Development and validation of multivariable machine learning algorithms to predict risk of cancer in symptomatic patients referred urgently from primary care: a diagnostic accuracy study.

Objectives: To develop and validate tests to assess the risk of any cancer for patients referred to the NHS Urgent Suspected Cancer (2-week wait, 2WW) clinical pathways.

Setting: Primary and secondary care, one participating regional centre.

Participants: Retrospective analysis of data from 371 799 consecutive 2WW referrals in the Leeds region from 2011 to 2019.

Point absorbers in Advanced LIGO.

Small, highly absorbing points are randomly present on the surfaces of the main interferometer optics in Advanced LIGO. The resulting nanometer scale thermo-elastic deformations and substrate lenses from these micron-scale absorbers significantly reduce the sensitivity of the interferometer directly though a reduction in the power-recycling gain and indirect interactions with the feedback control system. We review the expected surface deformation from point absorbers and provide a pedagogical description of the impact on power buildup in second generation gravitational wave detectors (dual-recycled Fabry-Perot Michelson interferometers).

An improved machine learning pipeline for urinary volatiles disease detection: Diagnosing diabetes.

Motivation: The measurement of disease biomarkers in easily-obtained bodily fluids has opened the door to a new type of non-invasive medical diagnostics. New technologies are being developed and fine-tuned in order to make this possibility a reality. One such technology is Field Asymmetric Ion Mobility Spectrometry (FAIMS), which allows the measurement of volatile organic compounds (VOCs) in biological samples such as urine.

Identifying volatile metabolite signatures for the diagnosis of bacterial respiratory tract infection using electronic nose technology: A pilot study.

Objectives: New point of care diagnostics are urgently needed to reduce the over-prescription of antimicrobials for bacterial respiratory tract infection (RTI). We performed a pilot cross sectional study to assess the feasibility of gas-capillary column ion mobility spectrometer (GC-IMS), for the analysis of volatile organic compounds (VOC) in exhaled breath to diagnose bacterial RTI in hospital inpatients.

Methods: 71 patients were prospectively recruited from the Acute Medical Unit of the Royal Liverpool University Hospital between March and May 2016 and classified as confirmed or probable bacterial or viral RTI on the basis of microbiologic, biochemical and radiologic testing.

Faecal volatile organic compounds analysis using field asymmetric ion mobility spectrometry: non-invasive diagnostics in paediatric inflammatory bowel disease.

Background And Objectives: Inflammatory bowel disease (IBD), including Crohn's disease (CD) and ulcerative colitis (UC), remains challenging to diagnose. Diagnostic work-up carries a high burden, especially in paediatric patients, due to invasive endoscopic procedures. IBD is associated with alterations in intestinal microbiota composition.

Protein oxidation, nitration and glycation biomarkers for early-stage diagnosis of osteoarthritis of the knee and typing and progression of arthritic disease.

Background: There is currently no blood-based test for detection of early-stage osteoarthritis (OA) and the anti-cyclic citrullinated peptide (CCP) antibody test for rheumatoid arthritis (RA) has relatively low sensitivity for early-stage disease. Morbidity in arthritis could be markedly decreased if early-stage arthritis could be routinely detected and classified by clinical chemistry test. We hypothesised that damage to proteins of the joint by oxidation, nitration and glycation, and with signatures released in plasma as oxidized, nitrated and glycated amino acids may facilitate early-stage diagnosis and typing of arthritis.

A simple breath test for tuberculosis using ion mobility: A pilot study.

Tuberculosis (TB) remains one of the world's major health burdens with 9.6 million new infections globally. Though considerable progress has been made in reduction of TB incidence and mortality, there is a continuous need for lower cost, simpler and more robust means of diagnosis.

Predicting chemoinsensitivity in breast cancer with 'omics/digital pathology data fusion.

Predicting response to treatment and disease-specific deaths are key tasks in cancer research yet there is a lack of methodologies to achieve these. Large-scale 'omics and digital pathology technologies have led to the need for effective statistical methods for data fusion to extract the most useful patterns from these diverse data types. We present FusionGP, a method for combining heterogeneous data types designed specifically for predicting outcome of treatment and disease.

Multiplex PCR and Next Generation Sequencing for the Non-Invasive Detection of Bladder Cancer.

Background: Highly sensitive and specific urine-based tests to detect either primary or recurrent bladder cancer have proved elusive to date. Our ever increasing knowledge of the genomic aberrations in bladder cancer should enable the development of such tests based on urinary DNA.

Methods: DNA was extracted from urine cell pellets and PCR used to amplify the regions of the TERT promoter and coding regions of FGFR3, PIK3CA, TP53, HRAS, KDM6A and RXRA which are frequently mutated in bladder cancer.

Non-invasive exhaled volatile organic biomarker analysis to detect inflammatory bowel disease (IBD).

Introduction: Early inflammatory bowel disease (IBD) diagnosis remains a clinical challenge. Volatile organic compounds (VOCs) have shown distinct patterns in Crohn's disease (CD) and ulcerative colitis (UC). VOC production, reflecting gut fermentome metabolites, is perturbed in IBD.

Bayesian modeling suggests that IL-12 (p40), IL-13 and MCP-1 drive murine cytokine networks in vivo.

Background: Cytokine-hormone network deregulations underpin pathologies ranging from autoimmune disorders to cancer, but our understanding of these networks in physiological/pathophysiological states remains patchy. We employed Bayesian networks to analyze cytokine-hormone interactions in vivo using murine lactation as a dynamic, physiological model system.

Results: Circulatory levels of estrogen, progesterone, prolactin and twenty-three cytokines were profiled in post partum mice with/without pups.

