identification and novel approaches for the characterization of these fungi are of great interest to improve the diagnosis and the knowledge about their pathogenic properties. This study aimed to characterize clinical isolates of Candida spp. by proteomics (MALDI-TOF MS) and metabolomics (1H-NMR), and to correlate their metabolic profiles with Candida species, source of infection and different virulence associated parameters. In particular, 49 Candida strains from different sources (blood, n = 15; vagina, n = 18; respiratory tract, n = 16), belonging mainly to C. albicans complex (61%), C. glabrata (20%) and C. parapsilosis (12%) species were used. Several extracellular and intracellular metabolites showed significantly different concentrations among isolates recovered from different sources of infection, as well as among different Candida species. These metabolites were mainly related to the glycolysis or gluconeogenesis, tricarboxylic acid cycle, nucleic acid synthesis and amino acid and lipid metabolism. Moreover, we found specific metabolic fingerprints associated with the ability to form biofilm, the antifungal resistance (i.e. caspofungin and fluconazole) and the production of secreted aspartyl proteinase. In conclusion, 1H-NMR-based metabolomics can be useful to deepen Candida spp. virulence and pathogenicity properties.

Occurrence of Albifimbria verrucaria in the Blood of a Female Child With Neuroblastoma.

We report for the first time the occurrence of a filamentous fungus, Albifimbria verrucaria, in the blood of a pediatric neuroblastoma patient. The Albifimbria genus comprises common soil-inhabiting and saprophytic fungi and has been isolated as a plant pathogen in Northern and Southern Italy. As a human pathogen, A. verrucaria has been implicated in keratitis and can produce trichothecene toxins, which are weakly cytotoxic for mammalian cell lines. A. verrucaria was isolated from blood during the follow-up of a previous coagulase-negative Staphylococcus catheter-related infection. Lung nodules, compatible with fungal infection, had been observed on a CT scan 6 months earlier; they still persist. Possible routes of transmission were considered to be airborne, catheter related, or transfusion dependent, as the patient had undergone platelet and red blood cell transfusions during rescue chemotherapy. No filamentous fungi were isolated from sputum or CVCs. In conclusion, we describe an unprecedented fungemia caused by A. verrucaria and show how an unexpected pathogen may be acquired from the environment by patients at high risk due to immunosuppression. The route of transmission remains unknown.

Benefits and challenges of incorporating citizen science into university education.

A common feature of many citizen science projects is the collection of data by unpaid contributors with the expectation that the data will be used in research. Here we report a teaching strategy that combined citizen science with inquiry-based learning to offer first year university students an authentic research experience. A six-year partnership with the Australian phenology citizen science program ClimateWatch has enabled biology students from the University of Western Australia to contribute phenological data on plants and animals, and to conduct the first research on unvalidated species datasets contributed by public and university participants. Students wrote scientific articles on their findings, peer-reviewed each other's work and the best articles were published online in a student journal. Surveys of more than 1500 students showed that their environmental engagement increased significantly after participating in data collection and data analysis. However, only 31% of students agreed with the statement that "data collected by citizen scientists are reliable" at the end of the project, whereas the rate of agreement was initially 79%. This change in perception was likely due to students discovering erroneous records when they mapped data points and analysed submitted photographs. A positive consequence was that students subsequently reported being more careful to avoid errors in their own data collection, and making greater efforts to contribute records that were useful for future scientific research. Evaluation of our project has shown that by embedding a research process within citizen science participation, university students are given cause to improve their contributions to environmental datasets. If true for citizen scientists in general, enabling participants as well as scientists to analyse data could enhance data quality, and so address a key constraint of broad-scale citizen science programs.