Enhancing Open-World Bacterial Raman Spectra Identification by Feature Regularization for Improved Resilience against Unknown Classes

The combination of Deep Learning techniques and Raman spectroscopy shows great potential offering precise and prompt identification of pathogenic bacteria in clinical settings. However, the traditional closed-set classification approaches assume that all test samples belong to one of the known pathogens, and their applicability is limited since the clinical environment is inherently unpredictable and dynamic, unknown or emerging pathogens may not be included in the available catalogs. We demonstrate that the current state-of-the-art Neural Networks identifying pathogens through Raman spectra are vulnerable to unknown inputs, resulting in an uncontrollable false positive rate. To address this issue, first, we developed a novel ensemble of ResNet architectures combined with the attention mechanism which outperforms existing closed-world methods, achieving an accuracy of $87.8 \pm 0.1\%$ compared to the best available model's accuracy of $86.7 \pm 0.4\%$. Second, through the integration of feature regularization by the Objectosphere loss function, our model achieves both high accuracy in identifying known pathogens from the catalog and effectively separates unknown samples drastically reducing the false positive rate. Finally, the proposed feature regularization method during training significantly enhances the performance of out-of-distribution detectors during the inference phase improving the reliability of the detection of unknown classes. Our novel algorithm for Raman spectroscopy enables the detection of unknown, uncatalogued, and emerging pathogens providing the flexibility to adapt to future pathogens that may emerge, and has the potential to improve the reliability of Raman-based solutions in dynamic operating environments where accuracy is critical, such as public safety applications

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 The considerable disease burden caused by UTIs is severely exacerbated by an outdated diagnostic paradigm characterised by inaccuracy and delay.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

Optical methods for ultrafast screening of microorganisms

En aquesta tesi doctoral hem desenvolupat un mètode per la detecció i quantificació múltiple dels microorganismes més comuns que causen infeccions bacterianes amb una velocitat de detecció sense precedents a baix cost i alta sensibilitat. A més a més, fent servir fluids humans reals directament evitant així, els pretractaments tediosos de les mostres. El disseny del sistema està basat en augments d'intensitat del senyal obtingut per espectroscòpia SERS. Això s'aconsegueix utilitzant nanopartícules plasmòniques codificades i funcionalitzades amb elements de reconeixement biològics. D'aquesta manera, quan una mostra conté el patogen a identificar interactua amb els elements de reconeixement units a les nanopartícules, induint la seva acumulació en la superfície del microorganisme. Aquesta agregació de partícules a la membrana dels bacteris produeix espais molt petits entre les partícules fent que el senyal Raman s'amplifiqui en diversos ordres de magnitud respecte a les partícules soltes. Permetent així, la identificació de múltiples microorganismes a la vegada. La quantificació d'aquests, s'aconsegueix passant la mostra a través d'un dispositiu de micro-fluids amb una finestra de recol•lecció on un làser interroga i classifica els agregats a temps real. A més a més, també hem investigat els avantatges de fer servir aptàmers en lloc d'anticossos com a elements de reconeixement biològic. Aquest nou sistema de detecció de patògens obre interessants perspectives per al diagnòstic ràpid i econòmic d'infeccions bacterianes.En esta tesis doctoral hemos desarrollado un método para la detección y cuantificación múltiple de los microorganismos más comunes que causan infecciones bacterianas con una velocidad de detección sin precedentes a bajo coste y alta sensibilidad. Utilizando además, fluidos humanos reales directamente evitando así, pre-tratamientos tediosos de las muestras. El diseño del sistema está basado en aumentos de intensidad de la señal obtenida por espectroscopia SERS. Esto se logra utilizando nanopartículas plasmónicas codificadas y funcionalizadas con elementos de reconocimiento biológico. De esta manera, cuando una muestra que contiene el patógeno a identificar interactúa con los elementos de reconocimiento unidos a las nanopartículas, induce su acumulación en la superficie del microorganismo. Esta agregación de partículas en las membranas de las bacterias produce espaciados muy pequeños entre las partículas haciendo que la señal Raman se amplifique en varios órdenes de magnitud con respecto a las partículas sueltas. Permitiendo así la identificación de múltiples microorganismos a la vez. La cuantificación de los mismos, se logra pasando la muestra a través de un dispositivo de micro-fluidos con una ventana de recolección donde un láser interroga y clasifica los agregados en tiempo real. Además, también hemos investigado las ventajas de usar aptámeros frente a anticuerpos como elementos de reconocimiento biológico. Este nuevo sistema de detección de patógenos abre interesantes perspectivas para el diagnóstico rápido y barato de las infecciones bacterianas.This doctoral thesis intended to develop and optimize a method for multiplex detection and quantification of the most common microorganisms causing bacterial infections. This detection approach envisions to directly use different real human fluids avoiding thus, tedious pre-treatments of the samples with an unprecedented speed, low cost, and sensitivity. The design of the system is based on variations in the SERS intensity. This is accomplished using encoded plasmonic nanoparticles functionalized with bio-recognition elements. Consequently, when a sample containing the biological target to be identified interacts with the recognition elements attached to the nanoparticle, will induce an accumulation of them at the surface of the targeted microorganism. This particle aggregation on the bacteria membranes renders a dense array of inter-particle gaps in which the Raman signal is amplified by several orders of magnitude relative to the dispersed particles, enabling a multiplexed deterministic identification of the microorganisms. Quantification is achieved by passing the sample through a microfluidic device with a collection window where a laser interrogates and classifies the bacteria–nanoparticle aggregates in real time. Additionally, a comparison between two of the most common bio-recognition elements (antibodies and aptamers) was performed. This new pathogen detection system opens exciting prospects for fast inexpensive diagnosis of bacterial infections

