Predicting hepatic encephalopathy in patients with cirrhosis: A UK population-based study and validation of risk scores.

BACKGROUND: HE is a common neurologic complication in cirrhosis associated with substantial disease and economic burden. HE symptoms are nonspecific and there are limited ways of identifying patients with cirrhosis at high risk of later developing HE. A risk score was previously developed to identify patients at risk of developing HE in a predominately male US cohort. Here, we evaluated the performance of the HE risk scores in a UK cohort study. METHODS: Health care records from Clinical Practice Research Datalink and linked Hospital Episode Statistics were used to select patients with cirrhosis who were diagnosed with HE, confirmed by a diagnosis code for HE or a rifaximin-α prescription. The index date was the date of incident cirrhosis. The study period was from January 2003 to June 2019. RESULTS: A total of 40,809 patients with cirrhosis were selected in the UK cohort, of whom 59% were male. A total of 1561 patients were diagnosed with HE. Applying the UK cohort to the baseline sensitivity risk cutoff (≥-11) from the US cohort provided a sensitivity of 92% and a negative predictive value of 99%. Within a longitudinal model, applying a sensitivity cutoff of ≥-3 to this cohort gave a sensitivity of 89% and a negative predictive value of 99%. CONCLUSIONS: Using data from the UK, the previously developed HE risk scores were found to be reliable for selecting those most likely to progress to HE in patients with liver cirrhosis. Despite the HE risk scores originally being estimated using the data from a predominately male US cohort, the scores were validated and found to be generalizable to female patients.

Understanding radiation response and cell cycle variation in brain tumour cells using Raman spectroscopy.

Radiation therapy is currently utilised in the treatment of approximately 50% of cancer patients. A move towards patient tailored radiation therapy would help to improve the treatment outcome for patients as the inter-patient and intra-patient heterogeneity of cancer leads to large differences in treatment responses. In radiation therapy, a typical treatment outcome is cell cycle arrest which leads to cell cycle synchronisation. As treatment is typically given over multiple fractions it is important to understand how variation in the cell cycle can affect treatment response. Raman spectroscopy has previously been assessed as a method for monitoring radiation response in cancer cells and has shown promise in detecting the subtle biochemical changes following radiation exposure. This study evaluated Raman spectroscopy as a potential tool for monitoring cellular response to radiation in synchronised versus unsynchronised UVW human glioma cells in vitro. Specifically, it was hypothesised that the UVW cells would demonstrate a greater radiation resistance if the cell cycle phase of the cells was synchronised to the G1/S boundary prior to radiation exposure. Here we evaluated whether Raman spectroscopy, combined with cell cycle analysis and DNA damage and repair analysis (γ-H2AX assay), could discriminate the subtle cellular changes associated with radiation response. Raman spectroscopy combined with principal component analysis (PCA) was able to show the changes in radiation response over 24 hours following radiation exposure. Spectral changes were assigned to variations in protein, specifically changes in protein signals from amides as well as changes in lipid expression. A different response was observed between cells synchronised in the cell cycle and unsynchronised cells. After 24 hours following irradiation, the unsynchronised cells showed greater spectral changes compared to the synchronised cells demonstrating that the cell cycle plays an important role in the radiation resistance or sensitivity of the UVW cells, and that radiation resistance could be induced by controlling the cell cycle. One of the main aims of cancer treatment is to stop the proliferation of cells by controlling or halting progression through the cell cycle, thereby highlighting the importance of controlling the cell cycle when studying the effects of cancer treatments such as radiation therapy. Raman spectroscopy has been shown to be a useful tool for evaluating the changes in radiation response when the cell cycle phase is controlled and therefore highlighting its potential for assessing radiation response and resistance.

A treatment prediction strategy for overactive bladder using a machine learning algorithm that utilized data from the FAITH study.

