Vibrational Biospectroscopy in the Clinical Setting: Exploring the Impact of New Advances in the Field of Immunology

The investigation of pathological diseases largely relies on laboratory examinations. The ability to identify and characterise cells is an essential process for clinicians to reach an accurate diagnosis and inform appropriate treatments. There is currently a gap between the advancement of scientific knowledge on cellular and molecular pathways and the development of novel techniques capable of detecting subtle cellular changes associated with disease. Biospectroscopy is the use of spectroscopy techniques to investigate biological materials. Within a biological sample, important molecules such as lipids, carbohydrates, nucleic acids, and proteins are held together by chemical bonds; these bonds will vibrate following excitation with infrared light. By measuring the vibrational energy of each molecule present in a biological sample, a unique spectrum, known as the “molecular fingerprint” is generated. As disease-related changes in biological samples will be reflected in the molecular fingerprint, biospectroscopy is a well-placed candidate for the investigation of disease. Biospectroscopy has been gaining wider acceptance and application in the clinical setting over the past decade; however, it has yet to reach diagnostic laboratories and healthcare clinics as a routine platform for clinical assessment. Immunological disorders are complex, often demonstrating interaction across multiple molecular pathways which results in delayed diagnosis. Vibrational spectroscopy is being applied in many fields, and here we present a review of its use in cellular immunology. Potential benefits, including an enhanced definition of molecular processes and the use of spectroscopy in disease diagnosis, monitoring, and treatment response, are discussed. The translation of vibrational spectroscopic techniques into clinical practice offers rapid, noninvasive, and inexpensive methods to obtain information on the molecular composition of biological samples. The potential clinical benefits of biospectroscopy include providing a more prompt and accurate disease diagnosis, thus improving patient care and resulting in better health outcomes

Component-resolved diagnostics in the clinical and laboratory investigation of allergy

The diagnosis and management of allergy is complex; the clinical symptoms associated with allergic reactions span a broad spectrum of severity, from mild hay fever-type symptoms through to life-threatening anaphylaxis. Obtaining an allergy-focused clinical history is therefore vital for identifying possible allergic triggers and directing testing. However, this focus could be changing as scientific and technological advances have paved the way for developments within in vitro testing for allergy. With knowledge of allergens at the molecular level expanding, there are now the facilities to characterize the sensitization profiles of allergy sufferers and determine the specific molecules (or components) against which the allergen-inducing immunoglobulin type E proteins have been produced. This technology is termed component-resolved diagnostics. We know that accurate identification of immunoglobulin type E specificity, the source of the causative allergen, and knowledge of potential allergic cross-reactivities are required for optimal clinical management of allergy patients. These factors can make allergy a diagnostic challenge outside of a specialist centre, and contribute to the difficulties associated with requesting and interpreting allergy tests. The incorporation of component-resolved diagnostics into current practice has provided a platform for patient-tailored risk stratification and improved the application of allergen-specific immunotherapy, revolutionizing specialist management of these patients. This review discusses the roles of each type of testing in allergy management and predictions for future pathway

Classification of Systemic Lupus Erythematosus Using Raman Spectroscopy of Blood and Automated Computational Detection Methods: A Novel Tool for Future Diagnostic Testing

The aim of this study was to explore the proof of concept for using Raman spectroscopy as a diagnostic platform in the setting of systemic lupus erythematosus (SLE). We sought to identify unique Raman signatures in serum blood samples to successfully segregate SLE patients from healthy controls (HC). In addition, a retrospective audit was undertaken to assess the clinical utility of current testing platforms used to detect anti-double stranded DNA (dsDNA) antibodies (n = 600). We examined 234 Raman spectra to investigate key variances between SLE patients (n = 8) and HC (n = 4). Multi-variant analysis and classification model construction was achieved using principal component analysis (PCA), PCA-linear discriminant analysis and partial least squares-discriminant analysis (PLS-DA). We achieved the successful segregation of Raman spectra from SLE patients and healthy controls (p-value < 0.0001). Classification models built using PLS-DA demonstrated outstanding performance characteristics with 99% accuracy, 100% sensitivity and 99% specificity. Twelve statistically significant (p-value < 0.001) wavenumbers were identified as potential diagnostic spectral markers. Molecular assignments related to proteins and DNA demonstrated significant Raman intensity changes between SLE and HC groups. These wavenumbers may serve as future biomarkers and offer further insight into the pathogenesis of SLE. Our audit confirmed previously reported inconsistencies between two key methodologies used to detect anti-dsDNA, highlighting the need for improved laboratory testing for SLE. Raman spectroscopy has demonstrated powerful performance characteristics in this proof-of-concept study, setting the foundations for future translation into the clinical setting

Seroprevalence of SARS-CoV-2 infection in healthcare workers in a large teaching hospital in the North West of England: a period prevalence survey

