Thermodynamic Study Of The Micellization Of Zwitterionic Surfactants And Their Interaction With Polymers In Water By Isothermal Titration Calorimetry.

The micellization of a homologous series of zwitterionic surfactants, a group of sulfobetaines, was studied using isothermal titration calorimetry (ITC) in the temperature range from 15 to 65 °C. The increase in both temperature and the alkyl chain length leads to more negative values of ΔGmic(0) , favoring the micellization. The entropic term (ΔSmic(0)) is predominant at lower temperatures, and above ca. 55-65 °C, the enthalpic term (ΔHmic(0)) becomes prevalent, figuring a jointly driven process as the temperature increases. The interaction of these sulfobetaines with different polymers was also studied by ITC. Among the polymers studied, only two induced the formation of micellar aggregates at lower surfactant concentration: poly(acrylic acid), PAA, probably due to the formation of hydrogen bonds between the carboxylic group of the polymer and the sulfonate group of the surfactant, and poly(sodium 4-styrenesulfonate), PSS, probably due to the incorporation of the hydrophobic styrene group into the micelles. The prevalence of the hydrophobic and not the electrostatic contributions to the interaction between sulfobetaine and PSS was confirmed by an increased interaction enthalpy in the presence of electrolytes (NaCl) and by the observation of a significant temperature dependence, the latter consistent with the proposed removal of hydrophobic groups from water.

Sensitivity and specificity of machine learning classifiers for glaucoma diagnosis using Spectral Domain OCT and standard automated perimetry

PURPOSE: To evaluate the sensitivity and specificity of machine learning classifiers (MLCs) for glaucoma diagnosis using Spectral Domain OCT (SD-OCT) and standard automated perimetry (SAP). METHODS: Observational cross-sectional study. Sixty two glaucoma patients and 48 healthy individuals were included. All patients underwent a complete ophthalmologic examination, achromatic standard automated perimetry (SAP) and retinal nerve fiber layer (RNFL) imaging with SD-OCT (Cirrus HD-OCT; Carl Zeiss Meditec Inc., Dublin, California). Receiver operating characteristic (ROC) curves were obtained for all SD-OCT parameters and global indices of SAP. Subsequently, the following MLCs were tested using parameters from the SD-OCT and SAP: Bagging (BAG), Naive-Bayes (NB), Multilayer Perceptron (MLP), Radial Basis Function (RBF), Random Forest (RAN), Ensemble Selection (ENS), Classification Tree (CTREE), Ada Boost M1(ADA),Support Vector Machine Linear (SVML) and Support Vector Machine Gaussian (SVMG). Areas under the receiver operating characteristic curves (aROC) obtained for isolated SAP and OCT parameters were compared with MLCs using OCT+SAP data. RESULTS: Combining OCT and SAP data, MLCs' aROCs varied from 0.777(CTREE) to 0.946 (RAN).The best OCT+SAP aROC obtained with RAN (0.946) was significantly larger the best single OCT parameter (p<0.05), but was not significantly different from the aROC obtained with the best single SAP parameter (p=0.19). CONCLUSION: Machine learning classifiers trained on OCT and SAP data can successfully discriminate between healthy and glaucomatous eyes. The combination of OCT and SAP measurements improved the diagnostic accuracy compared with OCT data alone.