AIMS: To use machine learning algorithms to develop a model to accurately predict treatment responses to mirabegron or antimuscarinic agents in patients with overactive bladder (OAB), using real-world data from the FAITH registry (NCT03572231). METHODS: The FAITH registry data included patients who had been diagnosed with OAB symptoms for at least 3 months and were due to initiate monotherapy with mirabegron or any antimuscarinic. For the development of the machine learning model, data from patients were included if they had completed the 183-day study period, had data for all timepoints and had completed the overactive bladder symptom scores (OABSS) at baseline and end of study. The primary outcome of the study was a composite outcome combining efficacy, persistence, and safety outcomes. Treatment was deemed "more effective" if the composite outcome criteria for "successful," "no treatment change," and "safe" were met, otherwise treatment was deemed "less effective." To explore the composite algorithm, a total of 14 clinical risk factors were included in the initial data set and a 10-fold cross-validation procedure was performed. A range of machine learning models were evaluated to determine the most effective algorithm. RESULTS: In total, data from 396 patients were included (266 [67.2%] treated with mirabegron and 130 [32.8%] treated with an antimuscarinic). Of these, 138 (34.8%) were in the "more effective" group and 258 (65.2%) were in the "less effective" group. The groups were comparable in terms of their characteristic distributions across patient age, sex, body mass index, and Charlson Comorbidity Index. Of the six models initially selected and tested, the decision tree (C5.0) model was chosen for further optimization, and the receiver operating characteristic of the final optimized model had an area under the curve result of 0.70 (95% confidence interval: 0.54-0.85) when 15 was used for the min n parameter. CONCLUSIONS: This study successfully created a simple, rapid, and easy-to-use interface that could be further refined to produce a valuable educational or clinical decision-making aid.

A novel blood based triage test for colorectal cancer in primary care: a pilot study.

BACKGROUND: The majority of colorectal cancers (CRCs) are detected after symptomatic presentation to primary care. Given the shared symptoms of CRC and benign disorders, it is challenging to manage the risk of missed diagnosis. Colonoscopy resources cannot keep pace with increasing demand. There is a pressing need for access to simple triage tools in primary care to help prioritise patients for referral. AIM: To evaluate the performance of a novel spectroscopy-based CRC blood test in primary care. DESIGN & SETTING: Mixed-methods pilot study of test performance and GP focus group discussions in South Wales. METHOD: Patients on the urgent suspected cancer (USC) pathway were recruited for the Raman spectroscopy (RS) test coupled to machine learning classification ('Raman-CRC') to identify CRC within the referred population. Qualitative focus group work evaluated the acceptability of the test in primary care by thematic analysis of focus group theorising. RESULTS: A total of 532 patients aged ≥50 years referred on the USC pathway were recruited from 27 GP practices. Twenty-nine patients (5.0%) were diagnosed with CRC. Raman-CRC identified CRC with sensitivity 95.7%, specificity 69.3% with area under curve (AUC) of 0.80 compared with colonoscopy as the reference test (248 patients). Stage I and II cancers were detected with 78.6% sensitivity. Focus group themes underlined the convenience of a blood test for the patient and the test's value as a risk assessment tool in primary care. CONCLUSION: The findings support this novel, non-invasive, blood-based method to prioritise those patients most likely to have CRC. Raman-CRC may accelerate access to diagnosis with potential to improve cancer outcomes.

Liquid Biopsy for Pancreatic Cancer Detection Using Infrared Spectroscopy.

Pancreatic cancer claims over 460,000 victims per year. The carbohydrate antigen (CA) 19-9 test is the blood test used for pancreatic cancer's detection; however, its levels can be raised in symptomatic patients with other non-malignant diseases, or with other tumors in the surrounding area. Attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy has demonstrated exceptional potential in cancer diagnostics, and its clinical implementation could represent a significant step towards early detection. This proof-of-concept study, investigating the use of ATR-FTIR spectroscopy on dried blood serum, focused on the discrimination of both cancer versus healthy control samples, and cancer versus symptomatic non-malignant control samples, as a novel liquid biopsy approach for pancreatic cancer diagnosis. Machine learning algorithms were applied, achieving results of up to 92% sensitivity and 88% specificity when discriminating between cancers (n = 100) and healthy controls (n = 100). An area under the curve (AUC) of 0.95 was obtained through receiver operating characteristic (ROC) analysis. Balanced sensitivity and specificity over 75%, with an AUC of 0.83, were achieved with cancers (n = 35) versus symptomatic controls (n = 35). Herein, we present these results as demonstration that our liquid biopsy approach could become a simple, minimally invasive, and reliable diagnostic test for pancreatic cancer detection.