Objectives: Since its emergence in late 2019, SARS-CoV-2 has caused a global pandemic that has significantly challenged healthcare systems. Healthcare workers have previously been shown to have experienced higher rates of infection than the general population. We aimed to assess the extent of infection in staff working in our healthcare setting. Design: A retrospective analysis of antibody results, compared with staff demographic data, and exposure to patients with COVID-19 infection. Setting: A large teaching hospital in the North West of England. Participants: 4474 staff in diverse clinical and non-patient facing roles who volunteered for SARS-CoV-2 antibody testing by the Roche Elecsys assay between 29 May and 4 July 2020. Results: Seroprevalence was 17.4%. Higher rates were seen in Asian/Asian British (OR 1.61, 95% CI 1.27 to 2.04) and Black/Black British (OR 2.08, 95% CI 1.25 to 3.45) staff. Staff working in any clinical location were more likely to be seropositive (OR 2.68, 95% 2.27 to 3.15). Staff were at an increased risk of seropositivity as the ‘per 100 COVID-19 bed-days change’ increased in the clinical area in which they worked (OR 1.12, 95% 1.10 to 1.14). Staff working in critical care were no more likely to have detectable antibodies than staff working in non-clinical areas. Symptoms compatible with COVID-19 were reported in 41.8% and antibodies were detected in 30.7% of these individuals. In staff who reported no symptoms, antibodies were detected in 7.7%. In all staff who had detectable antibodies, 25.2% reported no symptoms. Conclusions: Staff working in clinical areas where patients with COVID-19 were nursed were more likely to have detectable antibodies. The relationship between seropositivity in healthcare workers and the increase in ‘per 100 COVID-19 bed-days’ of the area in which they worked, although statistically significant, was weak, suggesting other contributing factors to the risk profile. Of staff with detectable antibodies and therefore evidence of prior infection, a quarter self-reported that they had experienced no compatible symptoms. This has implications for potential unrecorded transmission in both staff and patients

New approach to investigate Common Variable Immunodeficiency patients using spectrochemical analysis of blood

Common variable immune deficiency (CVID) is a primary immunodeficiency disease, characterized by hypogammaglobulinemia, recurrent infections and various complications. The clinical heterogeneity of CVID has hindered identification of an underlying immune defect; diagnosis relies on clinical judgement, alongside evidence-based criteria. The lack of pathognomonic clinical or laboratory features leads to average diagnostic delays of 5 years or more from the onset. Vibrational spectroscopic techniques such as Fourier-transform infrared (FTIR) spectroscopy have recently gained increasing clinical importance, being rapid-, non-invasive and inexpensive methods to obtain information on the content of biological samples. This has led us to apply FTIR spectroscopy to the investigation of blood samples from a cohort of CVID patients; revealing spectral features capable of stratifying CVID patients from healthy controls with sensitivities and specificities of 97% and 93%, respectively for serum, and 94% and 95%, respectively for plasma. Furthermore we identified several discriminating spectral biomarkers; wavenumbers in regions indicative of nucleic acids (984 cm−1, 1053 cm−1, 1084 cm−1, 1115 cm−1, 1528 cm−1, 1639 cm−1), and a collagen-associated biomarker (1528 cm−1), which may represent future candidate biomarkers and provide new knowledge on the aetiology of CVID. This proof-of-concept study provides a basis for developing a novel diagnostic tool for CVID

Raman spectroscopic techniques to detect ovarian cancer biomarkers in blood plasma

Robust diagnosis of ovarian cancer is crucial to improve patient outcomes. The lack of a single and accurate diagnostic approach necessitates the advent of novel methods in the field. In the present study, two spectroscopic techniques, Raman and surface-enhanced Raman spectroscopy (SERS) using silver nanoparticles, have been employed to identify signatures linked to cancer in blood. Blood plasma samples were collected from 27 patients with ovarian cancer and 28 with benign gynecological conditions, the majority of which had a prolapse. Early ovarian cancer cases were also included in the cohort (n = 17). The derived information was processed to account for differences between cancerous and healthy individuals and a support vector machine (SVM) algorithm was applied for classification. A subgroup analysis using CA-125 levels was also conducted to rule out that the observed segregation was due to CA-125 differences between patients and controls. Both techniques provided satisfactory diagnostic accuracy for the detection of ovarian cancer, with spontaneous Raman achieving 94% sensitivity and 96% specificity and SERS 87% sensitivity and 89% specificity. For early ovarian cancer, Raman achieved sensitivity and specificity of 93% and 97%, respectively, while SERS had 80% sensitivity and 94% specificity. Five spectral biomarkers were detected by both techniques and could be utilised as a panel of markers indicating carcinogenesis. CA-125 levels did not seem to undermine the high classification accuracies. This minimally invasive test may provide an alternative diagnostic and screening tool for ovarian cancer that is superior to other established blood-based biomarkers. [Abstract copyright: Copyright © 2018 Elsevier B.V. All rights reserved.

