Les déclarations interprétatives françaises au premier Protocole additionnel aux Conventions de Genève de 1949 relatives à la conduite des hostilités

Élaborées au lendemain de la Seconde Guerre mondiale, les quatre Conventions de Genève du 12 août 1949 visent à protéger « les victimes de la guerre ». Elles ont été « complétées » en 1977 par deux Protocoles additionnels dont le Protocole I relatif à la protection des victimes de conflits armés internationaux. Les conflits récents ont généralement revêtu des formes très éloignées de celles dans lesquelles les lois et coutumes de la guerre s’étaient développées au cours des siècles, qu’il s’a..

Extended-spectrum beta-lactamases-producing <i>Escherichia coli</i> in common vampire bats <i>Desmodus rotundus</i> and livestock in Peru

Antibiotic resistance mediated by bacterial production of extended‐spectrum beta‐lactamase (ESBL) is a global threat to public health. ESBL resistance is most commonly hospital‐acquired; however, infections acquired outside of hospital settings have raised concerns over the role of livestock and wildlife in the zoonotic spread of ESBL‐producing bacteria. Only limited data are available on the circulation of ESBL‐producing bacteria in animals. Here, we report ESBL‐producing Escherichia coli in wild common vampire bats Desmodus rotundus and livestock near Lima, Peru. Molecular analyses revealed that most of this resistance resulted from the expression of blaCTX‐M‐15 genes carried by plasmids, which are disseminating worldwide in hospital settings and have also been observed in healthy children of Peru. Multilocus sequence typing showed a diverse pool of E. coli strains carrying this resistance that were not always host species‐specific, suggesting sharing of strains between species or infection from a common source. This study shows widespread ESBL resistance in wild and domestic animals, supporting animal communities as a potential source of resistance. Future work is needed to elucidate the role of bats in the dissemination of antibiotic‐resistant strains of public health importance and to understand the origin of the observed resistance

Proteomic profile determination of autosomal aneuploidies by mass spectrometry on amniotic fluids

<p>Abstract</p> <p>Background</p> <p>Prenatal diagnosis of chromosomal abnormalities by cytogenetic analysis is time-consuming, expensive, and requires highly qualified technicians. Rapid diagnosis of aneuploidies followed by reassurance of women with normal results can be performed by molecular analysis of uncultured foetal cells. In the present study, we developed a proteomic fingerprinting approach coupled with a statistical classification method to improve diagnosis of aneuploidies, including trisomies 13, 18, and 21, in amniotic fluid samples.</p> <p>Results</p> <p>The proteomic spectra obtained from 52 pregnant women were compiled, normalized, and mass peaks with mass-to-charge ratios between 2.5 and 50 kDa identified. Peak information was combined together and analysed using univariate statistics. Among the 208 expressed protein peaks, 40 differed significantly between aneuploid and non aneuploid samples, with AUC diagnostic values ranging from 0.71 to 0.91. Hierarchical clustering, principal component analysis and support vector machine (SVM) analysis were performed. Two class predictor models were defined from the training set, which resulted in a prediction accuracy of 92.3% and 96.43%, respectively. Using an external and independent validation set, diagnostic accuracies were maintained at 87.5% and 91.67%, respectively.</p> <p>Conclusion</p> <p>This pilot study demonstrates the potential interest of protein expression signature in the identification of new potential biological markers that might be helpful for the rapid clinical management of high-risk pregnancies.</p

ifCNV: A novel isolation-forest-based package to detect copy-number variations from various targeted NGS datasets

Copy-number variations (CNVs) are an essential component of genetic variation distributed across large parts of the human genome. CNV detection from next-generation sequencing data and artificial intelligence algorithms have progressed in recent years. However, only a few tools have taken advantage of machine-learning algorithms for CNV detection, and none propose using artificial intelligence to automatically detect probable CNV-positive samples. The most developed approach is to use a reference or normal dataset to compare with the samples of interest, and it is well known that selecting appropriate normal samples represents a challenging task that dramatically influences the precision of results in all CNV-detecting tools. With careful consideration of these issues, we propose here ifCNV, a new software based on isolation forests that creates its own reference, available in R and python with customizable parameters. ifCNV combines artificial intelligence using two isolation forests and a comprehensive scoring method to faithfully detect CNVs among various samples. It was validated using targeted next-generation sequencing (NGS) datasets from diverse origins (capture and amplicon, germline and somatic), and it exhibits high sensitivity, specificity, and accuracy. ifCNV is a publicly available open-source software (https://github.com/SimCab-CHU/ifCNV) that allows the detection of CNVs in many clinical situations