Optimised Pre-Processing of Raman Spectra for Colorectal Cancer Detection Using High-Performance Computing.

Spectral pre-processing is an essential step in data analysis for biomedical diagnostic applications of Raman spectroscopy, allowing the removal of undesirable spectral contributions that could mask biological information used for diagnosis. However, due to the specificity of pre-processing for a given sample type and the vast number of potential pre-processing combinations, optimisation of pre-processing via a manual "trial and error" format is often time intensive with no guarantee that the chosen method is optimal for the sample type. Here we present the use of high-performance computing (HPC) to trial over 2.4 million pre-processing permutations to demonstrate the optimisation on the pre-processing of human serum Raman spectra for colorectal cancer detection. The effect of varying pre-processing order, using extended multiplicative scatter correction, spectral smoothing, baseline correction, binning and normalization was considered. Permutations were assessed on their ability to detect patients with disease using a random forest (RF) algorithm trained with 102 patients (510 spectra) and independently tested with a set of 439 patients (1317 spectra) in a primary care patient cohort. Optimising via HPC enables improved performance in diagnostic abilities, with sensitivity increasing by 14.6%, specificity increasing by 6.9%, positive predictive value increasing by 3.4%, and negative predictive value increasing by 2.4% when compared to a standard pre-processing optimisation. Ultimate values of these metrics are very important for diagnostic adoption, and once diagnostics demonstrate good accuracy these types of optimisations can make a significant difference to roll-out of a test and demonstrating advantages over existing tests. We also provide tips/recommendations for pre-processing optimisation without the use of HPC. From the HPC permutations, recommendations for appropriate parameter constraints for conducting a more basic pre-processing optimisation are also detailed, thus helping model development for researchers not having access to HPC.

Application of Molecular Vapour Deposited Al2O3 for Graphene-Based Biosensor Passivation and Improvements in Graphene Device Homogeneity.

Graphene-based point-of-care (PoC) and chemical sensors can be fabricated using photolithographic processes at wafer-scale. However, these approaches are known to leave polymer residues on the graphene surface, which are difficult to remove completely. In addition, graphene growth and transfer processes can introduce defects into the graphene layer. Both defects and resist contamination can affect the homogeneity of graphene-based PoC sensors, leading to inconsistent device performance and unreliable sensing. Sensor reliability is also affected by the harsh chemical environments used for chemical functionalisation of graphene PoC sensors, which can degrade parts of the sensor device. Therefore, a reliable, wafer-scale method of passivation, which isolates the graphene from the rest of the device, protecting the less robust device features from any aggressive chemicals, must be devised. This work covers the application of molecular vapour deposition technology to create a dielectric passivation film that protects graphene-based biosensing devices from harsh chemicals. We utilise a previously reported "healing effect" of Al2O3 on graphene to reduce photoresist residue from the graphene surface and reduce the prevalence of graphene defects to improve graphene device homogeneity. The improvement in device consistency allows for more reliable, homogeneous graphene devices, that can be fabricated at wafer-scale for sensing and biosensing applications.

Rapid analysis of disease state in liquid human serum combining infrared spectroscopy and "digital drying".

In recent years, the diagnosis of brain tumors has been investigated with attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy on dried human serum samples to eliminate spectral interferences of the water component, with promising results. This research evaluates ATR-FTIR on both liquid and air-dried samples to investigate "digital drying" as an alternative approach for the analysis of spectra obtained from liquid samples. Digital drying approaches, consisting of water subtraction and least-squares method, have demonstrated a greater random forest (RF) classification performance than the air-dried spectra approach when discriminating cancer vs control samples, reaching sensitivity values higher than 93.0% and specificity values higher than 83.0%. Moreover, quantum cascade laser infrared (QCL-IR) based spectroscopic imaging is utilized on liquid samples to assess the implications of a deep-penetration light source on disease classification. The RF classification of QCL-IR data has provided sensitivity and specificity amounting to 85.1% and 75.3% respectively.