Automated Computational Detection of Disease Activity in ANCA-Associated Glomerulonephritis Using Raman Spectroscopy: A Pilot Study

Biospectroscopy offers the ability to simultaneously identify key biochemical changes in tissue associated with a given pathological state to facilitate biomarker extraction and automated detection of key lesions. Herein, we evaluated the application of machine learning in conjunction with Raman spectroscopy as an innovative low-cost technique for the automated computational detection of disease activity in anti-neutrophil cytoplasmic autoantibody (ANCA)-associated glomerulonephritis (AAGN). Consecutive patients with active AAGN and those in disease remission were recruited from a single UK centre. In those with active disease, renal biopsy samples were collected together with a paired urine sample. Urine samples were collected immediately prior to biopsy. Amongst those in remission at the time of recruitment, archived renal tissue samples representative of biopsies taken during an active disease period were obtained. In total, twenty-eight tissue samples were included in the analysis. Following supervised classification according to recorded histological data, spectral data from unstained tissue samples were able to discriminate disease activity with a high degree of accuracy on blind predictive modelling: F-score 95% for >25% interstitial fibrosis and tubular atrophy (sensitivity 100%, specificity 90%, area under ROC 0.98), 100% for necrotising glomerular lesions (sensitivity 100%, specificity 100%, area under ROC 1) and 100% for interstitial infiltrate (sensitivity 100%, specificity 100%, area under ROC 0.97). Corresponding spectrochemical changes in paired urine samples were limited. Future larger study is required, inclusive of assigned variables according to novel non-invasive biomarkers as well as the application of forward feature extraction algorithms to predict clinical outcomes based on spectral features

Distinguishing active from quiescent disease in ANCA-associated vasculitis using attenuated total reflection Fourier-transform infrared spectroscopy

Abstract: The current lack of a reliable biomarker of disease activity in anti-neutrophil cytoplasmic autoantibody (ANCA) associated vasculitis poses a significant clinical unmet need when determining relapsing or persisting disease. In this study, we demonstrate for the first time that attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy offers a novel and functional candidate biomarker, distinguishing active from quiescent disease with a high degree of accuracy. Paired blood and urine samples were collected within a single UK centre from patients with active disease, disease remission, disease controls and healthy controls. Three key biofluids were evaluated; plasma, serum and urine, with subsequent chemometric analysis and blind predictive model validation. Spectrochemical interrogation proved plasma to be the most conducive biofluid, with excellent separation between the two categories on PC2 direction (AUC 0.901) and 100% sensitivity (F-score 92.3%) for disease remission and 85.7% specificity (F-score 92.3%) for active disease on blind predictive modelling. This was independent of organ system involvement and current ANCA status, with similar findings observed on comparative analysis following successful remission-induction therapy (AUC > 0.9, 100% sensitivity for disease remission, F-score 75%). This promising technique is clinically translatable and warrants future larger study with longitudinal data, potentially aiding earlier intervention and individualisation of treatment

A Fragment of the LG3 Peptide of Endorepellin Is Present in the Urine of Physically Active Mining Workers: A Potential Marker of Physical Activity

Biomarker analysis has been implemented in sports research in an attempt to monitor the effects of exertion and fatigue in athletes. This study proposed that while such biomarkers may be useful for monitoring injury risk in workers, proteomic approaches might also be utilised to identify novel exertion or injury markers. We found that urinary urea and cortisol levels were significantly elevated in mining workers following a 12 hour overnight shift. These levels failed to return to baseline over 24 h in the more active maintenance crew compared to truck drivers (operators) suggesting a lack of recovery between shifts. Use of a SELDI-TOF MS approach to detect novel exertion or injury markers revealed a spectral feature which was associated with workers in both work categories who were engaged in higher levels of physical activity. This feature was identified as the LG3 peptide, a C-terminal fragment of the anti-angiogenic/anti-tumourigenic protein endorepellin. This finding suggests that urinary LG3 peptide may be a biomarker of physical activity. It is also possible that the activity mediated release of LG3/endorepellin into the circulation may represent a biological mechanism for the known inverse association between physical activity and cancer risk/survival

Intuitions

What are intuitions? Should we ever trust them? And if so, when? Do they have an indispensable role in science, e.g. in thought experiments, as well as in philosophy? Or should appeal to intuitions be abandoned altogether? This book brings together leading early- to late-career philosophers, to tackle such questions. It presents state-of-the-art thinking on the topic. The chapters in the first part of the book discuss the epistemological and metaphysical standing of intuitions; the chapters in the second part look at how intuitions are used in disciplines besides philosophy, and in sub-disciplines of philosophy; the chapters in the final part consider the challenges to intuitions-driven philosophy from experimental philosophy and contemporary analytic metaphysics