Standardisation of intestinal ultrasound scoring in clinical trials for luminal Crohn's disease

Background: Intestinal ultrasound (IUS) is a valuable tool for assessment of Crohn’s disease (CD). However, there is no widely accepted luminal disease activity index. / Aims: To identify appropriate IUS protocols, indices, items, and scoring methods for measurement of luminal CD activity and integration of IUS in CD clinical trials. / Methods: An expert international panel of adult and paediatric gastroenterologists (n = 15) and radiologists (n = 3) rated the appropriateness of 120 statements derived from literature review and expert opinion (scale of 1-9) using modified RAND/UCLA methodology. Median panel scores of 1 to ≤3.5, >3.5 to <6.5 and ≥6.5 to 9 were considered inappropriate, uncertain and appropriate ratings respectively. The statement list and survey results were discussed prior to voting. / Results: A total of 91 statements were rated appropriate with agreement after two rounds of voting. Items considered appropriate measures of disease activity were bowel wall thickness (BWT), vascularity, stratification and mesenteric inflammatory fat. There was uncertainty if any of the existing IUS disease activity indices were appropriate for use in CD clinical trials. Appropriate trial applications for IUS included patient recruitment qualification when diseased segments cannot be adequately assessed by ileocolonoscopy and screening for exclusionary complications. At outcome assessment, remission endpoints including BWT and vascularity, with or without mesenteric inflammatory fat, were considered appropriate. Components of an ideal IUS disease activity index were identified based upon panel discussions. / Conclusions: The panel identified appropriate component items and applications of IUS for CD clinical trials. Empiric evidence, and development and validation of an IUS disease activity index are needed

Passively mode-locked laser using an entirely centred erbium-doped fiber

This paper describes the setup and experimental results for an entirely centred erbium-doped fiber laser with passively mode-locked output. The gain medium of the ring laser cavity configuration comprises a 3 m length of two-core optical fiber, wherein an undoped outer core region of 9.38 μm diameter surrounds a 4.00 μm diameter central core region doped with erbium ions at 400 ppm concentration. The generated stable soliton mode-locking output has a central wavelength of 1533 nm and pulses that yield an average output power of 0.33 mW with a pulse energy of 31.8 pJ. The pulse duration is 0.7 ps and the measured output repetition rate of 10.37 MHz corresponds to a 96.4 ns pulse spacing in the pulse train

Identification of human pathogens isolated from blood using microarray hybridisation and signal pattern recognition

<p>Abstract</p> <p>Background</p> <p>Pathogen identification in clinical routine is based on the cultivation of microbes with subsequent morphological and physiological characterisation lasting at least 24 hours. However, early and accurate identification is a crucial requisite for fast and optimally targeted antimicrobial treatment. Molecular biology based techniques allow fast identification, however discrimination of very closely related species remains still difficult.</p> <p>Results</p> <p>A molecular approach is presented for the rapid identification of pathogens combining PCR amplification with microarray detection. The DNA chip comprises oligonucleotide capture probes for 25 different pathogens including Gram positive cocci, the most frequently encountered genera of <it>Enterobacteriaceae</it>, non-fermenter and clinical relevant <it>Candida </it>species. The observed detection limits varied from 10 cells (e.g. <it>E. coli</it>) to 10⁵cells (<it>S. aureus</it>) per mL artificially spiked blood. Thus the current low sensitivity for some species still represents a barrier for clinical application. Successful discrimination of closely related species was achieved by a signal pattern recognition approach based on the k-nearest-neighbour method. A prototype software providing this statistical evaluation was developed, allowing correct identification in 100 % of the cases at the genus and in 96.7 % at the species level (n = 241).</p> <p>Conclusion</p> <p>The newly developed molecular assay can be carried out within 6 hours in a research laboratory from pathogen isolation to species identification. From our results we conclude that DNA microarrays can be a useful tool for rapid identification of closely related pathogens particularly when the protocols are adapted to the special clinical scenarios.</p

MLPAinter for MLPA interpretation: An integrated approach for the analysis, visualisation and data management of Multiplex Ligation-dependent Probe Amplification