Vibrational Spectroscopy: A Valuable Screening and Diagnostic Tool for Obstetric Disorders?

Preeclampsia (PE) is a common obstetric disorder typically affecting 2-8% of all pregnancies and can lead to several adverse obstetric outcomes for both mother and fetus with the greatest burden of severe outcomes in low middle-income countries (LMICs), therefore, screening for PE is vital. Globally, screening is based on maternal characteristics and medical history which are nonspecific for the disorder. In 2004, the World Health Organization acknowledged that no clinically useful test was able to predict the onset of PE, which prompted a universal search for alternative means of screening. Over the past decade or so, emphasis has been placed on the use of maternal characteristics in conjunction with biomarkers of disease combined into predictive algorithms, however these are yet to transition into the clinic and are cost prohibitive in LMICs. As a result, the screening paradigm for PE remains unchanged. It is evident that novel approaches are needed. Vibrational spectroscopy, specifically Raman spectroscopy and Fourier-transform infrared spectroscopy (FTIR), could provide better alternatives suited for implementation in low resource settings as no specialized reagents are required for conventional approaches and there is a drive to portable platforms usable in both urban and rual community settings. These techniques are based on light scattering and absorption, respectively, allowing detailed molecular analysis of samples to produce a unique molecular fingerprint of diseased states. The specificity of vibrational spectroscopy might well make it suited for application in other obstetric disorders such as gestational diabetes mellitus and obstetric cholestasis. In this review, we summarize current approaches sought as alternatives to current screening methodologies and introduce how vibrational spectroscopy could offer superior screening and diagnostic paradigms in obstetric care. Additionally, we propose a real benefit of such tools in LMICs where limited resources battle the higher prevalence of obstetric disorders.

A high-throughput serum Raman spectroscopy platform and methodology for colorectal cancer diagnostics.

Vibrational spectroscopic techniques such as Raman spectroscopy and Fourier transform infrared spectroscopy (FTIR) have huge potential for the analysis of biological specimens. The techniques allow the user to gain label-free, non-destructive biochemical information about a given sample. Previous studies using vibrational spectroscopy with the specific application of diagnosing colorectal diseases such as cancer have mainly focused on in vivo or in vitro studies of tissue specimens using microscopy or probe based techniques. There have been few studies of vibrational spectroscopic techniques based on the analysis of blood serum for the advancement of colorectal cancer diagnostics. With growing interest in the field of liquid biopsies, this study presents the development of a high-throughput (HT) serum Raman spectroscopy platform and methodology and compares dry and liquid data acquisition of serum samples. This work considers factors contributing to translatability of the methodologies such as HT design, inter-user variability and sample handling effects on diagnostic capability. The HT Raman methods were tested on a pilot dataset of serum from 30 cancer patients and 30 matched control patients using statistical analysis via cross-validated PLS-DA with a maximum achieved a sensitivity of 83% and specificity of 83% for detecting colorectal cancer.

Role of Raman spectroscopy and surface enhanced Raman spectroscopy in colorectal cancer.

Colorectal cancer (CRC) is the fourth most common cancer in the United Kingdom and is the second largest cause of cancer related death in the United Kingdom after lung cancer. Currently in the United Kingdom there is not a diagnostic test that has sufficient differentiation between patients with cancer and those without cancer so the current referral system relies on symptomatic presentation in a primary care setting. Raman spectroscopy and surface enhanced Raman spectroscopy (SERS) are forms of vibrational spectroscopy that offer a non-destructive method to gain molecular information about biological samples. The techniques offer a wide range of applications from in vivo or in vitro diagnostics using endoscopic probes, to the use of micro-spectrometers for analysis of biofluids. The techniques have the potential to detect molecular changes prior to any morphological changes occurring in the tissue and therefore could offer many possibilities to aid the detection of CRC. The purpose of this review is to look at the current state of diagnostic technology in the United Kingdom. The development of Raman spectroscopy and SERS in clinical applications relation for CRC will then be discussed. Finally, future areas of research of Raman/SERS as a clinical tool for the diagnosis of CRC are also discussed.