Development and validation of a machine learning-based predictive model to improve the prediction of inguinal status of anal cancer patients: A preliminary report

Introduction: The role of prophylactic inguinal irradiation (PII) in the treatment of anal cancer patients is controversial. We developped an innovative algorithm based on the Machine Learning (ML) allowing the tailoring of the prescription of PII. Results: Once verified on the independent testing set, J48 showed the better performances, with specificity, sensitivity, and accuracy rates in predicting relapsing patients of 86.4%, 50.0% and 83.1% respectively (vs 36.5%, 90.4% and 80.25%, respectively, for LR). Methods: We classified 194 anal cancer patients with Logistic Regression (LR) and other 3 ML techniques based on decision trees (J48, Random Tree and Random Forest), using a large set of clinical and therapeutic variables. We tested obtained ML algorithms on an independent testing set of 65 anal cancer patients. TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis) methodology was used for the development, the Quality Assurance and the description of the experimental procedures. Conclusion: In an internationally approved quality assurance framework, ML seems promising in predicting the outcome of patients that would benefit or not of the PII. Once confirmed in larger and/or multi-centric databases, ML could support the physician in tailoring the treatment and in deciding if deliver or not the PII

pMineR: An Innovative R Library for Performing Process Mining in Medicine

Process Mining is an emerging discipline investigating tasks related with the automated identification of process models, given realworld data (Process Discovery). The analysis of such models can provide useful insights to domain experts. In addition, models of processes can be used to test if a given process complies (Conformance Checking) with specifications. For these capabilities, Process Mining is gaining importance and attention in healthcare. In this paper we introduce pMineR, an R library specifically designed for performing Process Mining in the medical domain, and supporting human experts by presenting processes in a human-readable way

VHMPID: a new detector for the ALICE experiment at LHC

This article presents the basic idea of VHMPID, an upgrade detector for the ALICE experiment at LHC, CERN. The main goal of this detector is to extend the particle identification capabilities of ALICE to give more insight into the evolution of the hot and dense matter created in Pb-Pb collisions. Starting from the physics motivations and working principles the challenges and current status of development is detailed.Comment: 4 pages, 6 figures. To be published in EPJ Web of Conference

Agrin mediates chondrocyte homeostasis and requires both LRP4 and alpha-dystroglycan to enhance cartilage formation in vitro and in vivo

Objectives Osteoarthritis (OA) is a leading cause of disability for which there is no cure. The identification of molecules supporting cartilage homeostasis and regeneration is therefore a major pursuit in musculoskeletal medicine. Agrin is a heparan sulfate proteoglycan which, through binding to low-density lipoprotein receptor-related protein 4 (LRP4), is required for neuromuscular synapse formation. In other tissues, it connects the cytoskeleton to the basement membrane through binding to α-dystroglycan. Prompted by an unexpected expression pattern, we investigated the role and receptor usage of agrin in cartilage. Methods Agrin expression pattern was investigated in human osteoarthritic cartilage and following destabilisation of the medial meniscus in mice. Extracellular matrix (ECM) formation and chondrocyte differentiation was studied in gain and loss of function experiments in vitro in three-dimensional cultures and gain of function in vivo, using an ectopic cartilage formation assay in nude mice. Receptor usage was investigated by disrupting LRP4 and α-dystroglycan by siRNA and blocking antibodies respectively. Results Agrin was detected in normal cartilage but was progressively lost in OA. In vitro, agrin knockdown resulted in reduced glycosaminoglycan content, downregulation of the cartilage transcription factor SOX9 and other cartilage-specific ECM molecules. Conversely, exogenous agrin supported cartilage differentiation in vitro and ectopic cartilage formation in vivo. In the context of cartilage differentiation, agrin used an unusual receptor repertoire requiring both LRP4 and α-dystroglycan. Conclusions We have discovered that agrin strongly promotes chondrocyte differentiation and cartilage formation in vivo. Our results identify agrin as a novel potent anabolic growth factor with strong therapeutic potential in cartilage regeneration

What Role Can Process Mining Play in Recurrent Clinical Guidelines Issues? A Position Paper

[EN] In the age of Evidence-Based Medicine, Clinical Guidelines (CGs) are recognized to be an indispensable tool to support physicians in their daily clinical practice. Medical Informatics is expected to play a relevant role in facilitating diffusion and adoption of CGs. However, the past pioneering approaches, often fragmented in many disciplines, did not lead to solutions that are actually exploited in hospitals. Process Mining for Healthcare (PM4HC) is an emerging discipline gaining the interest of healthcare experts, and seems able to deal with many important issues in representing CGs. In this position paper, we briefly describe the story and the state-of-the-art of CGs, and the efforts and results of the past approaches of medical informatics. Then, we describe PM4HC, and we answer questions like how can PM4HC cope with this challenge? 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Performance and results of the RICH detector for kaon physics in Hall A at Jefferson Lab

Abstract A proximity focusing RICH detector has been constructed for the hadron High Resolution Spectrometer (HRS) of Jefferson Lab Experimental Hall-A. This detector is intended to provide excellent hadron identification up to a momentum of 2.5 GeV / c . The RICH uses a 15 mm thick liquid perfluorohexane radiator in proximity focusing geometry to produce Cherenkov photons traversing a 100 mm thick proximity gap filled with pure methane and converted into electrons by a thin film of CsI deposited on the cathode plane of a MWPC. The detector has been successfully employed in the fixed target, high luminosity and high resolution hypernuclear spectroscopy experiment. With its use as a kaon identifier in the 2 GeV / c region, the very large contribution from pions and protons to the hypernuclear spectrum was reduced to a negligible level. The basic parameters and the resulting performance obtained during the experiment are reported in this paper