Background: Multiplex Ligation-Dependent Probe Amplification (MLPA) is an application that can be used for the detection of multiple chromosomal aberrations in a single experiment. In one reaction, up to 50 different genomic sequences can be analysed. For a reliable work-flow, tools are needed for administrative support, data management, normalisation, visualisation, reporting and interpretation.Results: Here, we developed a data management system, MLPAInter for MLPA interpretation, that is windows executable and has a stand-alone database for monitoring and interpreting the MLPA data stream that is generated from the experimental setup to analysis, quality control and visualisation. A statistical approach is applied for the normalisation and analysis of large series of MLPA traces, making use of multiple control samples and internal controls.Conclusions: MLPAinter visualises MLPA data in plots with information about sample replicates, normalisation settings, and sample characteristics. This integrated approach helps in the automated handling of large series of MLPA data and guarantees a quick and streamlined dataflow from the beginning of an experiment to an authorised report

Translating AI to digital pathology workflow: Dealing with scarce data and high variation by minimising complexities in data and models

The recent conversion to digital pathology using Whole Slide Images (WSIs) from conventional pathology opened the doors for Artificial Intelligence (AI) in pathology workflow. The recent interests in machine learning and deep learning have gained a high interest in medical image processing. However, WSIs differ from generic medical images. WSIs are complex images which can reveal various information to support different diagnosis varying from cancer to unknown underlying conditions which were not discovered in other medical investigations. These investigations require expert knowledge spending a long time for investigations, applying different stains to the WSIs, and comparing the WSIs. Differences in WSI differentiate general machine learning methods that are applied for medical image processing. Co-analysing multistained WSIs, high variation of the WSIs from different sites, and lack of labelled data are the main key interest areas that directly influence in developing machine learning models that support pathologists in their investigations. However, most of the state-ofthe- art machine learning approaches cannot be applied in the general clinical workflow without using high compute power, expert knowledge, and time. Therefore, this thesis explores avenues to translate the highly computational and time intensive model to a clinical workflow. Co-analysing multi-stained WSIs require registering differently stained WSI together. In order to get a high precision in the registration exploring nonrigid and rigid transformation is required. The non-rigid transformation requires complex deep learning approaches. Using super-convergence on a small Convolutional Neural Network model it is possible to achieve high precision compared to larger auto-encoders and other state-of-the-art models. High variation of the WSIs from different sites heavily effect machine learning models in their predictions. The thesis presents an approach of using a pre-trained model by using only a small number of samples from the new site. Therefore, re-training larger deep learning models are not required which saves expert time for re-labelling and computational power. Finally, lack of labelled data is one of the main issues in training any supervised machine learning or deep learning model. Using a Generative Adversarial Networks (GAN) is an approach which can be easily implemented to avoid this issue. However, GANs are time and computationally expensive. These are not applicable in a general clinical workflow. Therefore, this thesis presents an approach using a simpler GANthat can generate accurate sample labelled data. The synthetic data are used to train classifier and the thesis demonstrates that the predictive model can generate higher accuracy in the test environment. This thesis demonstrates that machine learning and deep learning models can be applied to a clinical workflow, without exploiting expert time and high computing power

Discovering new kinds of patient safety incidents

Every year, large numbers of patients in National Health Service (NHS) care suffer because of a patient safety incident. The National Patient Safety Agency (NPSA) collects large amounts of data describing individual incidents. As well as being described by categorical and numerical variables, each incident is described using free text. The aim of the work was to find quite small groups of similar incidents, which were of types that were previously unknown to the NPSA. A model of the text was produced, such that the position of each incident reflected its meaning to the greatest extent possible. The basic model was the vector space model. Dimensionality reduction was carried out in two stages: unsupervised dimensionality reduction was carried out using principal component analysis, and supervised dimensionality reduction using linear discriminant analysis. It was then possible to look for groups of incidents that were more tightly packed than would be expected given the overall distribution of the incidents. The process for assessing these groups had three stages. Firstly, a quantitative measure was used, allowing a large number of parameter combinations to be examined. The groups found for an ‘optimum’ parameter combination were then divided into categories using a qualitative filtering method. Finally, clinical experts assessed the groups qualitatively. The transition probabilities model was also examined: this model was based on the empirical probabilities that two word sequences were seen in the text. An alternative method for dimensionality reduction was to use information about the subjective meaning of a small sample of incidents elicited from experts, producing a mapping between high and low dimensional models of the text. The analysis also included the direct use of the categorical variables to model the incidents, and empirical analysis of the behaviour of high dimensional spaces