Artificial Intelligence and Endo-Histo-OMICs: New Dimensions of Precision Endoscopy and Histology in Inflammatory Bowel Disease

Integrating artificial intelligence into inflammatory bowel disease (IBD) has the potential to revolutionise clinical practice and research. Artificial intelligence harnesses advanced algorithms to deliver accurate assessments of IBD endoscopy and histology, offering precise evaluations of disease activity, standardised scoring, and outcome prediction. Furthermore, artificial intelligence offers the potential for a holistic endo-histo-omics approach by interlacing and harmonising endoscopy, histology, and omics data towards precision medicine. The emerging applications of artificial intelligence could pave the way for personalised medicine in IBD, offering patient stratification for the most beneficial therapy with minimal risk. Although artificial intelligence holds promise, challenges remain, including data quality, standardisation, reproducibility, scarcity of randomised controlled trials, clinical implementation, ethical concerns, legal liability, and regulatory issues. The development of standardised guidelines and interdisciplinary collaboration, including policy makers and regulatory agencies, is crucial for addressing these challenges and advancing artificial intelligence in IBD clinical practice and trials

ECCO Guidelines on Therapeutics in Ulcerative Colitis: Surgical Treatment

This is the second of a series of two articles reporting the European Crohn's and Colitis Organisation [ECCO] evidence-based consensus on the management of adult patients with ulcerative colitis [UC]. The first article is focused on medical management, and the present article addresses medical treatment of acute severe ulcerative colitis [ASUC] and surgical management of medically refractory UC patients, including preoperative optimisation, surgical strategies, and technical issues. The article provides advice for a variety of common clinical and surgical conditions. Together, the articles represent an update of the evidence-based recommendations of the ECCO for UC

ECCO Guidelines on Therapeutics in Ulcerative Colitis: Medical Treatment

hese recommendations summarise the current evidence on the medical management of adult patients with UC. Gaps were iden-tified during the analysis of the data, which should be addressed by further research. Where evidence is lacking or is very weak and evidence-based recommendations cannot be given, ECCO provides alternative tools, such as Topical Reviews28,95,153–158 or Position Papers.159–161 It is important that clinicians use these guidelines within the framework of local regulations, and seek to understand and address the individual needs and expectations of every patient. We recognise that constraints on health care resources are an im-portant factor in determining whether recommendations can be implemented for patients in many countries. The recommendations outlined here should be used to inform treatment decisions and form part of an overall multidisciplinary treatment plan for patients with UC, which may also encompass psychological, nutritional, and other non-pharmacological interventions

Which clustering algorithm is better for predicting protein complexes?

<p>Abstract</p> <p>Background</p> <p>Protein-Protein interactions (PPI) play a key role in determining the outcome of most cellular processes. The correct identification and characterization of protein interactions and the networks, which they comprise, is critical for understanding the molecular mechanisms within the cell. Large-scale techniques such as pull down assays and tandem affinity purification are used in order to detect protein interactions in an organism. Today, relatively new high-throughput methods like yeast two hybrid, mass spectrometry, microarrays, and phage display are also used to reveal protein interaction networks.</p> <p>Results</p> <p>In this paper we evaluated four different clustering algorithms using six different interaction datasets. We parameterized the MCL, Spectral, RNSC and Affinity Propagation algorithms and applied them to six PPI datasets produced experimentally by Yeast 2 Hybrid (Y2H) and Tandem Affinity Purification (TAP) methods. The predicted clusters, so called protein complexes, were then compared and benchmarked with already known complexes stored in published databases.</p> <p>Conclusions</p> <p>While results may differ upon parameterization, the MCL and RNSC algorithms seem to be more promising and more accurate at predicting PPI complexes. Moreover, they predict more complexes than other reviewed algorithms in absolute numbers. On the other hand the spectral clustering algorithm achieves the highest valid prediction rate in our experiments. However, it is nearly always outperformed by both RNSC and MCL in terms of the geometrical accuracy while it generates the fewest valid clusters than any other reviewed algorithm. This article demonstrates various metrics to evaluate the accuracy of such predictions as they are presented in the text below. Supplementary material can be found at: <url>http://www.bioacademy.gr/bioinformatics/projects/ppireview.htm</url></p

Interpreting the Omics 'era' Data

The analysis and the interpretation of the complex and dynamic biological systems has become a major bottleneck nowadays. The latest high-throughput "omics" approaches, such as genomics, proteomics and transcriptomics and the available data repositories hosting information concerning bioentities and their properties grow exponentially in size over time. Therefore, to better understand biological systems as a whole and at a higher level, visualization is a necessity as clear and meaningful views and intuitive layouts can give a better insight into coping with data complexity. The implementation of tools to maximize user friendliness, portability and provide intuitive views is a difficult task and still remains a hurdle to overcome. In this chapter, we present a variety of significant visualization tools as they specialize in different topics covering different areas of the broad biological spectrum varying from visualization of molecular structures to phylogenies, pathways, gene expression, networks, and next generation sequencing. We emphasize their functionality, the latest research findings, and insights into how these tools could be further developed both in terms of visualization but also in the direction of data integration and information sharing. © Springer International Publishing Switzerland 2013

