Raman spectroscopy for point of care urinary tract infection diagnosis

Urinary tract infections (UTIs) are one of the most common bacterial infections experience by humans, with 150 million people suffering one or more UTIs each year. The massive scale at which UTIs occurs translates to a tremendous health burden comprising of patient morbidity and mortality, massive societal costs and a recognised contribution to expanding antimicrobial resistance. The considerable disease burden caused by UTIs is severely exacerbated by an outdated diagnostic paradigm characterised by inaccuracy and delay. Poor accuracy of screening tests, such as urinalysis, lead to misdiagnosis which in turn result in delayed recognition or overtreatment. Additionally, these screening tests fail to identify the causative pathogen, causing an overreliance on broad-spectrum antimicrobials which exacerbate burgeoning antimicrobial resistance. While diagnosis may be accurately confirmed though culture and sensitivity testing, the prolonged delay incurred negates the value of the information provided doing so. A novel diagnostic paradigm is required that that targets rapid and accurate diagnosis of UTIs, while providing real-time identification of the causative pathogen. Achieving this precision management is contingent on the development of novel diagnostic technologies that bring accurate diagnosis and pathogen classification to the point of care. The purpose of this thesis is to develop a technology that may form the core of a point-of-care diagnostic capable of delivering rapid and accurate pathogen identification direct from urine sample. Raman spectroscopy is identified as a technology with the potential to fulfil this role, primarily mediated though its ability to provide rapid biochemical phenotyping without requiring prior biomass expansion. Raman spectroscopy has demonstrated an ability to achieve pathogen classification through the analysis of inelastically scattered light arising from pathogens. The central challenge to developing a Raman-based diagnostic for UTIs is enhancing the weak bacterial Raman signal while limiting the substantial background noise. Developing a technology using Raman spectroscopy able to provide UTI diagnosis with uropathogen classification is contingent on developing a robust experimental methodology that harnesses the multitude of experimental and analytical parameters. The refined methodology is applied in a series of experimental works that demonstrate the unique Raman spectra of pathogens has the potential for accurate classification. Achieving this at a clinically relevant pathogen load and in a clinically relevant timeframe is, however, dependent on overcoming weak bacterial signal to improve signal-to-noise ratio. Surface-enhanced Raman spectroscopy (SERS) provides massive Raman signal enhancement of pathogens held in close apposition to noble metal nanostructures. Additionally, vacuum filtration is identified as a means of rapidly capturing pathogens directly from urine. SERS-active filters are developed by applying a gold nanolayer to commercially available membrane filters through physical vapour deposition. These SERS-active membrane filter perform multiple roles of capturing pathogens, separating them from urine, while providing Raman signal enhancement through SERS. The diagnostic and classification performance of SERS-active filters for UTIs is demonstrated to achieve rapid and accurate diagnosis of infected samples, with real-time uropathogen classification, using phantom urine samples, before piloting the technology using clinical urine samples. The Raman technology developed in this thesis will be further developed toward a clinically implementable technology capable of ameliorating the substantial burden of disease caused by UTIs.Open Acces

Long-term neurological symptoms after acute COVID-19 illness requiring hospitalization in adult patients: insights from the ISARIC-COVID-19 follow-up study

in this study we aimed to characterize the type and prevalence of neurological symptoms related to neurological long-COVID-19 from a large international multicenter cohort of adults after discharge from hospital for acute COVID-19

ISARIC-COVID-19 dataset: A Prospective, Standardized, Global Dataset of Patients Hospitalized with COVID-19

The International Severe Acute Respiratory and Emerging Infection Consortium (ISARIC) COVID-19 dataset is one of the largest international databases of prospectively collected clinical data on people hospitalized with COVID-19. This dataset was compiled during the COVID-19 pandemic by a network of hospitals that collect data using the ISARIC-World Health Organization Clinical Characterization Protocol and data tools. The database includes data from more than 705,000 patients, collected in more than 60 countries and 1,500 centres worldwide. Patient data are available from acute hospital admissions with COVID-19 and outpatient follow-ups. The data include signs and symptoms, pre-existing comorbidities, vital signs, chronic and acute treatments, complications, dates of hospitalization and discharge, mortality, viral strains, vaccination status, and other data. Here, we present the dataset characteristics, explain its architecture and how to gain access, and provide tools to facilitate its use