Bright conjugated polymer nanoparticles containing a biodegradable shell produced at high yields and with tuneable optical properties by a scalable microfluidic device

This study compares the performance of a microfluidic technique and a conventional bulk method to manufacture conjugated polymer nanoparticles (CPNs) embedded within a biodegradable poly(ethylene glycol) methyl ether-block-poly(lactide-co-glycolide) (PEG5K–PLGA55K) matrix. The influence of PEG5K–PLGA55K and conjugated polymers cyano-substituted poly(p-phenylene vinylene) (CN-PPV) and poly(9,9-dioctylfluorene-2,1,3-benzothiadiazole) (F8BT) on the physicochemical properties of the CPNs was also evaluated. Both techniques enabled CPN production with high end product yields (?70–95%). However, while the bulk technique (solvent displacement) under optimal conditions generated small nanoparticles (∼70–100 nm) with similar optical properties (quantum yields ∼35%), the microfluidic approach produced larger CPNs (140–260 nm) with significantly superior quantum yields (49–55%) and tailored emission spectra. CPNs containing CN-PPV showed smaller size distributions and tuneable emission spectra compared to F8BT systems prepared under the same conditions. The presence of PEG5K–PLGA55K did not affect the size or optical properties of the CPNs and provided a neutral net electric charge as is often required for biomedical applications. The microfluidics flow-based device was successfully used for the continuous preparation of CPNs over a 24 hour period. On the basis of the results presented here, it can be concluded that the microfluidic device used in this study can be used to optimize the production of bright CPNs with tailored properties with good reproducibility

Using data mining for prediction of hospital length of stay: an application of the CRISP-DM Methodology

Hospitals are nowadays collecting vast amounts of data related with patient records. All this data hold valuable knowledge that can be used to improve hospital decision making. Data mining techniques aim precisely at the extraction of useful knowledge from raw data. This work describes an implementation of a medical data mining project approach based on the CRISP-DM methodology. Recent real-world data, from 2000 to 2013, were collected from a Portuguese hospital and related with inpatient hospitalization. The goal was to predict generic hospital Length Of Stay based on indicators that are commonly available at the hospitalization process (e.g., gender, age, episode type, medical specialty). At the data preparation stage, the data were cleaned and variables were selected and transformed, leading to 14 inputs. Next, at the modeling stage, a regression approach was adopted, where six learning methods were compared: Average Prediction, Multiple Regression, Decision Tree, Artificial Neural Network ensemble, Support Vector Machine and Random Forest. The best learning model was obtained by the Random Forest method, which presents a high quality coefficient of determination value (0.81). This model was then opened by using a sensitivity analysis procedure that revealed three influential input attributes: the hospital episode type, the physical service where the patient is hospitalized and the associated medical specialty. Such extracted knowledge confirmed that the obtained predictive model is credible and with potential value for supporting decisions of hospital managers

INTCare: a knowledge discovery based intelligent decision support system for intensive care medicine

This paper introduces the INTCare system, an intelligent information system based on a completely automated Knowledge Discovery process and on the Agents paradigm. The system was designed to work in Hospital Intensive Care Units, supporting the physicians’ decisions by means of prognostic Data Mining models. In particular, these techniques were used to predict organ failure and mortality assessment. The main intention is to change the current reactive behaviour to a pro-active one, enhancing the quality of service. Current applications and experimentations, the functional and structural aspects, and technological options are presented

Ash agglomeration and deposition during combustion of poultry litter in a bubbling fluidized-bed combustor

peer-reviewedn this study, we have characterized the ash resulting from fluidized bed combustion of poultry litter as being dominated by a coarse fraction of crystalline ash composed of alkali-Ca-phosphates and a fine fraction of particulate K2SO4 and KCl. Bed agglomeration was found to be coating-induced with two distinct layers present. The inner layer (0.05–0.09 mm thick) was formed due to the reaction of gaseous potassium with the sand (SiO2) surface forming K-silicates with low melting points. Further chemical reaction on the surface of the bed material strengthened the coating forming a molten glassy phase. The outer layer was composed of loosely bound, fine particulate ash originating from the char. Thermodynamic equilibrium calculations showed slag formation in the combustion zone is highly temperature-dependent, with slag formation predicted to increase from 1.8 kg at 600 °C to 7.35 kg at 1000 °C per hour of operation (5.21 kg of ash). Of this slag phase, SiO2 and K2O were the dominant phases, accounting for almost 95%, highlighting the role of K-silicates in initiating bed agglomeration. The remaining 5% was predicted to consist mainly of Al2O3, K2SO4, and Na2O. Deposition downstream in the low-temperature regions was found to occur mostly through the vaporization–condensation mechanism, with equilibrium decreasing significantly with decreasing temperatures. The dominant alkali chloride-containing gas predicted to form in the combustion zone was KCl, which corresponds with the high KCl content in the fine baghouse ash