Soft Methodology for Cost-and-error Sensitive Classification

Many real-world data mining applications need varying cost for different types of classification errors and thus call for cost-sensitive classification algorithms. Existing algorithms for cost-sensitive classification are successful in terms of minimizing the cost, but can result in a high error rate as the trade-off. The high error rate holds back the practical use of those algorithms. In this paper, we propose a novel cost-sensitive classification methodology that takes both the cost and the error rate into account. The methodology, called soft cost-sensitive classification, is established from a multicriteria optimization problem of the cost and the error rate, and can be viewed as regularizing cost-sensitive classification with the error rate. The simple methodology allows immediate improvements of existing cost-sensitive classification algorithms. Experiments on the benchmark and the real-world data sets show that our proposed methodology indeed achieves lower test error rates and similar (sometimes lower) test costs than existing cost-sensitive classification algorithms. We also demonstrate that the methodology can be extended for considering the weighted error rate instead of the original error rate. This extension is useful for tackling unbalanced classification problems.Comment: A shorter version appeared in KDD '1

Enhancing disease diagnosis: Biomedical applications of surface-enhanced raman scattering

© 2019 by the authors. Surface-enhanced Raman scattering (SERS) has recently gained increasing attention for the detection of trace quantities of biomolecules due to its excellent molecular specificity, ultrasensitivity, and quantitative multiplex ability. Specific single or multiple biomarkers in complex biological environments generate strong and distinct SERS spectral signals when they are in the vicinity of optically active nanoparticles (NPs). When multivariate chemometrics are applied to decipher underlying biomarker patterns, SERS provides qualitative and quantitative information on the inherent biochemical composition and properties that may be indicative of healthy or diseased states. Moreover, SERS allows for differentiation among many closely-related causative agents of diseases exhibiting similar symptoms to guide early prescription of appropriate, targeted and individualised therapeutics. This review provides an overview of recent progress made by the application of SERS in the diagnosis of cancers, microbial and respiratory infections. It is envisaged that recent technology development will help realise full benefits of SERS to gain deeper insights into the pathological pathways for various diseases at the molecular level

Emerging (Bio)Sensing Technology for Assessing and Monitoring Freshwater Contamination - Methods and Applications

