Paclitaxel, vinorelbine and 5-fluorouracil in breast cancer patients pretreated with adjuvant anthracyclines

We investigated the activity and toxicity of a combination of vinorelbine (VNB), paclitaxel (PTX) and 5-fluorouracil (5-FU) continuous infusion administered as first-line chemotherapy in metastatic breast cancer patients pretreated with adjuvant anthracyclines. A total of 61 patients received a regimen consisting of VNB 25 mg m−2 on days 1 and 15, PTX 60 mg m−2 on days 1, 8 and 15 and continuous infusion of 5-FU at 200 mg m−2 every day. Cycles were repeated every 28 days. Disease response was evaluated by both RECIST and World Health Organization (WHO) criteria. Objective responses were recorded in 39 of 61 patients (64.0%) assessed by WHO and in 36 of 50 patients (72.0%) assessable by RECIST criteria. Complete remission occurred in 15 (24.6%) and 14 patients (28.0%), respectively. The median time to progression and overall survival of entire population was 10.6 and 27.3 months, respectively, and median duration of complete response was 14.8 months. The dose-limiting toxicity was myelosuppression (leucopenia grade 3/4 in 52.5% of patients). Grade 3/4 nonhaematologic toxicities included mucositis/diarrhoea in 13.1%, skin in 3.3% and cardiac in 1.6% of patients. Grade 2/3 neurotoxicity was observed in five patients (7.2%). The VNB, PTX and 5-FU continuous infusion combination regimen was active and manageable. Complete responses were frequent and durable

Immunofluorometric quantitation and histochemical localisation of kallikrein 6 protein in ovarian cancer tissue: a new independent unfavourable prognostic biomarker

Human kallikrein 6 protein is a newly discovered human kallikrein. We determined the amount of human kallikrein 6 in extracts of 182 ovarian tumours and correlated specific activity (ng hK6 mg−1 total protein) with clinicopathological variables documented at the time of surgical excision and with outcome (progression free survival, overall survival) monitored over a median interval of 62 months. Thirty per cent of the tumours were positive for human kallikrein 6 (>35 ng hK6 mg−1 total protein). Human kallikrein 6-specific immunohistochemical staining of four ovarian tissues that included benign, borderline and malignant lesions indicated a cytoplasmic location of human kallikrein 6 in tumour cells of epithelial origin, although the intensity of staining was variable. Tumour human kallikrein 6 (ng hK6 mg−1 total protein) was higher in late stage disease, serous histotype, residual tumour >1 cm and suboptimal debulking (>1 cm) (P<0.05). Univariate analysis revealed that patients with tumour human kallikrein 6 positive specific activity were more likely to suffer progressive disease and to die (hazard ratio 1.71 (P=0.015) and 1.88 (P=0.022), respectively). Survival curves demonstrated the same (P=0.013 and 0.019, respectively). Multivariate analysis revealed that human kallikrein 6 positivity was retained as an independent prognostic variable in several subgroups of patients, namely those with (low) grade I and II tumours (hazard ratio progression free survival 4.3 (P=0.027) and overall survival 4.1 (P=0.023)) and those with optimal debulking (hazard ratio progression free survival 3.8 (P=0.019) and overall survival 5.6 (P=0.011)). We conclude that tumour kallikrein 6 protein levels have utility as an independent adverse prognostic marker in a subgroup of ovarian cancer patients with otherwise apparently good prognosis

Development of a Software Reliability Prediction Method for Onboard European Train Control System

Software prediction is a complex area as there are no accurate models to represent reliability throughout the use of software, unlike hardware reliability. In the context of the software reliability of on-board train systems, ensuring good software reliability over time is all the more critical given the current density of rail traffic and the risk of accidents resulting from a software malfunction. This thesis proposes to use soft computing methods and historical failure data to predict the software reliability of on-board train systems. For this purpose, four machine learning models (Multi-Layer Perceptron, Imperialist Competitive Algorithm Multi-Layer Perceptron, Long Short-Term Memory Network and Convolutional Neural Network) are compared to determine which has the best prediction performance. We also study the impact of having one or more features represented in the dataset used to train the models. The performance of the different models is evaluated using the Mean Absolute Error, Mean Squared Error, Root Mean Squared Error and the R Squared. The report shows that the Long Short-Term Memory Network is the best performing model on the data used for this project. It also shows that datasets with a single feature achieve better prediction. However, the small amount of data available to conduct the experiments in this project may have impacted the results obtained, which makes further investigations necessary. Att förutsäga programvara är ett komplext område eftersom det inte finns några exakta modeller för att representera tillförlitligheten under hela programvaruanvändningen, till skillnad från hårdvarutillförlitlighet. När det gäller programvarans tillförlitlighet i fordonsbaserade tågsystem är det ännu viktigare att säkerställa en god tillförlitlighet över tiden med tanke på den nuvarande tätheten i järnvägstrafiken och risken för olyckor till följd av ett programvarufel. I den här avhandlingen föreslås att man använder mjuka beräkningsmetoder och historiska data om fel för att förutsäga programvarans tillförlitlighet i fordonsbaserade tågsystem. För detta ändamål jämförs fyra modeller för maskininlärning (Multi-Layer Perceptron, Imperialist Competitive Algorithm Mult-iLayer Perceptron, Long Short-Term Memory Network och Convolutional Neural Network) för att fastställa vilken som har den bästa förutsägelseprestandan. Vi undersöker också effekten av att ha en eller flera funktioner representerade i den datamängd som används för att träna modellerna. De olika modellernas prestanda utvärderas med hjälp av medelabsolut fel, medelkvadratfel, rotmedelkvadratfel och R-kvadrat. Rapporten visar att Long Short-Term Memory Network är den modell som ger bäst resultat på de data som använts för detta projekt. Den visar också att dataset med en enda funktion ger bättre förutsägelser. Den lilla mängd data som fanns tillgänglig för att genomföra experimenten i detta projekt kan dock ha påverkat de erhållna resultaten, vilket gör att ytterligare undersökningar är nödvändiga

Histoire étrangère

Joüon des Longrais Frédéric. Histoire étrangère. In: École pratique des hautes études, Section des sciences historiques et philologiques. Annuaire 1935-1936. 1935. pp. 41-44

Histoire étrangère

Joüon des Longrais Frédéric. Histoire étrangère. In: École pratique des hautes études. 4e section, Sciences historiques et philologiques. Annuaire 1961-1962. 1961. pp. 59-61

Histoire étrangère

Joüon des Longrais Frédéric. Histoire étrangère. In: École pratique des hautes études, Section des sciences historiques et philologiques. Annuaire 1952-1953. 1953. pp. 40-41

Histoire étrangère

Joüon des Longrais Frédéric. Histoire étrangère. In: École pratique des hautes études, Section des sciences historiques et philologiques. Annuaire 1955-1956. 1956. pp. 37-38