Using multivariate sequential patterns to improve survival prediction in intensive care burn unit

Resuscitation and stabilization are key issues in Intensive Care Burn Units and early survival predictions help to decide the best clinical action during these phases. Current survival scores of burns focus on clinical variables such as age or the body surface area. However, the evolution of other parameters (e.g. diuresis or fluid balance) during the first days is also valuable knowledge. In this work we suggest a methodology and we propose a Temporal Data Mining algorithm to estimate the survival condition from the patient’s evolution. Experiments conducted on 480 patients show the improvement of survival prediction.

Fabrication of heterogeneous biocatalyst tethering artificial prosthetic groups to obtain omega-3-fatty acids by selective hydrolysis of fish oils

The active site of lipase from Bacillus thermocathenolatus was selectively modified with allyl and naphthyl chains at different positions. Lipase immobilization and selective tethering of a naphthyl side chain to its position 320 improve both the hydrolysis rate of fish oils and the selectivity towards the eicosapentaenoic acid acyl chains. © The Royal Society of Chemistry 2016.