Ecological Water Quality - Water Treatment and ReuseWater is life and its preservation is not only a moral obligation but also a legal requirement. By 2030, global demands will exceed more than 40 % the existing resources and more than a third of the world's population will have to deal with water shortages (European Environmental Agency [EEA], 2010). Climate change effects on water resources will not help. Efforts are being made throughout Europe towards a reduced and efficient water use and prevention of any further deterioration of the quality of water (Eurostat, European Comission [EC], 2010). The Water Framework Directive (EC, 2000) lays down provisions for monitoring, assessing and classifying water quality. Supporting this, the Drinking Water sets standards for 48 microbiological and chemical parameters that must be monitored and tested regularly (EC, 1998). The Bathing Water Directive also sets concentration limits for microbiological pollutants in inland and coastal bathing waters (EC, 2006), addressing risks from algae and cyanobacteria contamination and faecal contamination, requiring immediate action, including the provision of information to the public, to prevent exposure. With these directives, among others, the European Union [EU] expects to offer its citizens, by 2015, fresh and coastal waters of good quality

Improving diagnosis of pneumococcal disease by multiparameter testing and micro/nanotechnologies

The diagnosis and management of pneumococcal disease remains challenging, in particular in children who often are asymptomatic carriers, and in low-income countries with a high morbidity and mortality from febrile illnesses where the broad range of bacterial, viral and parasitic cases are in contrast to limited, diagnostic resources. Integration of multiple markers into a single, rapid test is desirable in such situations. Likewise, the development of multiparameter tests for relevant arrays of pathogens is important to avoid overtreatment of febrile syndromes with antibiotics. Miniaturization of tests through use of micro- and nanotechnologies combines several advantages: miniaturization reduces sample requirements, reduces the use of consumables and reagents leading to a reduction in costs, facilitates parallelization, enables point-of-care use of diagnostic equipment and even reduces the amount of potentially infectious disposables, characteristics that are highly desirable in most healthcare settings. This critical review emphasizes our vision on the importance of multiparametric testing for diagnosing pneumococcal infections in patients with fever and examines recent relevant developments in micro/nanotechnologies to achieve this goal

Recent trends in molecular diagnostics of yeast infections : from PCR to NGS

The incidence of opportunistic yeast infections in humans has been increasing over recent years. These infections are difficult to treat and diagnose, in part due to the large number and broad diversity of species that can underlie the infection. In addition, resistance to one or several antifungal drugs in infecting strains is increasingly being reported, severely limiting therapeutic options and showcasing the need for rapid detection of the infecting agent and its drug susceptibility profile. Current methods for species and resistance identification lack satisfactory sensitivity and specificity, and often require prior culturing of the infecting agent, which delays diagnosis. Recently developed high-throughput technologies such as next generation sequencing or proteomics are opening completely new avenues for more sensitive, accurate and fast diagnosis of yeast pathogens. These approaches are the focus of intensive research, but translation into the clinics requires overcoming important challenges. In this review, we provide an overview of existing and recently emerged approaches that can be used in the identification of yeast pathogens and their drug resistance profiles. Throughout the text we highlight the advantages and disadvantages of each methodology and discuss the most promising developments in their path from bench to bedside

Surface-enhanced Raman Spectroscopic Imaging of Bacteria within Fresh Produce In Situ

The growth curves for E. coli O157:H7 (#043888) are reported. We make the case that the onset of stationary growth is the optimal point at which a bacteria culture is considered suitable for quantitative Raman analyses. The optimal conditions for 3-mercaptophenylboronic acid coating of bacteria cells is also reported. Fundamental drawbacks of the status-quo approach have been elucidated and overcome, based on measurable improvements to the experimental methodology. This approach is shown to be suitable for the evaluation of bacterial rinse-washing efficacy by means of Raman light-scattering. The data were compared to label-free applications and the measurable differences between each approach were defined. Future use of 3-mercaptophenylboronic acid labelling for SERS analyses of bacteria should strictly follow the methods that we outlined within this paper. A real-time method to surveil mass bacterial communities directly in situ is also reported. The approach was successfully employed to indiscriminately monitor mass bacteria populations directly among plant tissue