Non-invasive distinction of non-alcoholic fatty liver disease using urinary volatile organic compound analysis: early results.

Background & Aims: Non-Alcoholic Fatty Liver Disease (NAFLD) is the commonest cause of chronic liver disease in the western world. Current diagnostic methods including Fibroscan have limitations, thus there is a need for more robust non-invasive screening methods. The gut microbiome is altered in several gastrointestinal and hepatic disorders resulting in altered, unique gut fermentation patterns, detectable by analysis of volatile organic compounds (VOCs) in urine, breath and faeces.

Prediction of resistance to chemotherapy in ovarian cancer: a systematic review.

Background: Patient response to chemotherapy for ovarian cancer is extremely heterogeneous and there are currently no tools to aid the prediction of sensitivity or resistance to chemotherapy and allow treatment stratification. Such a tool could greatly improve patient survival by identifying the most appropriate treatment on a patient-specific basis.

Methods: PubMed was searched for studies predicting response or resistance to chemotherapy using gene expression measurements of human tissue in ovarian cancer.

Rapid, accurate, and on-site detection of C. difficile in stool samples.

Objectives: A rapid test to diagnose Clostridium difficile infection (CDI) on hospital wards could minimize common but critical diagnostic delay. Field asymmetric ion mobility spectrometry (FAIMS) is a portable mass spectrometry instrument that quickly analyses the chemical composition of gaseous mixtures (e.g.

Biomarkers of early stage osteoarthritis, rheumatoid arthritis and musculoskeletal health.

There is currently no biochemical test for detection of early-stage osteoarthritis (eOA). Tests for early-stage rheumatoid arthritis (eRA) such as rheumatoid factor (RF) and anti-cyclic citrullinated peptide (CCP) antibodies require refinement to improve clinical utility. We developed robust mass spectrometric methods to quantify citrullinated protein (CP) and free hydroxyproline in body fluids.

Differentiating coeliac disease from irritable bowel syndrome by urinary volatile organic compound analysis--a pilot study.

Coeliac disease (CD), a T-cell-mediated gluten sensitive enteropathy, affects ∼ 1% of the UK population and can present with wide ranging clinical features, often being mistaken for Irritable Bowel Syndrome (IBS). Heightened clinical awareness and serological screening identifies those with potential coeliac disease; the diagnosis is confirmed with duodenal biopsies, and symptom improvement with a gluten-free diet. Limitations to diagnosis are false negative serology and reluctance to undergo biopsy.

Bayesian hierarchical clustering for studying cancer gene expression data with unknown statistics.

Clustering analysis is an important tool in studying gene expression data. The Bayesian hierarchical clustering (BHC) algorithm can automatically infer the number of clusters and uses Bayesian model selection to improve clustering quality. In this paper, we present an extension of the BHC algorithm.

Cognitive and affective reassurance and patient outcomes in primary care: a systematic review.

In the context of uncertainty about aetiology and prognosis, good clinical practice commonly recommends both affective (creating rapport, showing empathy) and cognitive reassurance (providing explanations and education) to increase self-management in groups with nonspecific pain conditions. The specific impact of each of these components in reference to patients' outcomes has not been studied. This review aimed to systematically evaluate the evidence from prospective cohorts in primary care that measured patient-practitioner interactions with reference to patient outcomes.

Accelerating Bayesian hierarchical clustering of time series data with a randomised algorithm.

We live in an era of abundant data. This has necessitated the development of new and innovative statistical algorithms to get the most from experimental data. For example, faster algorithms make practical the analysis of larger genomic data sets, allowing us to extend the utility of cutting-edge statistical methods.

Bayesian correlated clustering to integrate multiple datasets.

Motivation: The integration of multiple datasets remains a key challenge in systems biology and genomic medicine. Modern high-throughput technologies generate a broad array of different data types, providing distinct-but often complementary-information. We present a Bayesian method for the unsupervised integrative modelling of multiple datasets, which we refer to as MDI (Multiple Dataset Integration).

Patient-specific data fusion defines prognostic cancer subtypes.

Different data types can offer complementary perspectives on the same biological phenomenon. In cancer studies, for example, data on copy number alterations indicate losses and amplifications of genomic regions in tumours, while transcriptomic data point to the impact of genomic and environmental events on the internal wiring of the cell. Fusing different data provides a more comprehensive model of the cancer cell than that offered by any single type.

Bayesian hierarchical clustering for microarray time series data with replicates and outlier measurements.

Background: Post-genomic molecular biology has resulted in an explosion of data, providing measurements for large numbers of genes, proteins and metabolites. Time series experiments have become increasingly common, necessitating the development of novel analysis tools that capture the resulting data structure. Outlier measurements at one or more time points present a significant challenge, while potentially valuable replicate information is often ignored by existing techniques.

Discovering transcriptional modules by Bayesian data integration.

Motivation: We present a method for directly inferring transcriptional modules (TMs) by integrating gene expression and transcription factor binding (ChIP-chip) data. Our model extends a hierarchical Dirichlet process mixture model to allow data fusion on a gene-by-gene basis. This encodes the intuition that co-expression and co-regulation are not necessarily equivalent and hence we do not expect all genes to group similarly in both datasets.

Computational approaches to the integration of gene expression, ChIP-chip and sequence data in the inference of gene regulatory networks.

A major challenge in systems biology is the ability to model complex regulatory interactions, such as gene regulatory networks, and a number of computational approaches have been developed over recent years to address this challenge. This paper reviews a number of these approaches, with a focus on probabilistic graphical models and the integration of diverse data sets, such as gene expression and transcription factor binding site location and activity.