Optimizing radiation dose and image quality

Radiation exposure is a critical issue in multidetector CT (MDCT) particularly since fast MDCT scanners have become widely available, and the method has been proposed as a noninvasive diagnostic tool for an increasing number of clinical applications. Additional features of MDCT imaging affecting individual dose are related to the inappropriate use of scanners caused by practices such as scanning beyond the area of interest or acquiring unnecessary multiphase image sets. In order to reduce individual exposure and in accordance with the ALARA principle, several strategies have been implemented over the last few years which are based on X-ray emission or optimization of scanning parameters (i.e. mAs, kV, pitch, collimation) or which take account of the individual patient's characteristics (automatic exposure control systems and ECG-pulsing techniques for ECG-gated acquisitions). These strategies allow optimization of image quality while keeping individual exposure at the lowest level. We review here these different strategies taking into account the relationship between image noise and different scanning parameters. Data from the literature are discussed, and current technological developments are considered, including initial results of dual source and SnapShot pulse technologies which have been shown to result in a significant dose reduction in ECG-gated cardiac acquisitions without compromising image quality

Simulation par la dynamique moleculaire de la diffusion des adatomes et adlacunes sur les terrasses (100), (110) et (111) de gaz rares dans les solides

Available from CEN Saclay, Service de Documentation, 91191 Gif-sur-Yvette Cedex (France) / INIST-CNRS - Institut de l'Information Scientifique et TechniqueSIGLEFRFranc

Unraveling genomic variation from next generation sequencing data

Elucidating the content of a DNA sequence is critical to deeper understand and decode the genetic information for any biological system. As next generation sequencing (NGS) techniques have become cheaper and more advanced in throughput over time, great innovations and breakthrough conclusions have been generated in various biological areas. Few of these areas, which get shaped by the new technological advances, involve evolution of species, microbial mapping, population genetics, genome-wide association studies (GWAs), comparative genomics, variant analysis, gene expression, gene regulation, epigenetics and personalized medicine. While NGS techniques stand as key players in modern biological research, the analysis and the interpretation of the vast amount of data that gets produced is a not an easy or a trivial task and still remains a great challenge in the field of bioinformatics. Therefore, efficient tools to cope with information overload, tackle the high complexity and provide meaningful visualizations to make the knowledge extraction easier are essential. In this article, we briefly refer to the sequencing methodologies and the available equipment to serve these analyses and we describe the data formats of the files which get produced by them. We conclude with a thorough review of tools developed to efficiently store, analyze and visualize such data with emphasis in structural variation analysis and comparative genomics. We finally comment on their functionality, strengths and weaknesses and we discuss how future applications could further develop in this field.status: publishe

Changes in Right Ventricular Function Measured by Cardiac Magnetic Resonance Imaging in Patients Receiving Pulmonary Arterial Hypertension-Targeted Therapy The EURO-MR Study

Background Most measures that predict survival in pulmonary hypertension (PH) relate directly to, or correlate with, right ventricular (RV) function. Direct assessment of RV function using noninvasive techniques such as cardiac MRI may therefore be an appropriate way of determining response to therapy and monitoring disease progression in PH. Methods and Results In this pan-European study, 91 patients with PH (mean pulmonary arterial pressure 4615 mm Hg) underwent clinical and cardiac MRI assessments at baseline and after 12 months of disease-targeted therapy (predominantly endothelin receptor antagonists [47.3%] or phosphodiesterase type-5 inhibitors [25.3%]). At month 12, functional class had improved in 21 patients, was unchanged in 63 patients, and had deteriorated in 7 patients. Significant improvements were achieved in RV and left ventricular ejection fraction (P<0.001 and P=0.0007, respectively), RV stroke volume index (P<0.0001), and left ventricular end-diastolic volume index (P=0.0015). Increases in 6-minute walk distance were significant (P<0.0001) and correlated with change in RV ejection fraction and left ventricular end-diastolic volume, although correlation coefficients were low (r=0.28, P=0.01 and r=0.26, P=0.02, respectively). Conclusions On-treatment changes in cardiac MRI-derived variables from left and right sides of the heart reflected changes in functional class and survival in patients with PH. Direct measurement of RV function using cardiac MRI can fully assess potential benefits of treatment in PH