PSS25 COST-EFFECTIVENESS OF ETANERCEPT AND EFALIZUMAB IN THE MANAGEMENT OF MODERATE AND SEVERE PLAQUE PSORIASIS

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Support Vector Machine based method to distinguish proteobacterial proteins from eukaryotic plant proteins

Background: Members of the phylum Proteobacteria are most prominent among bacteria causing plant diseases that result in a diminution of the quantity and quality of food produced by agriculture. To ameliorate these losses, there is a need to identify infections in early stages. Recent developments in next generation nucleic acid sequencing and mass spectrometry open the door to screening plants by the sequences of their macromolecules. Such an approach requires the ability to recognize the organismal origin of unknown DNA or peptide fragments. There are many ways to approach this problem but none have emerged as the best protocol. Here we attempt a systematic way to determine organismal origins of peptides by using a machine learning algorithm. The algorithm that we implement is a Support Vector Machine (SVM).Result: The amino acid compositions of proteobacterial proteins were found to be different from those of plant proteins. We developed an SVM model based on amino acid and dipeptide compositions to distinguish between a proteobacterial protein and a plant protein. The amino acid composition (AAC) based SVM model had an accuracy of 92.44% with 0.85 Matthews correlation coefficient (MCC) while the dipeptide composition (DC) based SVM model had a maximum accuracy of 94.67% and 0.89 MCC. We also developed SVM models based on a hybrid approach (AAC and DC), which gave a maximum accuracy 94.86% and a 0.90 MCC. The models were tested on unseen or untrained datasets to assess their validity.Conclusion: The results indicate that the SVM based on the AAC and DC hybrid approach can be used to distinguish proteobacterial from plant protein sequences.Peer reviewedBiochemistry and Molecular Biolog

A literature-based approach to evaluate the predictive capacity of a marker using time-dependent summary receiver operating characteristics

Meta-analyses are popular tools to summarize the results of publications. Prognostic performances of a marker are usually summarized by meta-analyses of survival curves or hazard ratios. These approaches may detect a difference in survival according to the marker but do not allow evaluation of its prognostic capacity. Time-dependent receiver operating characteristic curves evaluate the ability of a marker to predict time-to-event. In this article, we describe an adaptation of time-dependent summary receiver operating characteristic curves from published survival curves. To achieve this goal, we modeled the marker and the time-to-event distributions using non-linear mixed models. First, we applied this methodology to individual data in kidney transplantation presented as aggregated data, in order to validate the method. Second, we re-analyzed a published meta-analysis, which focused on the capacity of KI-67 to predict the overall survival of patients with breast cancer

Tratamiento de la artritis reumatoide con metotrexato: un estudio prospectivo abierto a largo plazo de 191 casos

One hundred and ninety-one patients with severe rheumatoid arthritis (RA) were included in a prospective open longterm study of the safety, efficacy and maintenance of methotrexate (MTX) treatment. The mean duration of MTX treatment was 19 +/- 13.2 (3-58) months; the mean weekly dose of MTX was 10.2 +/- 0.2 mg. Analysis of the 191 patients in an intent-to-treat manner showed a significant improvement of all the clinical variables and a decrease of erythrocyte sedimentation rate with a steroid sparing effect. The probability of continuing MTX therapy for up to 2 years was 65% and for up to 5 years was 46%. Adverse effects of MTX occurred in 37.1% of the patients, but only 15.7% discontinued MTX permanently

Prognostic factors of 10-year radiographic outcome in early rheumatoid arthritis. A prospective study

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