Diagnostics de base, mise en oeuvre et appréciation du potentiel des NTIC (radio, internet, concertation) dans les zones enclavées de la région de Daraina (sous-préfécture de Vohémar, Nord-Est Madagascar)

Rapport d'avancement - février 2005

Postmortem diagnostics using MSCT and MRI of a lethal streptococcus group A infection at infancy: a case report.

Postmortem cross-sectional imaging using multislice computed tomography (MSCT) and magnetic resonance imaging (MRI) was considered as a base for a minimal invasive postmortem investigation in forensic medicine such as within the Virtopsy approach. We present the case of a 3-year-old girl with a lethal streptococcus group A infection and the findings of postmortem imaging in this kind of natural death. Postmortem MSCT and MRI revealed an edematous occlusion of the larynx at the level of the vocal cords, severe pneumonia with atelectatic parts of both upper lobes and complete atelectasis of both lower lobes, purulent fluid-filled right main bronchus, enlargement of cervical lymph nodes and pharyngeal tonsils, and additionally, a remaining glossopharyngeal cyst as well as an ureter fissus of the right kidney. All relevant autopsy findings could be obtained and visualized by postmortem imaging and confirmed by histological and microbiological investigations supporting the idea of a minimal invasive autopsy technique.

Fast Prediction of Femoral Biomechanics Using Supervised Machine Learning and Statistical Shape Modeling

Finite element (FE) analysis is an important computational tool in biomechanics. However, its adoption into clinical practice has been hampered by its computational complexity and required high technical competences for clinicians. In this paper we propose a supervised learning approach to predict the outcome of the FE analysis. We demonstrate our approach on clinical CT and X-ray femur images for FE predictions ( FEP), with features extracted, respectively, from a statistical shape model and from 2D-based morphometric and density information. Using leave-one-out experiments and sensitivity analysis, comprising a database of 89 clinical cases, our method is capable of predicting the distribution of stress values for a walking loading condition with an average correlation coefficient of 0.984 and 0.976, for CT and X-ray images, respectively. These findings suggest that supervised learning approaches have the potential to leverage the clinical integration of mechanical simulations for the treatment of musculoskeletal conditions.

Particle Accelerators and Detectors for medical Diagnostics and Therapy

This Habilitationsschrift (Habilitation thesis) is focused on my research activities on medical applications of particle physics and was written in 2013 to obtain the Venia Docendi (Habilitation) in experimental physics at the University of Bern. It is based on selected publications, which represented at that time my major scientific contributions as an experimental physicist to the field of particle accelerators and detectors applied to medical diagnostics and therapy. The thesis is structured in two parts. In Part I, Chapter 1 presents an introduction to accelerators and detectors applied to medicine, with particular focus on cancer hadrontherapy and on the production of radioactive isotopes. In Chapter 2, my publications on medical particle accelerators are introduced and put into their perspective. In particular, high frequency linear accelerators for hadrontherapy are discussed together with the new Bern cyclotron laboratory. Chapter 3 is dedicated to particle detectors with particular emphasis on three instruments I contributed to propose and develop: segmented ionization chambers for hadrontherapy, a proton radiography apparatus with nuclear emulsion films, and a beam monitor detector for ion beams based on doped silica fibres. Selected research and review papers are contained in Part II. For copyright reasons, they are only listed and not reprinted in this on-line version. They are available on the websites of the journals.

First international external quality assessment of molecular diagnostics for Mers-CoV

BACKGROUND: Since the discovery of Middle East respiratory syndrome coronavirus (MERS-CoV) in 2012, diagnostic protocols were quickly published and deployed globally. OBJECTIVES: We set out to assess the quality of MERS-CoV molecular diagnostics worldwide. STUDY DESIGN: Both sensitivity and specificity were assessed using 12 samples containing different viral loads of MERS-CoV or common coronaviruses (OC43, 229E, NL63, HKU1). RESULTS: The panel was sent to more than 106 participants, of which 99 laboratories from 6 continents returned 189 panel results.Scores ranged from 100% (84 laboratories) to 33% (1 laboratory). 15% of respondents reported quantitative results, 61% semi-quantitative (Ct-values or time to positivity) and 24% reported qualitative results. The major specific technique used was real-time RT-PCR using the WHO recommended targets upE, ORF1a and ORF1b. The evaluation confirmed that RT-PCRs targeting the ORF1b are less sensitive, and therefore not advised for primary diagnostics. CONCLUSIONS: The first external quality assessment MERS-CoV panel gives a good insight in molecular diagnostic techniques and their performances for sensitive and specific detection of MERS-CoV RNA globally. Overall, all laboratories were capable of detecting MERS-CoV with some differences in sensitivity. The observation that 8% of laboratories reported false MERS-CoV positive single assay results shows room for improvement, and the importance of using confirmatory targets.

Discourse-level Annotation over Europarl for Machine Translation: Connectives and Pronouns

This paper describes methods and results for the annotation of two discourse-level phenomena, connectives and pronouns, over a multilingual parallel corpus. Excerpts from Europarl in English and French have been annotated with disambiguation information for connectives and pronouns, for about 3600 tokens. This data is then used in several ways: for cross-linguistic studies, for training automatic disambiguation software, and ultimately for training and testing discourse-aware statistical machine translation systems. The paper presents the annotation procedures and their results in detail, and overviews the first systems trained on the annotated resources and their use for machine translation.