Comparison of Supervised Classification Methods for Protein Profiling in Cancer Diagnosis

A key challenge in clinical proteomics of cancer is the identification of biomarkers that could allow detection, diagnosis and prognosis of the diseases. Recent advances in mass spectrometry and proteomic instrumentations offer unique chance to rapidly identify these markers. These advances pose considerable challenges, similar to those created by microarray-based investigation, for the discovery of pattern of markers from high-dimensional data, specific to each pathologic state (e.g. normal vs cancer). We propose a three-step strategy to select important markers from high-dimensional mass spectrometry data using surface enhanced laser desorption/ionization (SELDI) technology. The first two steps are the selection of the most discriminating biomarkers with a construction of different classifiers. Finally, we compare and validate their performance and robustness using different supervised classification methods such as Support Vector Machine, Linear Discriminant Analysis, Quadratic Discriminant Analysis, Neural Networks, Classification Trees and Boosting Trees. We show that the proposed method is suitable for analysing high-throughput proteomics data and that the combination of logistic regression and Linear Discriminant Analysis outperform other methods tested

Serum Proteomic Profiling of Lung Cancer in High-Risk Groups and Determination of Clinical Outcomes

HypothesisLung cancer remains the leading cause of cancer-related mortality worldwide. Currently known serum markers do not efficiently diagnose lung cancer at early stage.MethodsIn the present study, we developed a serum proteomic fingerprinting approach coupled with a three-step classification method to address two important clinical questions: (i) to determine whether or not proteomic profiling differs between lung cancer and benign lung diseases in a population of smokers and (ii) to assess the prognostic impact of this profiling in lung cancer. Proteomic spectra were obtained from 170 pathologically confirmed lung cancer or smoking patients with benign chronic lung disease serum samples.ResultsAmong the 228 protein peaks differentially expressed in the whole population, 88 differed significantly between lung cancer patients and benign lung disease, with area under the curve diagnostic values ranging from 0.63 to 0.84. Multiprotein classifiers based on differentially expressed peaks allowed the classification of lung cancer and benign disease with an area under the curve ranging from 0.991 to 0.994. Using a cross-validation methodology, diagnostic accuracy was 93.1% (sensitivity 94.3%, specificity 85.9%), and more than 90% of the stage I/II lung cancers were correctly classified. Finally, in the prognosis part of the study, a 4628 Da protein was found to be significantly and independently associated with prognosis in advanced stage non-small cell lung cancer patients (p = 0.0005).ConclusionsThe potential markers that we identified through proteomic fingerprinting could accurately classify lung cancers in a high-risk population and predict survival in a non-small cell lung cancer population

Pharmacology of EAPB0203, a novel imidazo[1,2-a]quinoxaline derivative with anti-tumoral activity on melanoma

International audienceIn spite of the development of new anticancer drugs by the pharmaceutical industry, melanoma and T lymphomas are diseases for which medical advances remain limited. Thus, there was an urgent need of new therapeutics with an original mechanism of action. Since several years, our group develops quinox-alinic compounds. In this paper, the first preclinical results concerning one lead compound, EAPB0203, are presented. This compound exhibits in vitro cytotoxic activity on A375 and M4Be human melanoma cell lines superior to that of imiquimod and fotemustine. A liquid chromatography-mass spectrometry method was first validated to simultaneously quantify EAPB0203 and its metabolite, EAPB0202, in rat plasma. Thereafter, the pharmacokinetic profiles of EAPB0203 were studied in rat after intravenous and intraperitoneal administrations. After intraperitoneal administration the absolute bioavailability remains limited (22.7%). In xenografted mouse, after intraperitoneal administration of 5 and 20 mg/kg, EAPB0203 is more potent than fotemustine. The survival time was increased up to 4 and 2 weeks compared to control mice and mice treated by fotemustine, respectively. The results of this study demonstrate the relationship between the dose of EAPB0203 and its effects on tumor growth. Thus, promising efficacy, tolerance and pharmacokinetic data of EAPB0203 encourage the development towards patient benefit